Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
e-ISSN: 2685-0893
Pengembangan Model Random Forest Regressor untuk Prediksi Kelembaban pada Pertanian Perkotaan Berkelanjutan
Agriculture is a sector that supports food security. Currently, agriculture faces serious challenges due to climate change, land limitations, and low technology adoption. This study aims to develop an Internet of Things (IoT)-based smart farming system integrated with artificial intelligence and run through edge computing. The prototype system is designed to collect real-time data on crop growth environments using pH, TDS, temperature, humidity, and water level sensors. The data is then processed locally using the Random Forest Regressor algorithm to determine optimal environmental conditions. Test results show that the model has very high accuracy in predicting humidity (R² = 0.99; RMSE = 0.65) and temperature (R² = 0.99; RMSE = 0.17), although there are still discrepancies in extreme conditions. The integration of IoT, AI, and edge computing has proven to improve energy efficiency, accelerate response times, and provide adaptive and affordable solutions in support of sustainable urban agriculture productivity. Keywords: Artificial Intelligence; Random Forest Regressor; IoT; Edge Computing Abstrak Pertanian merupakan sektor yang mendukung ketahanan pangan, saat ini pertanian menghadapi tantangan serius akibat perubahan iklim, keterbatasan lahan, dan rendahnya adopsi teknologi. Penelitian ini bertujuan mengembangkan sistem pertanian cerdas berbasis Internet of Things (IoT) yang terintegrasi dengan kecerdasan buatan dan dijalankan melalui komputasi tepi. Prototipe sistem dirancang untuk mengumpulkan data lingkungan pertumbuhan tanaman secara real-time menggunakan sensor pH, TDS, suhu, kelembaban, dan tinggi permukaan air. Data kemudian diproses secara lokal menggunakan algoritma Random Forest Regressor untuk menentukan kondisi lingkungan optimal. Hasil pengujian menunjukkan model memiliki akurasi sangat tinggi pada prediksi kelembaban (R² = 0,99; RMSE = 0,65) dan suhu (R² = 0,99; RMSE = 0,17), meskipun masih terdapat selisih pada kondisi ekstrem. Integrasi IoT, AI, dan edge computing terbukti mampu meningkatkan efisiensi energi, mempercepat respons, serta memberikan solusi adaptif dan terjangkau dalam mendukung produktivitas pertanian perkotaan berkelanjutan.
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Prediksi Pergerakan Harga Saham Bank Mandiri Menggunakan Metode Support Vector Regression dan Algoritma Grid Search
The volatile nature of stock price movements poses a major challenge for investors in making accurate investment decisions. This study aims to predict the stock price movement of PT. Bank Mandiri (Persero) Tbk [BMRI] using Support Vector Regression (SVR) optimized through the Grid Search algorithm. The dataset consists of daily stock prices from August 2020 to August 2025, including open, high, low, close, adjusted close, and trading volume. The research process involves data collection, preprocessing (cleaning, feature selection, normalization), splitting into training and testing sets, parameter optimization using Grid Search with Leave-One-Out Cross Validation (LOOCV), model training, and evaluation with R², MSE, and RMSE. The results show that the SVR model with a linear kernel, C = 1 and epsilon = 0.01, achieved the best performance, with high accuracy (R² = 0.9991 on training data and R² = 0.9976 on testing data). These findings confirm the effectiveness of Grid Search–based SVR in predicting stock prices and supporting investment decision-making. Keywords: Stock Price Prediction; Support Vector Regression; Grid Search; Bank Mandiri Abstrak Pergerakan harga saham yang fluktuatif menjadi tantangan utama bagi investor dalam menentukan strategi investasi yang tepat. Penelitian ini bertujuan memprediksi pergerakan harga saham PT. Bank Mandiri (Persero) Tbk [BMRI] dengan metode Support Vector Regression (SVR) yang dioptimalkan menggunakan algoritma Grid Search. Data yang digunakan berupa harga saham harian periode Agustus 2020–Agustus 2025, mencakup variabel open, high, low, close, adjusted close, dan volume. Tahapan penelitian meliputi pengumpulan data, pra-pemrosesan (pembersihan, seleksi fitur, normalisasi), pembagian data latih dan uji, optimasi parameter dengan Grid Search berbasis Leave-One-Out Cross Validation (LOOCV), pelatihan model, serta evaluasi dengan R², MSE, dan RMSE. Hasil penelitian menunjukkan SVR dengan kernel linear, parameter C = 1 dan epsilon = 0,01 memberikan performa terbaik dengan akurasi tinggi (R² = 0,9991 pada data latih dan R² = 0,9976 pada data uji). Temuan ini menegaskan efektivitas SVR berbasis Grid Search dalam memprediksi harga saham dan mendukung pengambilan keputusan investasi.
Analisis Kinerja Algoritma SVM dan Naïve Bayes untuk Klasifikasi Sentimen Program Makan Gratis
The development of social media facilitates the massive dissemination of public opinion, including towards the government’s free meal program. The high volume of comments makes manual analysis inefficient, thus requiring technological approaches such as sentiment analysis for automatic opinion classification. This study focuses on comparing the performance of the SVM (Support Vector Machine) and Naïve Bayes algorithms in classifying public sentiment collected from X social media data. The research method includes data crawling, preprocessing with Natural Language Processing (NLP), and classification using both algorithms. Testing results on an imbalanced dataset show that SVM is the best model with 96% accuracy and an F1-score of 0.48 for the minority (negative) class, significantly outperforming Naïve Bayes variants which tend to predict the majority class. The findings demonstrate that the SVM algorithm has higher reliability in sentiment classification on datasets with imbalanced class distribution. Keywords: Sentiment Analysis; Support Vector Machines; Naive Bayes; Free Lunch Program; Text Classification Abstrak Perkembangan media sosial memfasilitasi penyampaian opini publik secara masif, termasuk terhadap program makan gratis pemerintah. Volume komentar yang tinggi menjadikan analisis manual tidak efisien, sehingga memerlukan pendekatan teknologi seperti analisis sentimen untuk klasifikasi opini otomatis. Penelitian ini berfokus pada perbandingan performa algoritma SVM (Support Vector Machine) dan Naïve Bayes dalam mengklasifikasikan sentimen masyarakat yang diambil dari data media sosial X. Metode penelitian mencakup pengumpulan data crawling, pra-pemrosesan dengan Natural Language Processing (NLP), hingga klasifikasi menggunakan kedua algoritma. Hasil pengujian pada dataset yang tidak seimbang menunjukkan bahwa SVM merupakan model terbaik dengan akurasi 96% dan nilai F1-score 0.48 pada kelas minoritas (negatif), secara signifikan mengungguli varian Algoritma Naïve Bayes sering kali lebih condong memprediksi pada kelas mayoritas. Hasil penelitian ini membuktikan bahwa algoritma SVM memiliki keandalan lebih tinggi dalam klasifikasi sentimen pada dataset dengan distribusi kelas yang tidak seimbang.
Model Pembeda Sampah Organik dan Anorganik dengan Machine Learning berbasis Phyton
This research developed an artificial intelligence-based system to differentiate between organic and inorganic waste using a Convolutional Neural Network (CNN) model. The problem addressed is the community's limited understanding of how to properly separate types of waste, which contributes to environmental pollution. The methodology employed includes data collection of waste images, training the CNN model, and system testing. The test results showed that the model achieved an accuracy of 75%, with a precision of 78% and a recall of 70%. These findings suggest that the developed system can help the community better understand and separate waste more effectively. This research makes a positive contribution to waste management and is expected to serve as a foundation for further development in waste classification technology. Keywords: Artificial intelligence; Convolutional Neural Network; Waste separation; Environmental management; Machine learning Abstrak Penelitian ini mengembangkan sistem berbasis kecerdasan buatan untuk membedakan sampah organik dan anorganik menggunakan model Convolutional Neural Network (CNN). Permasalahan yang dihadapi adalah rendahnya pemahaman masyarakat dalam memisahkan jenis sampah, yang berdampak pada pencemaran lingkungan. Metodologi yang digunakan meliputi pengumpulan data gambar sampah, pelatihan model CNN, dan pengujian sistem. Hasil pengujian menunjukkan bahwa model mencapai akurasi 75%, dengan presisi 78% dan recall 70%. Temuan ini menunjukkan bahwa sistem yang dikembangkan dapat membantu masyarakat dalam memahami dan memisahkan sampah dengan lebih baik. Penelitian ini memberikan kontribusi positif terhadap pengelolaan sampah dan diharapkan dapat menjadi dasar untuk pengembangan lebih lanjut dalam teknologi klasifikasi sampah.
Pengembangan Model Deep Learning dengan Slang-Aware Embeddings untuk Deteksi Promosi Judi Online
Online gambling promotions on social media platforms such as YouTube often employ slang or non-standard language to evade traditional moderation systems, increasing the spread of illegal content in Indonesia. This study aims to develop a hybrid deep learning model combining BERT and LSTM to accurately detect online gambling promotions. Data were collected from YouTube comments through a web scraping process, followed by text cleaning, labeling, and normalization using a semi-automatic slang dictionary. The model was trained with slang-aware embeddings to capture informal language context. Evaluation was conducted using precision, recall, F1-score, and confusion matrix metrics. The results show an accuracy of 96% with an F1-score of 0.96, indicating a strong balance between precision and recall. These findings demonstrate the effectiveness of the proposed hybrid approach in automatically detecting online gambling promotional content. Kata kunci: Online Gambling Detection; Deep Learning; NLP; Slang-Aware Embeddings; BERT-LTSM Abstrak Promosi judi daring di media sosial seperti YouTube sering menggunakan bahasa tidak baku atau slang untuk menghindari deteksi sistem moderasi. Kondisi ini berpotensi meningkatkan penyebaran konten ilegal di Indonesia. Penelitian ini bertujuan mengembangkan model deep learning hibrida yang mengombinasikan BERT dan LSTM guna mendeteksi promosi judi daring secara lebih akurat. Data dikumpulkan dari komentar YouTube melalui proses web scraping, kemudian diproses melalui tahap pembersihan teks, pelabelan, dan normalisasi menggunakan kamus slang semi-otomatis. Model dilatih dengan slang-aware embeddings untuk menangkap konteks bahasa tidak resmi. Pengujian dilakukan menggunakan metrik precision, recall, F1-score, dan confusion matrix. Hasil menunjukkan akurasi sebesar 96% dengan nilai F1-score 0,96, menandakan keseimbangan tinggi antara presisi dan sensitivitas model. Temuan ini membuktikan efektivitas pendekatan hibrida dalam mendeteksi konten promosi judi daring secara otomatis.
Model Penerimaan Sistem E-purchasing: Analisis Peran Kepercayaan dan Keamanan Pengguna
E-purchasing System Acceptance Model: Analysis of the Role of User Trust and Security. The adoption of local Business-to-Government (B2G) E-purchasing platforms faces challenges, as indicated by a decline in new user registrations. This study aims to analyze the influence of User Trust and Perceived Security on the Behavioral Intention to Use the Parto.id platform, extending the Technology Acceptance Model (TAM). This quantitative study surveyed 100 seller partners in Jambi City. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that Perceived Usefulness and User Trust significantly and positively influence Behavioral Intention to Use. However, Perceived Security was found to have no significant direct effect on user intention. The results highlight that User Trust is a more crucial determinant than Perceived Security in the adoption of B2G E-purchasing systems, suggesting that security is considered a standard prerequisite. Platform providers should prioritize trust-building initiatives to enhance user adoption. Keywords: Technology Acceptance Model; User Trust; Perceived Security; E-purchasing; B2G System. Abstrak Model Penerimaan Sistem E-purchasing: Analisis Peran Kepercayaan dan Keamanan Pengguna. Adopsi platform E-purchasing lokal Business-to-Government (B2G) menghadapi tantangan yang ditunjukkan dengan adanya penurunan jumlah pendaftar pengguna baru. Penelitian ini bertujuan untuk menganalisis pengaruh kepercayaan pengguna dan persepsi keamanan terhadap niat perilaku untuk menggunakan platform Parto.id dengan memperluas Technology Acceptance Model (TAM). Penelitian kuantitatif ini menggunakan survei terhadap 100 mitra penjual di Kota Jambi. Data dianalisis menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM). Hasil penelitian menunjukkan bahwa persepsi kemanfaatan dan kepercayaan pengguna berpengaruh positif dan signifikan terhadap niat perilaku untuk menggunakan. Namun, persepsi keamanan ditemukan tidak memiliki pengaruh langsung yang signifikan terhadap niat pengguna. Hasil ini menyoroti bahwa kepercayaan pengguna merupakan faktor penentu yang lebih krusial dibandingkan persepsi keamanan dalam adopsi sistem E-purchasing B2G, yang mengindikasikan bahwa keamanan dianggap sebagai prasyarat standar. Penyedia platform harus memprioritaskan inisiatif pembangunan kepercayaan untuk meningkatkan adopsi pengguna.
Penerapan Metode SAW Pada Sistem Pendukung Keputusan Penentuan Perangkat Lunak Anti Virus
The increasing dependence of society on computers and the internet highlights the urgency of protecting data and privacy from cyber threats. Antivirus software is one of the common solutions; however, the abundance of options, each with its own advantages and disadvantages, often makes it difficult for users to select the most suitable one. This study aims to assist users in choosing the best free antivirus through a systematic approach using a decision support system. The method applied is Simple Additive Weighting (SAW) with five evaluation criteria: scanning speed, detection capability, rating, resource optimization, and available features. Five antivirus software were tested as alternatives, namely Windows Security, Avira, Avast, Bitdefender, and Kaspersky. The testing and calculation results indicate that Windows Security ranked the highest with a vector value of 0.257, due to its high detection rate, fast scanning performance, good rating, and sufficient features. Therefore, the SAW method proves effective in providing objective and systematic recommendations for selecting the best antivirus software. Keywords: Decision support system; Simple additive weighting; Antivirus Abstrak Meningkatnya ketergantungan masyarakat terhadap sistem komputer dan internet menimbulkan urgensi akan perlindungan data dan privasi dari ancaman siber. Salah satu solusi yang umum digunakan adalah perangkat lunak antivirus, namun banyaknya pilihan dengan kelebihan dan kekurangan masing-masing seringkali menimbulkan kebingungan dalam menentukan pilihan yang tepat. Penelitian ini bertujuan untuk membantu pengguna dalam memilih antivirus gratis terbaik dengan pendekatan sistematis melalui sistem pendukung keputusan. Metode yang digunakan adalah Simple Additive Weighting (SAW) dengan lima kriteria penilaian, yaitu kecepatan pemindaian, kemampuan deteksi, rating, optimasi sumber daya, dan jumlah fitur. Lima perangkat lunak antivirus diuji sebagai alternatif, yakni Windows Security, Avira, Avast, Bitdefender, dan Kaspersky. Hasil pengujian dan perhitungan menunjukkan bahwa Windows Security memperoleh peringkat tertinggi dengan nilai vektor 0,257 karena memiliki kecepatan dan tingkat deteksi yang tinggi, rating yang baik, serta fitur yang cukup banyak. Dengan demikian, metode SAW terbukti efektif dalam memberikan rekomendasi obyektif dan sistematis untuk pemilihan perangkat lunak antivirus terbaik.
Optimalisasi Proses Penjualan dan Pengecekan Stok Pada Toko Mozza Aquatic Menggunakan BPMN
An efficient sales process and stock checking are crucial components in maintaining smooth operations and customer satisfaction, especially for MSMEs such as Toko Mozza Aquatic. This research aims to redesign these two main processes using the Business Process Model and Notation (BPMN) approach. This study involves comprehensive business flow analysis, throughput efficiency evaluation, and identification of information technology-based solutions. The research results show that the new system design, which digitally integrates sales and inventory interfaces, is able to improve process structure, reduce service time, and minimize manual errors. Features such as product search, transaction tracking, stock availability indicators also support more responsive and accurate decision making. These findings confirm that the implementation of BPMN accompanied by digitalization and HR training is an effective strategy in increasing the efficiency and competitiveness of MSMEs amidst competitive market dynamics. Keywords: Business Process Modeling Notation; Sales Process; Stock Checking; Information Systems; Operational Efficiency. Abstrak Proses penjualan dan pengecekan stok yang efisien merupakan komponen krusial dalam menjaga kelancaran operasional dan kepuasan pelanggan, terutama bagi pelaku UMKM seperti Toko Mozza Aquatic. Penelitian ini bertujuan untuk merancang ulang dua proses utama tersebut menggunakan pendekatan Business Process Model and Notation (BPMN). Studi ini melibatkan analisis alur bisnis secara menyeluruh, evaluasi efisiensi throughput, serta identifikasi solusi berbasis teknologi informasi. Hasil penelitian menunjukkan bahwa rancangan sistem baru, yang mengintegrasikan antarmuka penjualan dan inventaris secara digital, mampu meningkatkan struktur proses, mengurangi waktu layanan, dan meminimalkan kesalahan manual. Fitur-fitur seperti pencarian produk, pelacakan transaksi, indikator ketersediaan stok turut mendukung pengambilan keputusan yang lebih responsif dan akurat. Temuan ini menegaskan bahwa penerapan BPMN yang diiringi digitalisasi dan pelatihan SDM merupakan strategi efektif dalam meningkatkan efisiensi dan daya saing UMKM di tengah dinamika pasar yang kompetitif.
Pengembangan Sistem Informasi Monitoring Kinerja Guru dan Siswa Menggunakan Metode Agile
This study aims to develop a web-based monitoring information system for teachers and students using the Agile methodology. The system integrates the Google Classroom API to monitor teacher performance and student learning progress in real time. Unlike previous studies that focused only on academic data management, this research applies Agile stages—product backlog, sprint planning, execution, and review—in an iterative process involving teachers and students. The system was tested at Tunas Gading School, Jakarta, featuring key functions such as a teacher performance dashboard, student progress tracking, and automated task notifications. Results from User Acceptance Testing (UAT) show that 90% of teachers found the system helpful in monitoring student progress, while 85% of students benefited from task notifications. The system effectively improves learning monitoring efficiency and provides an innovative approach to automated academic monitoring integrated with online learning platforms. Keywords: Information System; Agile; Google Classroom; Academic Monitoring; Online Learning Abstrak Penelitian ini bertujuan mengembangkan sistem informasi monitoring kinerja guru dan siswa berbasis web menggunakan metode Agile. Sistem ini mengintegrasikan Google Classroom API untuk memantau kinerja guru dan perkembangan belajar siswa secara real-time. Berbeda dari penelitian sebelumnya yang hanya mengelola data akademik, pendekatan Agile diterapkan melalui tahapan product backlog, sprint planning, execution, dan review secara iteratif melibatkan guru dan siswa. Uji coba di Sekolah Tunas Gading Jakarta menampilkan fitur utama seperti dashboard kinerja guru, pemantauan progres siswa, dan notifikasi tugas otomatis. Hasil User Acceptance Testing (UAT) menunjukkan 90% guru menyatakan sistem membantu pemantauan siswa dan 85% siswa merasa terbantu dengan notifikasi tugas. Sistem ini meningkatkan efisiensi pemantauan pembelajaran serta menghadirkan inovasi dalam otomatisasi monitoring akademik terintegrasi dengan platform pembelajaran daring.
Penerapan Design Thinking dan SUS pada Pengembangan Website SIA SMA Sedes Sapientiae
User experience in web-based academic information systems is strongly influenced by the efficiency and intuitiveness of the user interface design. This study focuses on developing the user interface of the Academic Information System at SMA Sedes Sapientiae Jambu, which previously had not been fully optimized in presenting students’ academic progress reports. By applying the Design Thinking method, feature requirements were identified and designed according to users’ needs and preferences. To evaluate usability, the System Usability Scale (SUS) was employed after the system was developed. The evaluation results indicated a score in category “B” and a “good” rating on the adjective scale. These findings demonstrate that the Design Thinking approach contributes positively to enhancing usability and improving user comfort in accessing the academic information system. Keywords: User Interface; Design Thinking; System Usability Scale; Academic Information System, User Experience Abstrak Pengalaman pengguna dalam sistem informasi akademik berbasis website dipengaruhi oleh desain antarmuka pengguna yang efisien dan intuitif. Penelitian ini menitikberatkan pada pengembangan tampilan pengguna Sistem Informasi Akademik (SIA) di SMA Sedes Sapientiae Jambu yang sebelumnya belum maksimal dalam menyajikan laporan perkembangan akademik siswa. Melalui penerapan metode Design Thinking, kebutuhan fitur diidentifikasi sekaligus dirancang sesuai dengan keinginan dan kebutuhan. Untuk menilai tingkat kegunaan, digunakan metode System Usability Scale (SUS) setelah sistem selesai dikembangkan. Hasil evaluasi menunjukkan nilai dengan kategori “B” serta penilaian “good” pada skala adjektiva. Temuan ini membuktikan bahwa pendekatan design thinking mampu memberikan kontribusi positif terhadap peningkatan kegunaan dan kenyamanan pengguna dalam mengakses SIA di SMA Sedes Sapientiae Jambu.
Analisis Peran Media Sosial dalam Membangun Kepercayaan Publik Layanan E-Government Pendidikan
The development of E-Government in the education sector requires public services that are credible and capable of strengthening public trust. However, in the implementation of online student admission in South Sumatra, issues remain regarding transparency, responsiveness, and clarity of information. This study aims to examine the factors influencing public trust in the Provincial Education Office’s admission service through its social media platforms. A quantitative approach was applied using a survey analyzed with PLS-SEM, involving six variables: transparency, accountability, information quality, system quality and responsiveness, security and privacy, and managerial competence. The results indicate that only accountability and information quality have a positive and significant impact on public trust. These findings highlight that institutional responsibility and reliable information are essential in fostering trust in digital education services. Keywords: Public trust; Government social media; Accountability; Information quality; Education services Abstrak Perkembangan pelayanan pendidikan berbasis E-Government menuntut pemerintah daerah menyediakan layanan yang kredibel dan mampu membangun kepercayaan publik. Namun, dalam pelaksanaan PPDB di Provinsi Sumatera Selatan masih muncul keluhan terkait transparansi, responsivitas, dan kejelasan informasi. Penelitian ini bertujuan menganalisis faktor-faktor yang memengaruhi kepercayaan publik terhadap layanan PPDB melalui media sosial Dinas Pendidikan sebagai kanal komunikasi digital pemerintah. Metode yang digunakan adalah pendekatan kuantitatif melalui survei, dengan analisis menggunakan PLS-SEM pada enam variabel: transparansi, akuntabilitas, kualitas informasi, kualitas sistem dan responsivitas, keamanan dan privasi, serta kompetensi pengelola. Hasil penelitian menunjukkan bahwa hanya akuntabilitas dan kualitas informasi yang berpengaruh positif dan signifikan terhadap kepercayaan publik. Temuan ini menegaskan pentingnya tanggung jawab lembaga dan kualitas konten informasi dalam membangun kepercayaan pada layanan pendidikan digital.
Implementasi YOLOv10 untuk Pengenalan Alfabet SIBI berbasis Deteksi Gerakan Tangan
Sign language is a primary communication tool for people with hearing and speech impairments. One key component of the Indonesian Sign System (SIBI) is the SIBI alphabet, used for words without specific signs. However, limited public understanding creates communication gaps. This study develops a web-based SIBI alphabet recognition system using the YOLOv10 model. The dataset initially contained 6,110 images and was augmented to 15,859 images. Eight hyperparameter settings were tested, with the best result at a learning rate of 0.001, batch size 32, and Adam optimizer, achieving mAP@50 of 0.988 and mAP@50–95 of 0.971. The model was then converted to ONNX for faster inference, yielding mAP@50 of 0.953 and 797.7 ms per image. The ONNX model was integrated into a web app capable of real-time SIBI recognition. Results show this approach is effective, inclusive, and accessible as an assistive communication tool. Keywords: Deep learning; SIBI Alphabet; YOLOv10 Abstrak Bahasa isyarat merupakan sarana utama komunikasi bagi penyandang tunarungu dan tunawicara. Salah satu komponennya, alfabet Sistem Isyarat Bahasa Indonesia (SIBI), digunakan untuk menyampaikan kata yang tidak memiliki padanan isyarat khusus. Namun, kurangnya pemahaman masyarakat terhadap alfabet SIBI menimbulkan kesenjangan komunikasi. Penelitian ini bertujuan mengembangkan sistem pengenalan alfabet SIBI berbasis web menggunakan model YOLOv10. Dataset awal berjumlah 6.110 gambar dan diaugmentasi menjadi 15.859 gambar. Model dilatih menggunakan delapan konfigurasi hyperparameter yang menghasilkan performa terbaik pada learning rate 0,001, batch size 32, dan optimizer Adam dengan mAP@50 sebesar 0,988 dan mAP@50–95 sebesar 0,971. Untuk meningkatkan efisiensi, model dikonversi ke format ONNX dan diuji ulang menghasilkan mAP@50 sebesar 0,953 serta waktu inferensi 797,7 ms per gambar. Model ONNX kemudian diintegrasikan ke aplikasi web yang mampu mengenali alfabet SIBI secara real-time. Hasil menunjukkan pendekatan ini efektif sebagai alat bantu komunikasi yang inklusif dan mudah diakses.
Aplikasi Pendaftaran Online Poliklinik Di Rumah Sakit Karomah Holistic Berbasis Android
Karomah Holistic Hospital is a healthcare facility committed to providing high-quality and efficient medical services to the community. To enhance patient convenience in accessing healthcare services, an Android-based outpatient registration application has been developed. This application enables patients to register online, reduce physical queues, and provide more structured information regarding doctor schedules, room availability, and laboratory and radiology test results. The development of this application utilized the Waterfall method, which includes the stages of requirement analysis, design, implementation, testing, and maintenance. The application was developed using the Kotlin programming language with Jetpack Compose as the user interface framework and supported by WorkManager to manage the delivery of appointment reminder notifications. User Acceptance Testing of the application demonstrated that it is easy for patients to use and helps them access healthcare services more quickly and conveniently. With this application, it is hoped that patients will experience more efficient and comfortable service at Karomah Holistic Hospital. Keywords: Clinic registration; Android; Jetpack compose; Workmanager; Waterfall Abstrak Rumah Sakit Karomah Holistic adalah fasilitas layanan Kesehatan yang berkomitmen untuk memberikan pelayanan medis yang berkualitas dan efisien kepada masyarakat. Untuk meningkatkan kemudahan pasien dalam mengakses layanan kesehatan, dikembangkan aplikasi pendaftaran poliklinik berbasis Android. Aplikasi ini memungkinkan pasien untuk melakukan pendaftaran secara online, mengurangi antrean fisik, serta memberikan informasi yang lebih terstruktur terkait jadwal dokter, ketersediaan kamar, dan hasil pemeriksaan laboratorium maupun radiologi. Pengembangan aplikasi ini menggunakan metode Waterfall, yang mencakup tahap analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Aplikasi dikembangkan dengan bahasa pemrograman Kotlin menggunakan Jetpack Compose sebagai framework antarmuka pengguna, serta didukung oleh WorkManager untuk mengatur pengiriman notifikasi pengingat jadwal pemeriksaan. Pengujian aplikasi menggunakan metode User Acceptance Test menunjukkan bahwa aplikasi ini mudah digunakan oleh pasien dan membantu mereka dalam mengakses layanan kesehatan secara lebih cepat dan praktis. Dengan adanya aplikasi ini, diharapkan pasien dapat merasakan pengalaman layanan yang lebih efisien dan nyaman di Rumah Sakit Karomah Holistic.
Perbandingan Weighted Product Dengan AHP+WP Dalam Penentuan Pegawai Berprestasi Di Universitas XYZ
The selection of outstanding employees is an important aspect of human resource management in higher education, requiring objective and accurate evaluation. This study compares two multi-criteria decision-making methods: Weighted Product (WP) and a combination of Analytic Hierarchy Process with WP (AHP+WP) in ranking high-performing staff at XYZ University. The evaluation uses Mean Absolute Percentage Error (MAPE) to measure the closeness of the method’s results to expert-based manual assessments. WP produced a MAPE of 16.86%, while AHP+WP yielded only 9.17%, indicating higher accuracy. The Wilcoxon signed-rank test resulted in a p-value of 0.0002 (< 0.05), indicating a significant difference. AHP+WP is superior due to its systematic and consistent weighting mechanism, compared to the more subjective WP. These findings align with previous studies recommending hybrid methods. AHP+WP is considered more appropriate for official implementation in employee performance assessment systems to support fair, transparent, and data-driven evaluation. Kata kunci: AHP; Weighted Product; outstanding employee; MAPE; Wilcoxon. Abstrak Pemilihan pegawai berprestasi merupakan bagian penting dalam manajemen sumber daya manusia di perguruan tinggi, yang memerlukan evaluasi objektif dan akurat. Penelitian ini membandingkan dua metode pengambilan keputusan multikriteria: Weighted Product (WP) dan kombinasi Analytic Hierarchy Process dengan WP (AHP+WP) dalam menentukan peringkat pegawai berprestasi di Universitas XYZ. Evaluasi menggunakan Mean Absolute Percentage Error (MAPE) untuk mengukur kedekatan hasil metode dengan penilaian manual oleh pakar. WP menghasilkan MAPE sebesar 16,86%, sementara AHP+WP hanya 9,17%, menunjukkan akurasi lebih tinggi. Uji Wilcoxon signed-rank memberikan nilai p = 0,0002 (< 0,05), mengindikasikan perbedaan signifikan. AHP+WP unggul karena mekanisme pembobotannya yang sistematis dan konsisten, dibandingkan WP yang lebih subjektif. Temuan ini sejalan dengan studi sebelumnya yang merekomendasikan metode hybrid. AHP+WP dinilai lebih layak untuk diimplementasikan secara resmi dalam sistem penilaian pegawai berprestasi, guna mendukung evaluasi kinerja yang adil, transparan, dan berbasis data.
Model Aplikasi Sistem E-Commerce untuk Rekomendasi dan Pemantauan Harga Produk Penjual
Sellers on e-commerce platforms face difficulties in determining strategic pricing due to dynamic competition. To address this problem, an integrated system application model was developed as a solution to assist sellers. This research utilized the Research and Development (R&D) methodology with a waterfall model, which included the stages of needs analysis, system design, implementation, performance verification and data analysis, and final evaluation and revision. The result is a functional application capable of providing features such as search, strategic price recommendations at three levels (lowest, average/middle, highest), viewing more detailed information about products sold in graphical form, and other features. Usability testing conducted on target users yielded average scores of 95.54% for free-type customers, 91.26% for premium-type customers, and 96.8% for buyer-type cutomers. This indicates excellent ease of use and a high level of acceptance. This website model proved to be an effective and practical tool for sellers to establish competitive pricing strategies. Keywords: E-commerce; Price Monitoring; Strategic Pricing; Research and Development; Recommender System Abstrak Penjual pada platform e-commerce menghadapi kesulitan dalam menentukan harga strategis akibat persaingan dinamis. Untuk mengatasi masalah ini, sebuah model aplikasi sistem terintegrasi dikembangkan sebagai solusi untuk membantu penjual. Penelitian ini menggunakan metodologi Research and Development (R&D) dengan waterfall yang mencakup tahap analisis kebutuhan; perancangan sistem; implementasi; verifikasi kinerja dan analisis data; dan evaluasi akhir dan revisi. Hasilnya adalah sebuah aplikasi fungsional yang mampu menyediakan fitur pencarian, rekomendasi harga strategis pada tiga level (terendah, rata-rata/menengah, tertinggi), melihat informasi lebih detail mengenai produk yang dijual dalam bentuk grafik, dan fitur lainnya. Pengujian usabilitas menggunakan usability testing terhadap target pengguna menghasilkan skor rata-rata 95,54% pada customer yang bertipe gratis, 91,26% untuk pengguna bertipe premium, dan 96,8% pengguna yang bertipe pembeli. Hal ini yang mengindikasikan kemudahan penggunaan dan tingkat penerimaan sangat baik. Model website ini terbukti menjadi alat bantu yang efektif dan praktis bagi penjual untuk menetapkan strategi harga kompetitif.
Penerapan RFID dan WhatsApp Gateway untuk Monitoring Kehadiran Siswa secara Real-Time
This study aims to design and implement a real-time student attendance system based on Radio Frequency Identification (RFID) integrated with a WhatsApp Gateway. The background of this research is the need for a fast and accurate attendance system that can instantly provide attendance information to parents. The research method adopts Research and Development (R&D) with a system development approach, including requirements analysis, system architecture design, development of a PHP and MySQL-based web application, integration of the WhatsApp Gateway API (e.g., Fonnte or Wablas), as well as functional and reliability testing. The black-box testing results show that all core functions, such as RFID reading, database recording, and notification delivery, operate as designed without errors. Meanwhile, the load test demonstrates that the system can process more than 500 transactions per minute with a notification delivery success rate of >=98% and an average latency of 2–3 seconds, meeting real-time service standards This system not only provides attendance reports accessible at any time but also enhances parental involvement through instant WhatsApp notifications. These findings confirm the reliability of RFID in automating attendance and introduce novelty through the integration of a WhatsApp Gateway, which has rarely been addressed in previous studies. Keywords: RFID; WhatsApp Gateway; Student Attendance; Real-Time; Attendance Monitoring Abstrak Sistem absensi manual di sekolah masih lambat, rawan manipulasi, dan sulit direkap, sehingga diperlukan solusi presensi yang cepat dan akurat. Penelitian ini bertujuan mengembangkan sistem absensi siswa real-time berbasis RFID yang terintegrasi dengan WhatsApp Gateway untuk mengirimkan notifikasi otomatis kepada orang tua. Penelitian menggunakan pendekatan R&D melalui analisis kebutuhan, perancangan arsitektur, pengembangan aplikasi PHP MySQL, dan integrasi API WhatsApp. Pengujian dilakukan menggunakan metode black-box dan load test. Pada pengujian beban, sistem berhasil memproses hingga 800 transaksi dalam satu gelombang pemindaian puncak, yang merepresentasikan kondisi saat sebagian besar dari 830 siswa melakukan absensi dalam rentang waktu sangat singkat. Tingkat keberhasilan pengiriman notifikasi mencapai 98.7%, dengan latency rata-rata 2.7 detik. Seluruh fungsi berjalan sesuai rancangan tanpa kesalahan. Sistem ini terbukti cepat, stabil, dan efektif untuk mendukung monitoring kehadiran siswa secara real-time serta meningkatkan transparansi komunikasi antara sekolah dan orang tua.
Analisis Clustering Pengguna SIKP FTI UKSW Menggunakan User Experience Questionnaire
The ease of access to technology today encourages the use of technology to improve productivity and efficiency in the lecture process, including in the lecture process at FTI UKSW (Faculty of Information Technology, Satya Wacana Christian University) which uses SIKP (Practical Work Information System). This study identifies the grouping/clustering of SIKP user students based on student user experience. The UEQ and Fuzzy C-Means methods are used to formulate the clustering of SIKP user students with the aim of determining the level of satisfaction of FTI UKSW students with the SIKP currently in use and hopefully providing an analysis of which parts of the FTI UKSW SIKP need to be improved. The UEQ method uses six elements, namely Attractiveness, Clarity, Efficiency, Accuracy, Stimulation, and Novelty. Meanwhile, the Fuzzy C-Means method measures the degree of membership of a data set to determine the satisfaction level grouping. Testing the accuracy of the algorithm's performance showed that the clustering process can form groupings with a level of clarity and cluster separation that provides good performance and can still be developed to obtain more optimal performance. Keywords: Clustering; User Experience Questionnaire; Fuzzy C-Means Abstrak Mudahnya akses teknologi saat ini mendorong pemanfaatan teknologi untuk meningkatkan produktivitas dan efisiensi pada proses perkuliahan, termasuk pada proses perkuliahan di FTI UKSW (Fakultas Teknologi Informasi Universitas Kristen Satya Wacana) yang menggunakan SIKP (Sistem Informasi Kerja Praktek). Penelitian ini mengidentifikasi pengelompokan mahasiswa pengguna SIKP berdasarkan user experience mahasiswa. Metode UEQ dan Fuzzy C-Means adalah metode yang digunakan untuk merumuskan clustering mahasiswa pengguna SIKP dengan tujuan mengetahui tingkat kepuasan mahasiswa FTI UKSW terhadap SIKP yang digunakan saat ini dan diharapkan dapat memberikan analisis bagian mana yang perlu ditingkatkan pada SIKP FTI UKSW. Metode UEQ akan menggunakan 6 elemen yaitu Daya tarik, Kejelasan, Efisiensi, Ketepatan, Stimulasi, dan Kebaruan. Sementara itu, metode Fuzzy C-Means akan mengukur derajat keanggotaan suatu data untuk mengetahui pengelompokan tingkat kepuasan. Pengujian akurasi performa algoritma memberikan hasil bahwa proses clustering dapat membentuk pengelompokan dengan tingkat kejelasan dan pemisahan cluster yang memberikan performa baik serta masih dapat dikembangkan untuk memperoleh performa yang lebih optimal.
Model Aplikasi Point of Sale Berbasis Mobile dengan Modul Pre Order Real Time
Point of sale (POS) systems are critical to retail operations because they serve as the hub for transaction capture, inventory synchronization, and real time reporting, thereby reducing input errors and accelerating service. This study develops a mobile based point of sale application equipped with a real time pre order mechanism to address issues such as manual transaction recording, delayed recap processes, and limited visibility of scheduled orders. Development followed the Waterfall methodology, comprising the Communication, Planning, Modeling, Construction, and Deployment phases. The proposed architecture is mobile first, providing real time synchronization of stock and order status along with a sales-summary dashboard. System testing employed black box methods on core scenarios, including authentication, master data management, transactions and payments, order scheduling, and reporting. The result is a mobile software solution that implements POS and pre order concepts and has been deployed at ALVIN’S Bakery. Keywords: Point of Sale; Mobile Application; Pre Order; Real Time; Waterfall Abstrak Point of sale (POS) system penting bagi bisnis ritel karena menjadi pusat pencatatan transaksi, sinkronisasi stok, dan pelaporan real time yang menekan kesalahan input serta mempercepat layanan. Penelitian ini mengembangkan aplikasi point of sale berbasis mobile yang dilengkapi mekanisme pre order real time untuk mengatasi masalah seperti pencatatan transaksi manual, keterlambatan rekapitulasi, keterbatasan visibilitas pesanan yang dijadwalkan. Metode pengembangan aplikasi menggunakan Waterfall system meliputi: communication, planning, modeling, construction, dan deployment. Arsitektur yang diusulkan bersifat mobile first dengan sinkronisasi stok dan status pesanan secara real time serta dashboard ringkasan penjualan. Pengujian sistem menggunakan black box pada skenario inti, mencakup autentikasi, pengelolaan master data, transaksi dan pembayaran, penjadwalan pesanan, serta pelaporan. Hasil penelitian adalah perangkat lunak berbasis mobile dengan penerapan konsep POS dan pre order yang diterapkan pada salah satu bisnis ALVIN’S Bakery.
Optimalisasi TinyBERT-Vosk Untuk Kontrol Suara Real-Time Robot Mecanum Raspberry Pi
Intelligent Document Processing Berbasis OCR + Transformers dan CNN untuk Verifikasi Dokumen Bantuan Pangan
The rice assistance program is a government initiative to alleviate poverty by distributing rice to low-income households. This study aims to design and implement an Intelligent Document Processing system based on Convolutional Neural Networks (CNN) and hybrid Optical Character Recognition (OCR) to facilitate the verification of social assistance documents. The system integrates the LayoutLMv3 model for text entity extraction and a ResNet-18 CNN for signature-box classification, supported by conventional OCR and TrOCR for challenging text regions. The research methodology covers system design and model development. Experimental results show that the system can successfully convert PDF documents into images and accurately recognize key entities such as names and national ID numbers using LayoutLMv3 (validation accuracy 96.78%). Although ResNet-18 achieves a validation accuracy of 99.04%, it remains biased toward majority classes and is not yet reliable for verification decisions. The proposed system has the potential to accelerate and standardize document verification, with future work focusing on dataset rebalancing and improving the signature classification model. Keywords: LayoutLMv3; ResNet-18; Optical Character Recognition; TrOCR Abstrak Program bantuan pangan beras merupakan upaya pemerintah dalam menanggulangi kemiskinan melalui distribusi beras kepada masyarakat kurang mampu. Penelitian ini bertujuan merancang dan mengimplementasikan Intelligent Document Processing berbasis Convolutional Neural Network (CNN) dan OCR hibrida untuk mempermudah proses verifikasi dokumen bantuan pangan. Sistem ini mengintegrasikan model machine learning LayoutLMv3 untuk ekstraksi entitas teks serta CNN ResNet-18 untuk klasifikasi tanda tangan, dengan dukungan Optical Character Recognition (OCR) dan TrOCR. Metode penelitian mencakup perancangan sistem dan pengembangan perangkat. Hasil pengujian menunjukkan bahwa sistem mampu mengekstraksi dokumen PDF menjadi citra, mengenali entitas seperti nama dan NIK dengan akurasi tinggi menggunakan LayoutLMv3 (val_accuracy 96,78%), serta mengklasifikasikan kotak tanda tangan dengan ResNet-18 (val_accuracy 99,04%). Namun, ResNet-18 masih bias terhadap kelas mayoritas sehingga belum dapat dianggap andal untuk keputusan verifikasi. Sistem yang diusulkan berpotensi mempercepat pemeriksaan dokumen bantuan pangan, sementara pengembangan lanjut difokuskan pada penyeimbangan data dan penyempurnaan model klasifikasi tanda tangan.
Pengembangan Aplikasi Multimedia Pembelajaran Bahasa Inggris
The development of science and technology encourages innovations in the teaching and learning process, one of which is interactive learning multimedia that integrates text, images, audio, video, animation, and simulations to create a more engaging learning experience. English is often considered difficult by students because it is not used in their daily lives, resulting in relatively lower learning outcomes. The problem arises because teachers still use conventional teaching methods and make limited use of digital technology. This study aims to develop an English learning multimedia application to optimize the learning process for both students and teachers. The development method used is the Waterfall model, which includes needs analysis, system design, implementation, testing, and maintenance. Based on the functionality testing results, the developed multimedia application operates properly and meets all system requirements, making it feasible to use as a supporting learning medium. Keywords: Multimedia; Learning; Interactive; Android; Waterfall Abstrak Perkembangan ilmu pengetahuan dan teknologi mendorong pembaruan dalam proses belajar mengajar, salah satunya melalui multimedia pembelajaran interaktif yang menggabungkan teks, gambar, audio, video, animasi, dan simulasi untuk menciptakan pengalaman belajar yang lebih menarik. Bahasa Inggris sering dianggap sulit oleh siswa karena bukan bahasa yang digunakan sehari-hari, sehingga nilai hasil belajar cenderung lebih rendah. Permasalahan muncul karena metode pembelajaran yang digunakan guru masih konvensional dan kurang memanfaatkan teknologi digital. Penelitian ini bertujuan mengembangkan aplikasi multimedia pembelajaran Bahasa Inggris untuk mengoptimalkan proses pembelajaran siswa dan guru. Metode pengembangan yang digunakan adalah Waterfall, meliputi analisis kebutuhan, desain, implementasi, pengujian, dan pemeliharaan. Berdasarkan hasil pengujian fungsionalitas, aplikasi multimedia yang dikembangkan berfungsi dengan baik dan memenuhi seluruh kebutuhan sistem, sehingga dinilai layak digunakan sebagai media pendukung pembelajaran.
Desain User Interface Pendaftaran Akta Kelahiran Dan Kartu Identitas Anak Pada Data Kependudukan
Efficient and easy-to-understand public services are a primary demand in today’s digital era. One essential service that needs improvement is the registration system for birth certificates and Child Identity Cards (KIA). This study aims to design an efficient and user friendly user interface (UI) to facilitate online registration for the public. The Design Thinking method is used as the main approach in this research, consisting of five stages: empathize, define, ideate, prototype, and test. Through interviews and observations involving users and civil registration officers (Disdukcapil), several challenges were identified, such as long queues and the perception of excessive documentation required for registering birth certificates and KIAs. The ideation and prototyping process resulted in a simpler and more understandable UI design. Testing of the prototype showed a significant improvement in users’ understanding of the registration process and their overall satisfaction. Therefore, the Design Thinking approach has proven effective in producing a UI design that meets user needs and enhances the efficiency of digital public services. Keywords: Design user interface; Design Thinking; Birth certificate registration; Child identity card registration Abstrak Pelayanan publik yang efisien dan mudah dipahami merupakan tuntutan utama dalam era digital saat ini. Salah satu layanan penting yang perlu ditingkatkan adalah sistem pendaftaran akta kelahiran dan Kartu Identitas Anak (KIA). Penelitian ini bertujuan untuk merancang antarmuka pengguna (UI) yang efisien dan user-friendly guna mempermudah masyarakat dalam melakukan pendaftaran secara daring. Metode Design Thinking digunakan sebagai pendekatan utama dalam penelitian ini, yang terdiri dari lima tahap: empathize, define, ideate, prototype, dan test. Melalui wawancara dan observasi terhadap pengguna serta petugas Disdukcapil, ditemukan berbagai kendala seperti antrian yang panjang serta dokumen yang dianggap banyak dalam mengurus pendaftaran akta kelahiran dan Kartu Identitas Anak (KIA). Hasil dari proses ideasi dan prototyping menghasilkan desain UI yang lebih sederhana, dan mudah dipahami. Pengujian terhadap prototype menunjukkan peningkatan signifikan dalam pemahaman alur pendaftaran dan kepuasan pengguna. Dengan demikian, pendekatan Design Thinking terbukti efektif dalam menghasilkan desain UI yang mampu menjawab kebutuhan pengguna serta meningkatkan efisiensi layanan publik digital.
Designing a Data Mart at the Indonesian Psychological Healthcare Center Using Pentaho
Critical data is essential for organisational success, providing essential information for decision-making. This study focuses on the Indonesian Mental Health Centre (IndoPsyCare), which utilises data to improve service performance. The research aims to design a data mart to support analytical needs by transforming the Online Transaction Processing (OLTP) database into Online Analytical Processing (OLAP). This design follows Kimball's Nine-Step Approach and integrates the Extract, Transform, Load (ETL) process. Data was collected from August 2023 to August 2024 through interviews and electronic documentation. The system was implemented using Microsoft SQL Server Management Studio 18 and Pentaho Data Integration. The resulting data mart uses a star schema with fact and dimension tables tailored to stakeholder needs, particularly in appointment analysis. Findings indicate that the data mart facilitates efficient access, targeted analysis, and improved query performance, thereby supporting IndoPsyCare's decision-making and overall service optimisation.
Sistem Reservasi Berbasis Android dengan Dasbor Analitik
The conventional reservation process at Ema Mom Kids Baby Spa has led to various operational issues, such as scheduling errors and inefficient queues. This research aims to address these challenges through the development of an integrated digital reservation system. This study successfully produced a dual-platform system (a mobile application for the business owner and a web platform for customers) that manages booking, schedule, and service data. Testing using the black-box method demonstrated that all core system functionalities from booking to schedule management performed validly. The system also features a descriptive analytics dashboard that serves as a data-driven decision-making tool for the business owner. The developed system has proven effective in improving operational efficiency, minimizing operational errors, and providing strategic business insights. Keywords: Reservation system; Analytics dashboard; Baby spa; Schedule management; Android Abstrak Proses reservasi konvensional di Ema Mom Kids Baby Spa memicu antrean yang tidak efisien dan kesalahan jadwal. Untuk mengatasi masalah itu, sebuah sistem reservasi digital terintegrasi dikembangkan. Penelitian ini berhasil menghasilkan sistem dua platform (aplikasi mobile untuk pemilik usaha dan platform web untuk pelanggan) yang mampu mengelola seluruh fungsionalitas utama. Validasi fungsional menggunakan metode black box testing membuktikan bahwa seluruh fitur inti, dari alur pemesanan hingga pengelolaan jadwal, telah berjalan sesuai harapan. Aplikasi ini juga menyediakan dasbor analitik deskriptif yang berfungsi sebagai alat bantu pengambilan keputusan berbasis data bagi pemilik usaha. Implementasi sistem ini terbukti tidak hanya meningkatkan efisiensi dan meminimalkan galat (error) operasional, tetapi juga menyediakan wawasan bisnis strategis bagi pemilik.
Designing a Dashboard for Sales Trends and Analysis at Coventown Glodok Jakarta
Coventown is a coffee shop with a Chinese theme located in the Petak Enam Glodok area, Jakarta. The sales dashboard design allows Coventown Glodok Jakarta to identify sales patterns, and predict sales performance through historical data analysis. This study aims to present data in easy-to-understand visualizations to demonstrate the use of dashboards in improving the efficiency of sales monitoring, providing strategic insights into trends, and achieving sales targets to support more precise and effective decision-making. The prototype development iterations were planned quickly, and the data was processed using Microsoft Power BI and the Python programming language. The results of this study show that the sales dashboard at Coventown Glodok Jakarta effectively monitors sales in line with Key Performance Indicators (KPIs) such as time, category, product, price, and quantity. The resulting dashboard improves the efficiency of monitoring sales performance and helps the company display sales trends for best-selling products.
Machine Learning Classification of Insomnia Using Multidimensional Features and SMOTE
Sleep disorders, especially insomnia, are common among adolescents and negatively affect health. Early detection is crucial for appropriate treatment. This study aims to classify insomnia severity in adolescents using a Machine Learning (ML) model and multidimensional features derived from 19 questionnaire instruments. The dataset consists of 95 adolescents aged 16–19 years, categorized into Insomnia, Subclinical Insomnia, and Control classes. The modeling process includes reducing multicollinearity, class balancing with SMOTE, and hyperparameter optimization using GridSearchCV and StratifiedKFold. Feature importance analysis was conducted using decision tree-based methods and permutation importance. The results show that SMOTE improves SVM performance from 0.690 to 0.793 and positively affects Random Forest. Logistic Regression performs best without SMOTE (accuracy 0.759), while XGBoost shows the lowest accuracy (0.614) even with SMOTE. A total of 11 features consistently contribute to all models. In conclusion, ML models, particularly SVM, are effective for classifying insomnia severity in adolescents.
Business Process Reengineering Pencatatan Laporan Harian Mandor Pada PT. Nyalindung Menggunakan ODOO
The manual recording of the Foreman's Daily Report (LHM) at PT Nyalindung has caused various problems, such as inefficiency, potential data errors, and delays in the reporting process. This research aims to apply the Business Process Reengineering (BPR) approach by utilizing the Odoo system to automate and simplify the LHM recording workflow. The methods used include analyzing current business processes, identifying system requirements, redesigning workflows, and implementing web-based Odoo custom modules. The results showed a significant increase in reporting speed with an initial result of 60% increasing to 72%, as well as efficiency in resource usage. Based on these findings, it can be concluded that the implementation of BPR using Odoo can effectively optimize the LHM recording process. Keywords: Business Process Reengineering; Foreman Daily Report; Odoo; Automation, Business Process Efficiency. Abstrak Pencatatan Laporan Harian Mandor (LHM) di PT. Nyalindung yang masih dilakukan secara manual menimbulkan berbagai permasalahan, seperti ketidakefisienan, potensi kesalahan data, serta keterlambatan dalam proses pelaporan. Penelitian ini bertujuan menerapkan pendekatan Business Process Reengineering (BPR) dengan memanfaatkan sistem Odoo untuk mengotomatisasi dan menyederhanakan alur kerja pencatatan LHM. Metode yang digunakan mencakup analisis proses bisnis saat ini, identifikasi kebutuhan sistem, perancangan ulang alur kerja, hingga implementasi modul kostum Odoo berbasis web. Hasil penelitian menunjukkan adanya peningkatan signifikan dalam hal kecepatan pelaporan dengan hasil awal 60% meningkat menjadi 72%, serta efisiensi penggunaan sumber daya. Berdasarkan temuan tersebut, dapat disimpulkan bahwa penerapan BPR menggunakan Odoo secara efektif mampu mengoptimalkan proses pencatatan LHM.
Data Warehouse for Study Program Accreditation Data Management at Private University X
Technological advancements in the Fourth Industrial Revolution have transformed higher education, yet accreditation processes often suffer from inefficient data management, causing delays, inconsistencies, and limited monitoring of performance indicators. This study designs a data warehouse integrated with a dashboard and Early Warning System (EWS) to improve accreditation data management at University X, West Jakarta. Using Kimball’s Dimensional Model, data were structured into fact and dimension tables, with collection methods including interviews with the Head of the IT Department and document analysis of accreditation processes. Descriptive statistics were applied to identify trends and patterns. The dashboard enables real-time visualisation of key metrics, while the EWS issues alerts on missing or outdated data. Results show that the snowflake schema enhances data organisation and clarity, reducing manual processing and facilitating proactive monitoring. The system supports more efficient accreditation management and strengthens institutional readiness for assessment.
Analisis Usability eOffice UKSW Menggunakan Technology Acceptance Model
Design of a Data Mart for Optimizing Product Sales Analysis at PT. X
With the advancement of technology, companies can now efficiently manage and analyze their data, providing valuable insights that support better business decisions. PT. X is one such company that wants to leverage this capability to optimize its sales data analysis. Therefore, the goal of this study is to design an effective data mart. During the development process, we used Kimbal's Nine-Step Methodology alongside the ETL (extract, transform, load) process to ensure the data was accurately extracted, transformed, and loaded into the data mart. The outcome of this research is a data mart and star schema tailored to PT. X specific needs. Testing results showed that query execution time increased by 40% and data accuracy improved by 80%. Report generation time was also optimized, resulting in a process that was 83% faster. These results demonstrate how a well-structured data mart can improve decision-making efficiency and data reliability.
Pengembangan Aplikasi Pengaduan dan Monitoring Fasilitas Umum
Public facilities such as roads, street lights and drainage channels are vital elements that support the comfort and safety of the community. However, damage to these facilities is often not repaired immediately due to the lack of an integrated reporting and monitoring system. This study aims to design and develop a mobile and web-based public facility complaint and monitoring application that allows the community to report damage directly, along with the location, description, and photos. The system was developed using the Waterfall method, which includes needs analysis, design, implementation, testing, and maintenance. Testing results using Black Box Testing show that all features function according to specifications, including location-based reporting and real-time status monitoring. Keywords: Public facilities; Reporting system; Mobile application; Digitization of public services; Monitoring. Abstrak Fasilitas umum seperti jalan, lampu penerangan, dan saluran drainase merupakan elemen vital yang menunjang kenyamanan dan keselamatan masyarakat. Namun, kerusakan pada fasilitas tersebut sering kali tidak segera diperbaiki karena belum adanya sistem pelaporan dan pemantauan yang terintegrasi. Penelitian ini bertujuan untuk merancang dan mengembangkan aplikasi pengaduan dan monitoring fasilitas umum berbasis mobile dan web yang memungkinkan masyarakat melaporkan kerusakan secara langsung disertai lokasi, deskripsi, dan foto. Pengembangan sistem dilakukan menggunakan metode Waterfall yang meliputi analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Hasil pengujian menggunakan Black Box Testing menunjukkan bahwa seluruh fitur berfungsi sesuai spesifikasi, termasuk pelaporan berbasis lokasi dan pemantauan status secara real-time.
Pengembangan Aplikasi Sistem Informasi Perpustakaan untuk Efisiensi Layanan di SMP Negeri 4 Mauliru
The library is a strategic element as a learning resource center at Mauliru State Junior High School 4. However, the manual management process results in low service effectiveness, potential recording errors, and limited access to information on book collection availability. To address these issues, this study designed and developed a web-based library information system using the Extreme Programming (XP) method. This system provides centralized data management, real-time book borrowing and return services, automatic updates on availability status, fine calculations, and transaction history as a basis for reporting. Testing using the black box method showed that all system functions ran according to specifications and were able to improve service efficiency, data accuracy, and information access. Thus, this system is suitable for implementation as a library digitization solution to support the learning process Keywords: Black Box Testing; Extreme Programming; Mauliru State Junior High School 4 Abstrak Perpustakaan merupakan elemen strategis sebagai pusat sumber belajar di SMP Negeri 4 Mauliru. Namun, proses pengelolaan yang masih dilakukan secara manual menimbulkan rendahnya efektivitas layanan, potensi kesalahan pencatatan, serta keterbatasan akses informasi ketersediaan koleksi buku. Untuk mengatasi permasalahan tersebut, penelitian ini merancang dan mengembangkan sistem informasi perpustakaan berbasis web dengan metode Extreme Programming (XP). Sistem ini menyediakan pengelolaan data terpusat, layanan peminjaman dan pengembalian buku secara real-time, pembaruan otomatis status ketersediaan, perhitungan denda, serta riwayat transaksi sebagai dasar pelaporan. Hasil pengujian menggunakan metode black box menunjukkan seluruh fungsi sistem berjalan sesuai spesifikasi dan mampu meningkatkan efisiensi layanan, keakuratan data, serta akses informasi. Dengan demikian, sistem ini layak diterapkan sebagai solusi digitalisasi perpustakaan untuk mendukung proses pembelajaran.
Implementasi Dynamic Pricing dan Point System pada Aplikasi Pemesanan Air Minum Isi Ulang
The increasing demand for refillable drinking water, driven by population growth and a practical lifestyle, creates the need for an efficient ordering system. On the other hand, the manual ordering process still faces constraints such as inefficient record-keeping, delivery delays, and a lack of incentives for loyal customers. This research focuses on developing a mobile-based water ordering application that integrates the concepts of dynamic pricing and a point system to improve operational efficiency and strengthen customer retention. The dynamic pricing model is implemented to adjust prices based on the time of ordering, while the point system is designed to encourage repeat transactions. The system was developed using Flutter for the user application and React with Firebase for the admin dashboard. System testing using the Black Box Testing method shows that the main system functionalities encompassing the ordering process, dynamic pricing calculation, and point system management have operated in accordance with the functional design. Keywords: Drinking water ordering; Dynamic pricing; point system; Mobile application; Flutter; Firebase Abstrak Permintaan air minum isi ulang yang meningkat akibat pertumbuhan populasi dan gaya hidup praktis menimbulkan kebutuhan akan sistem pemesanan yang efisien. Di sisi lain, proses pemesanan manual masih menghadapi kendala seperti ketidakefisienan pencatatan, keterlambatan pengiriman, dan kurangnya insentif bagi pelanggan setia. Penelitian ini berfokus pada pengembangan aplikasi pemesanan air minum berbasis mobile yang mengintegrasikan konsep dynamic pricing dan point system guna meningkatkan efisiensi operasional serta memperkuat retensi pelanggan. Model dynamic pricing diterapkan guna menyesuaikan harga berdasarkan waktu pemesanan, sedangkan point system dirancang untuk mendorong transaksi berulang. Sistem dikembangkan menggunakan Flutter sebagai aplikasi pengguna dan React dengan Firebase untuk dashboard admin. Pengujian sistem menggunakan metode Black Box Testing menunjukkan bahwa fungsionalitas utama sistem mencakup proses pemesanan, kalkulasi dynamic pricing, dan pengelolaan point system telah berjalan sesuai dengan rancangan fungsional.
Rancang Bangun Aplikasi Rumah Aspirasi Berbasis Android dengan Metode Rapid Application Development
Technology development encourages government services to become more effective and responsive. However, in practice, the process of submitting public aspirations in Surabaya is still carried out manually, making it prone to delays and data inaccuracies. This study aims to design and develop the Rumah Aspirasi Android application as a more structured platform for managing citizen feedback and complaints. The system was developed using the Rapid Application Development (RAD) method through two iterations, followed by evaluation using Blackbox Testing and User Acceptance Testing (UAT). The results show that all main functions run well, with the UAT achieving an average score of more than 93% for functionality, reliability, usability, and efficiency. These findings indicate that the application can improve the efficiency and quality of services in managing public aspirations in Surabaya. Keyword: Rumah Aspirasi; Deputy Mayor of Surabaya; Kotlin; Rapid Application Development Abstrak Perkembangan teknologi menuntut layanan pemerintah untuk bertransformasi menjadi lebih efektif dan responsif. Namun, dalam praktiknya, proses penyampaian aspirasi masyarakat di Surabaya masih dilakukan secara manual, sehingga rentan mengalami keterlambatan dan ketidaktepatan data. Penelitian ini bertujuan merancang dan mengembangkan aplikasi Rumah Aspirasi berbasis Android sebagai sarana yang lebih terstruktur dalam pengelolaan masukan dan keluhan warga. Metode pengembangan yang digunakan adalah Rapid Application Development (RAD) melalui dua tahapan iterasi, disertai evaluasi sistem menggunakan Blackbox Testing dan User Acceptance Testing (UAT). Hasil pengujian menunjukkan bahwa seluruh fungsi utama berjalan dengan baik, dengan tingkat kelayakan UAT rata-rata melebihi 93% pada aspek functionality, reliability, usability, dan efficiency. Hasil ini menunjukkan bahwa aplikasi yang dikembangkan mampu meningkatkan efisiensi dan kualitas layanan dalam pengelolaan aspirasi Masyarakat Kota Surabaya.
Analytical Hierarchy Process Method of Prosthetic and Orthotic Materials for Patient Needs
This study highlights the importance of optimising assistive devices for people with disability, as the law says in Law No. 8 of 2016. Ligar Mandiri Prosthetics and Orthopaedics Clinic offer related services, where choosing the right materials is getting more complicated because of fast tech advances and lots of options like carbon fibre, plastic, and titanium. This study aims to develop a Decision Support System (DSS) to assist practitioners in selecting materials based on quality, cost, and production time criteria. The alternatives evaluated include local, semi-imported, and imported materials. Using the Analytic Hierarchy Process (AHP) method, the analysis shows that imported materials received the highest ranking (0.54708), followed by local materials (0.24157) and semi-imported materials (0.21155). The DSS developed in this study has enhanced the accuracy, efficiency, and transparency of decision-making, thereby supporting practitioners in providing effective and reliable prosthetic and orthotic solutions.
Perbandingan Naïve Bayes dan SVM untuk Analisis Sentimen Pengguna Aplikasi X Terhadap Danantara
The Indonesian Investment Management Agency Danantara program is an initiativeby the Indonesian government aimed at strengthening the national economy through the management of strategic assets and the enhancement of global investment. The launch of this program has generated various public responses on social media, particularly on the X (Twitter) platform. This study aims to analyze public sentiment in Indoensia roward the Danantara program and to compare the performance of two machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The analysis process begins with data collection through web crawling, followed by data preprocessing, sentiment labeling using a hybrid lexicon-based approach, TF-IDF weighting, and classsifcation using both algorithms. Model Evaluation is conducted using accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM algorithm achieves an accuracy of 87% while the Naïve Bayes algorithm attains an accuracy of 83,7%. These findings demonstrate that SVM outperforms Naïve Bayes in sentiment text classification. The result of this study is expected to serve as a reference for understanding public perception and as a consideration for policymakers. Keywords: Sentiment Analysis; Danantara; Support Vector Machine; Naïve Bayes; Lexicon-Based Abstrak Program Badan Pengelola Investasi Danantara merupakan inisiatif pemerintah indonesia yang bertujuan untuk memperkuat perekonomian nasional melalui pengelolaan aset strategis dan peningkatan investasi global. Peluncuran program ini memunculkan berbagai tanggapan di media sosial, terutama di platform X (Twitter). Penelitian ini bertujuan untuk menganalisis sentimen masyarakat Indonesia terhadap program Danantara serta membandingkan kinerja dua algoritma machine learning, yaitu Naïve Bayes dan Support Vector Machine (SVM). Proses analisis dimulai dengan pengumpulan data dengan crawling, preprocessing data, pelabelan menggunakan pendekatan hybrid Lexicon-based, pembobotan TF-IDF, serta klasifikasi menggunakan kedua algoritma. Evaluasi dilakukan dengan metrik accuracy, precision, recall, dan f1-score. Hasil penelitian menunjukkan bahwa algoritma SVM memperoleh nilai akurasi sebesar 87% dan Naïve Bayes memperoleh akurasi sebesar 83,7%. Temuan ini menunjukkan bahwa SVM lebih unggul dalam mengklasifikasikan teks sentimen. Hasil penelitian ini diharapkan dapat menjadi referensi dalam memahami persepsi publik serta menjadi bahan pertimbangan bagi pemangku kebijakan.
Design of Diabetes Prediction Interface Using E-ss and Classification Tree Algorithm
Diabetes was a chronic disease that continued to increase globally, making early detection essential to reduce long-term complications. This study aimed to develop a desktop-based diabetes prediction system that provided fast and simple classification results for medical personnel and individual users. The system used the entropy-based subset selection (E-ss) method to choose the most relevant attributes and a classification tree to classify the risk. The dataset from the National Institute of Diabetes and Digestive and Kidney Diseases, contained 768 patient records with attributes such as number of pregnancies, glucose level, blood pressure, and other risk factors. The E-ss process produced three attributes with the highest information scores, namely body mass index (BMI), blood pressure, and triceps skinfold thickness. These three attributes were then used as input to the classification tree model to generate diabetes risk predictions. Cross-validation testing showed an accuracy of up to 78.95%. These findings indicated that E-ss feature reduction helped maintain prediction performance while improving computational efficiency. This system was expected to serve as a practical and reliable diagnostic tool.
Perancangan Sistem Informasi Account Officer Berbasis Web Menggunakan Design Science Research
With the continuous evolution of the digital era and the rapid development of modern technology, the need for efficient information systems has become increasingly vital, especially in the banking sector. This study aims to design a web-based Account Officer (AO) information system for BPR Weleri Makmur to enhance the efficiency of lead generation and customer data management. Previously, AOs relied on manual approaches such as brochure distribution and direct visits, which were less effective and time-consuming. The research applies the Design Science Research Methodology (DSRM), consisting of stages including problem identification, goal definition, system design, demonstration, evaluation, and communication. The system includes features for customer data management, visit scheduling, multi-channel communication (WhatsApp, SMS, and email), performance reporting, and data analytics to identify potential clients. Based on blackbox testing, all functionalities operate as expected. The implementation improves AO productivity, speeds up customer analysis, and supports data-driven decision-making, contributing to the advancement of web-based banking information systems. Keywords: Information System; Account Officer; Web Base; Design Science Research Methodology Abstrak Perkembangan teknologi digital mendorong kebutuhan akan sistem informasi yang efisien, termasuk di sektor perbankan. Penelitian ini bertujuan merancang sistem informasi Account Officer (AO) berbasis web pada BPR Weleri Makmur untuk meningkatkan efektivitas proses pencarian dan pengelolaan data nasabah. Sebelumnya, kegiatan AO masih dilakukan secara manual melalui penyebaran brosur dan kunjungan langsung yang kurang efisien. Metode yang digunakan adalah Design Science Research Methodology (DSRM) dengan tahapan identifikasi masalah, penentuan tujuan, perancangan, demonstrasi, evaluasi, dan komunikasi. Pengujian dilakukan menggunakan metode Blackbox untuk memastikan kesesuaian fungsi sistem. Hasil penelitian menunjukkan bahwa seluruh fitur berjalan dengan baik dan mampu membantu AO dalam mengelola data nasabah, mempercepat proses analisis calon nasabah, serta mendukung pengambilan keputusan berbasis data. Sistem ini berkontribusi dalam peningkatan kinerja perbankan melalui penerapan teknologi informasi berbasis web.
Analisis Sentimen TikTok Shop pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes
The progress of TikTok Shop as an e-commerce platform has generated various public responses, including both support and criticism. Therefore, sentiment analysis is needed to determine user opinion. The purpose of this study is to categorize Twitter users' sentiments about TikTok Shop into positive, negative, and neutral categories. Data was collected through web scraping techniques, followed by pre-processing stages, namely case folding, tokenization, stopword removal, and stemming. Next, features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF). The Naïve Bayes algorithm was applied to classify the sentiment in the processed tweets. To perform the evaluation, a confusion matrix was used with accuracy, precision, recall, and F1-score metrics. The evaluation results show that the model achieves high accuracy (0.9602). However, the model experiences problems in classifying minority classes, namely positive and negative classes, due to an unbalanced class distribution, with neutral classes dominating the dataset. These findings highlight the importance of addressing class imbalance to improve model performance in predicting positive and negative sentiment. Keywords: Sentiment Analysis; TikTok Shop; Social Media; Twitter; Naïve Bayes Abstrak Kemajuan TikTok Shop sebagai platform e-commerce memunculkan berbagai tanggapan publik yang mencakup dukungan maupun kritik. Oleh karena itu, untuk mengetahui opini pengguna diperlukan analisis sentimen. Tujuan penelitian adalah untuk membagi sentimen pengguna Twitter tentang TikTok Shop menjadi kategori positif, negatif, dan netral. Data dikumpulkan melalui teknik web scraping kemudian diikuti tahap pre-pemrosesan, yaitu case folding, tokenisasi, stopword removal, dan stemming. Selanjutnya fitur diekstraksi menggunakan Term Frequency-Inverse Document Frequency (TF-IDF). Algoritma Naïve Bayes diterapkan untuk mengklasifikasikan sentimen pada tweet yang telah diproses. Untuk melakukan evaluasi, confusion matrix digunakan dengan metrik akurasi, precision, recall, dan F1-score. Hasil evaluasi menunjukkan bahwa model mencapai akurasi yang tinggi (0,9602). Namun, model mengalami masalah dalam klasifikasi kelas minoritas, yaitu kelas positif dan negatif, yang disebabkan oleh distribusi kelas yang tidak seimbang, dengan kelas netral yang mendominasi dataset. Temuan ini menunjukkan pentingnya menangani ketidakseimbangan kelas untuk meningkatkan kinerja model dalam memprediksi sentimen positif dan negatif.
Analisis Pola Penjualan Menu Makanan Dengan Metode Regresi Linier Dan Visualisasi Interaktif
Micro, small, and medium enterprises (MSME) often face difficulties in analyzing sales data to support accurate business planning. This study aims to identify sales patterns and forecast demand for the MSME Bakmi Ceria using the linear regression method. The dataset consists of monthly online sales records from Gojek and Grab platforms for four main menus: Mie Ayam, Mie Bakso, Mie Pangsit, and Mie Komplit. The independent variable analyzed is time (month), while the dependent variable is the sales quantity of each menu. Model performance was evaluated using Mean Absolute Percentage Error (MAPE) and the coefficient of determination (R²). Results show that three menus exhibited upward trends, while Mie Komplit declined. MAPE values ranged from 2.55% to 6.98%, with R² reaching 0.99, indicating high model accuracy. These findings confirm that linear regression effectively supports stock planning, promotion strategies, and data-driven decision-making for MSMEs. Keywords: Micro, small, and medium enterprises; Sales pattern; Linear regression; Prediction, Python Abstrak UMKM sering menghadapi kendala dalam menganalisis data penjualan untuk merencanakan strategi bisnis secara tepat. Penelitian ini bertujuan untuk mengidentifikasi pola penjualan dan memprediksi permintaan pada UMKM Bakmi Ceria dengan metode regresi linier. Data yang digunakan berupa catatan penjualan bulanan dari platform Gojek dan Grab terhadap empat menu utama: Mie Ayam, Mie Bakso, Mie Pangsit, dan Mie Komplit. Variabel independen yang dianalisis adalah waktu (bulan), sedangkan variabel dependen berupa jumlah penjualan tiap menu. Model diuji menggunakan Mean Absolute Percentage Error (MAPE) dan koefisien determinasi (R²) untuk menilai akurasi. Hasil menunjukkan tiga menu mengalami tren kenaikan, sedangkan Mie Komplit menurun. Nilai MAPE berkisar 2,55%–6,98% dengan R² mencapai 0,99, menunjukkan tingkat akurasi yang tinggi. Temuan ini menegaskan bahwa regresi linier efektif dalam mendukung perencanaan stok, promosi, dan pengambilan keputusan berbasis data pada UMKM.
Sistem E-Commerce Perlengkapan Aquatic Berbasis Web dengan Payment Gateway dan Firebase Cloud Messaging
Sky Aquatic Store in Dukun, Magelang faces several operational challenges, including limited customer reach, error-prone manual stock management, and inefficient transaction communication. This study develops a web-based e-commerce system integrated with the Midtrans payment gateway and Firebase Cloud Messaging (FCM) to support the digitalization of micro, small, and medium enterprises (MSMEs). The system was built using React.js for the frontend, Node.js with Express.js for the backend, and MySQL as the database, employing the Agile Scrum methodology over an eight-week development cycle. Black-box testing conducted on ten test cases demonstrated a 100% success rate under both normal and abnormal conditions. The system successfully integrates multi-method payments (Virtual Account, E-wallet, and Credit Card) via Midtrans, along with FCM infrastructure for real-time notifications. The results confirm that all core features operate according to specifications with robust error handling, providing a comprehensive e-commerce solution that supports the digital transformation of aquatic equipment MSMEs. Keywords: E-commerce; Payment gateway; Firebase Cloud Messaging; REST API; Aquatic equipment Abstrak Toko Sky Aquatic di Dukun, Magelang menghadapi kendala jangkauan pelanggan terbatas, pengelolaan stok manual yang rawan kesalahan, dan komunikasi transaksi tidak efisien. Penelitian ini mengembangkan sistem e-commerce berbasis web terintegrasi dengan payment gateway Midtrans dan Firebase Cloud Messaging (FCM) untuk mendukung digitalisasi UMKM. Sistem dibangun menggunakan React.js (frontend), Node.js dengan Express.js (backend), dan MySQL (database), dengan metodologi Agile Scrum selama 8 minggu. Pengujian black-box testing terhadap 10 test case menunjukkan tingkat keberhasilan 100% pada kondisi normal dan tidak normal. Sistem berhasil mengintegrasikan payment gateway multi-metode (Virtual Account, E-wallet, Kartu Kredit) dan infrastruktur FCM untuk notifikasi real-time. Hasil pengujian membuktikan seluruh fitur utama berfungsi sesuai spesifikasi dengan error handling yang robust, menyediakan solusi e-commerce komprehensif untuk transformasi digital UMKM perlengkapan aquatic.
Penerapan Metode Prototype untuk Membangun Sistem Informasi Pemilihan Mahasiswa Berprestasi di Universitas
The Selection Process for Outstanding Students (PILMAPRES) at the Faculty of Science and Technology still relies on manual processes, which are prone to recording errors and lack integrated digital documentation. This project aims to create a web-based information system to improve efficiency, accuracy, and data management in the selection process. The development method used is prototyping, which facilitates gradual system development through continuous user evaluation. This system includes essential functions, such as role-based procedures for all stakeholders and automated final score calculations by applying a weighting scheme tailored to the official PILMAPRES guidelines, thereby overcoming challenges related to data accuracy and centralization. The results of system function testing using the Black Box Testing method show that all implemented functions work as needed. Therefore, this system can be the right solution for managing PILMAPRES selection in a more systematic and objective manner. Kata kunci: Information System; PILMAPRES; Prototype Method; Process Automation. Abstrak Proses Seleksi Mahasiswa Berprestasi (PILMAPRES) di Fakultas Sains dan Teknologi masih bergantung pada proses manual, yang rentan terhadap kesalahan pencatatan dan kurangnya dokumentasi digital terintegrasi. Proyek ini bertujuan untuk menciptakan sistem informasi berbasis web untuk meningkatkan efisiensi, akurasi, dan pengelolaan data dalam proses seleksi. Metode pengembangan yang digunakan adalah prototyping, yang memfasilitasi pengembangan sistem secara bertahap melalui evaluasi pengguna yang berkelanjutan. Sistem ini mencakup fungsi-fungsi esensial, seperti prosedur berbasis peran untuk semua pemangku kepentingan dan perhitungan skor akhir dilakukan secara terotomatisasi dengan menerapkan skema pembobotan yang disesuaikan dengan pedoman resmi PILMAPRES, sehingga mengatasi tantangan terkait akurasi dan sentralisasi data. Evaluasi fungsionalitas melalui pendekatan Black Box Testing mengonfirmasi bahwa seluruh fitur sistem telah beroperasi sesuai kebutuhan. Oleh karena itu, sistem ini dapat menjadi solusi yang tepat untuk mengelola seleksi PILMAPRES secara lebih sistematis dan objektif.
Cross Selling Untuk Promosi Produk Dengan Metode Apriori di Toko Sapphira Cake N Dessert Kabupaten Batang
Business competition encourages culinary enterprises to understand consumer purchasing patterns as a basis for marketing decisions. This study aims to analyze product association patterns by applying the Apriori algorithm to sales transaction data consisting of three product categories: Cookies, Brownies, and Es Sticky Milk. A total of 20 transactions were digitally processed to calculate support and confidence values for each item combination. The testing parameters used were a minimum support of 0.85 and a minimum confidence of 0.90. The results show that the Apriori algorithm successfully identifies frequent itemsets and produces association rules that meet the required thresholds. These findings prove that Apriori is effective in revealing consumer buying patterns and can be utilized to support marketing strategies such as Ice Chocolate and Red Velvet White Choco Cheese product recommendations or bundling promotions. Keywords: Cross-Selling; Product Promotion; Data Mining; Apriori Method; MSMEs Abstrak Persaingan bisnis mendorong pelaku usaha kuliner untuk memahami pola pembelian konsumen sebagai dasar pengambilan keputusan pemasaran. Penelitian ini bertujuan menganalisis hubungan antar item produk penjualan dengan menerapkan algoritma Apriori pada data transaksi toko yang terdiri dari tiga kategori produk, yaitu Cookies, Brownies, dan Es Sticky Milk. Sebanyak 20 transaksi diproses secara digital untuk menghitung nilai support dan confidence setiap kombinasi produk. Parameter yang digunakan dalam pengujian adalah minimum support 0,85 dan minimum confidence 0,90. Hasil pengujian menunjukkan bahwa algoritma Apriori mampu mengidentifikasi itemset yang sering muncul secara bersamaan dan menghasilkan rule asosiasi dengan tingkat akurasi yang memenuhi ambang batas parameter. Temuan ini membuktikan bahwa penerapan algoritma Apriori efektif dalam menampilkan pola pembelian konsumen dan dapat menjadi dasar strategi pemasaran seperti produk Es Coklat dan Red Velvet White Choco Cheese menjadi pola paling dominan dibeli oleh pelanggan, sehingga pola penjualan produk Es Coklat dan Red Velvet White Choco Cheese dapat menjadi rekomendasi produk atau promosi bundling.
Implementasi Teknologi Geolocation Pada Sistem Pelaporan Parkir Liar Berbasis Web
This study is motivated by the low effectiveness of reporting illegal parking violations in urban and tourist areas, where the current manual reporting process often leads to delays in handling incidents and difficulties in accurately identifying the location. To address these issues, this research aims to develop an information system for reporting illegal parking based on web and mobile platforms by utilizing geolocation technology to make the reporting process faster, more accurate, and easier for the public to use. The system was developed using the Waterfall model, which includes the stages of requirements analysis, design, implementation, and testing using the Black-box method. It integrates the Google Maps API to automatically detect the reporter’s location and uses PostgreSQL as the main database for storing reports. The testing results indicate that all system features operate according to user requirements and accurately display incident locations, enabling more efficient reporting, assisting officers during verification processes, and supporting the digitalization of public services in the transportation sector. Keywords: Reporting System; Geolocation; API; Web Base Abstrak Penelitian ini dilatarbelakangi oleh rendahnya efektivitas pelaporan pelanggaran parkir liar di kawasan wisata dan perkotaan, di mana proses pelaporan yang masih dilakukan secara manual menyebabkan keterlambatan penanganan serta kesulitan dalam menentukan lokasi kejadian secara tepat. Untuk mengatasi masalah tersebut, penelitian ini bertujuan mengembangkan sistem informasi pelaporan parkir liar berbasis web dan mobile dengan memanfaatkan teknologi geolokasi agar proses pelaporan menjadi lebih cepat, akurat, dan mudah diakses oleh masyarakat. Sistem dikembangkan menggunakan model Waterfall yang meliputi tahap analisis kebutuhan, perancangan, implementasi, dan pengujian menggunakan metode Black-box. Sistem ini terintegrasi dengan Google Maps API untuk mendeteksi lokasi pelapor secara otomatis dan menggunakan PostgreSQL sebagai basis data penyimpanan laporan. Hasil pengujian menunjukkan bahwa seluruh fitur sistem telah berfungsi sesuai kebutuhan pengguna dan mampu menampilkan lokasi kejadian dengan akurat, sehingga sistem yang dibangun dapat meningkatkan efisiensi pelaporan, membantu proses verifikasi petugas, serta mendukung digitalisasi layanan publik di sektor transportasi.
Analisis Perbandingan Algoritma Linear Regression dan Polynomial Regression dalam Memprediksi Durasi Rawat Inap Pasien Rumah Sakit
The length of stay (LoS) of hospital patients is an essential indicator for measuring the efficiency of healthcare services. Accurate LoS prediction helps hospitals optimize resource management, estimate costs, and improve service quality. This study compares the performance of Linear Regression and Polynomial Regression algorithms in predicting patient LoS. The dataset, obtained from Kaggle, consists of 835 patient records that underwent preprocessing and transformation. The independent variables include gender, age, disease type, and type of service, while LoS serves as the dependent variable. The research applies the Knowledge Discovery from Data (KDD) approach, which includes the stages of selection, cleaning, transformation, data mining, and evaluation. Experiments were conducted using three data-splitting ratios (70:30, 80:20, and 90:10) with evaluation metrics MAE, MSE, RMSE, and R². The results show that Linear Regression performed slightly better, with average R² values ranging between 0.18 and 0.20, indicating its potential to support hospital management efficiency. Keywords: Length of Stay; Linear Regression; Polynomial Regression; Data Mining; Prediction. Abstrak Durasi rawat inap pasien (Length of Stay/LoS) merupakan indikator penting dalam mengukur efisiensi pelayanan rumah sakit. Prediksi LoS yang akurat membantu rumah sakit dalam pengelolaan sumber daya, estimasi biaya, dan peningkatan mutu layanan. Penelitian ini membahas perbandingan kinerja algoritma Linear Regression dan Polynomial Regression dalam memprediksi LoS pasien. Data penelitian diperoleh dari Kaggle dengan total 835 data pasien yang melalui proses preprocessing dan transformasi. Variabel independen meliputi gender, umur, jenis penyakit, dan jenis service, sedangkan LoS menjadi variabel dependen. Metode penelitian menggunakan pendekatan Knowledge Discovery from Data (KDD) yang mencakup tahapan selection, cleaning, transformation, data mining, dan evaluation. Pengujian dilakukan pada tiga rasio pembagian data (70:30, 80:20, dan 90:10) menggunakan metrik MAE, MSE, RMSE, dan R². Hasil menunjukkan Linear Regression memiliki performa sedikit lebih unggul dengan rata-rata R² sebesar 0,18–0,20, yang dapat mendukung efisiensi manajemen rumah sakit.
Pengembangan Fitur Lanjutan Pada Aplikasi Manajemen Personal Individual Berbasis Mobile Android
The rapid development of technology and personal management challenges among Indonesian youth require an integrated solution to manage both finances and time. This study develops advanced features on the Individual Personal Management Application (AMPI) for Android using the waterfall method, clean MVVM architecture, Coin dependency injection, and Firebase integration for authentication and data storage. A user needs analysis through an online questionnaire with 50 respondents showed a high interest in an integrated application with reminders, data visualization, task prioritization, and secure data storage. The system is designed using use case, activity, sequence, and class diagrams to support account, financial, and time management. Black-box testing confirmed that all features function as designed. The results demonstrate that AMPI effectively assists users, especially young people, in tracking daily transactions, managing schedules, and monitoring productivity and financial conditions. Keywords: Personal Management Application; Android; Financial Management; Time Management; Firebase Abstrak Perkembangan teknologi dan tantangan manajemen personal pada generasi muda Indonesia menuntut solusi aplikasi yang dapat mengelola keuangan dan waktu secara terintegrasi. Penelitian ini mengembangkan fitur lanjutan pada Aplikasi Manajemen Personal Individual (AMPI) berbasis Android menggunakan metode waterfall, arsitektur MVVM clean, Koin dependen injeksi, serta integrasi Firebase untuk autentikasi dan penyimpanan data. Analisis kebutuhan pengguna melalui kuesioner daring pada 50 responden menunjukkan minat tinggi terhadap aplikasi ter-integrasi dengan fitur mengingat, visualisasi data, prioritas tugas, dan penyimpanan data aman. Sistem dirancang menggunakan use case, aktivitas, sequence, dan class diagram untuk mendukung manajemen akun, keuangan, dan waktu. Pengujian black-box menunjukkan seluruh fitur berfungsi sesuai rancangan. Hasil penelitian menunjukkan AMPI efektif membantu pengguna, khususnya generasi muda, dalam mencatat transaksi harian, mengatur jadwal, serta memantau produktivitas dan kondisi keuangan.
Implementasi Sistem Prediksi Produksi Menggunakan Kombinasi Regresi Linier Sederhana dan Single Exponential Smoothing
Inaccurate production planning can cause losses in food businesses due to overproduction or stock shortages. This study develops a production prediction model for the Moshi Mochi business by combining the Single Exponential Smoothing (SES) method and Simple Linear Regression. SES is used to forecast sales for the next period, while the resulting prediction is used as an input variable in the regression model to estimate production quantity. Model performance is evaluated using RMSE and MAPE. The results show that an alpha value of 0.4 produces an RMSE of 13.31 and a MAPE of 26.24%, which falls into the fairly good category. The predicted sales value of 39 mochi units is then used in the regression model, resulting in a production prediction of 39 units with RMSE and MAPE values of 0%. This zero error indicates that the combined SES and Simple Linear Regression model achieves 100% accuracy and falls into the very good category for historical data with low variation. The model can be effectively implemented in a web-based cashier application. Keywords: Production Prediction; Simple Linear Regression; Single Exponential Smoothing; Moshi Mochi. Abstrak Ketidaktepatan jumlah produksi dapat menyebabkan kerugian bagi usaha makanan akibat kelebihan maupun kekurangan stok. Penelitian ini mengembangkan model prediksi produksi pada usaha Moshi Mochi dengan mengombinasikan metode Single Exponential Smoothing (SES) dan Regresi Linier Sederhana. Metode SES digunakan untuk memprediksi penjualan periode berikutnya, sedangkan hasilnya dijadikan variabel input pada model regresi untuk memprediksi jumlah produksi. Evaluasi performa dilakukan menggunakan RMSE dan MAPE. Hasil menunjukkan bahwa nilai alpha = 0,4 menghasilkan RMSE sebesar 13,31 dan MAPE sebesar 26,24%, yang berada pada kategori cukup baik. Nilai prediksi penjualan sebesar 39 buah mochi kemudian digunakan dalam model regresi yang menghasilkan prediksi produksi 39 buah mochi dengan RMSE sebesar 0 dan MAPE sebesar 0%. Nilai kesalahan nol ini menunjukkan bahwa akurasi model prediksi kombinasi SES dan Regresi Linier Sederhana mencapai 100% dan termasuk dalam kategori sangat baik pada data historis dengan variasi rendah. Model ini dapat diimplementasikan secara efektif dalam aplikasi kasir berbasis web.
Pengembangan Aplikasi Pengelolaan Bank Sampah Srikandi Mobile di Gunungkidul
The environmental sector is one area that is experiencing significant transformation thanks to advances in information technology. Digitalization of waste management through web-based and mobile applications can increase efficiency, transparency and community participation in supporting more modern waste management. This research aims to develop a management application for the Srikandi Mandiri Waste Bank. The system development method used is the Waterfall model. This method is applied linearly through the stages of needs analysis, design, implementation of the Srikandi Mobile application development based on Flutter and Laravel (equipped with registration, login, transaction history, cash deposit and withdrawal features, as well as trash pickup requests), testing, and integration with a Filament-based admin dashboard. The development results show that this application is able to facilitate real-time and safe two-way communication between residents and managers. Based on the test results, all the main features of the application, namely User Authentication, Balance Collection, Waste Collection, Waste Deposit, and Transaction History, have functioned well and in accordance with the expected results. Keywords: Waste Bank; Digitalization; Flutter; Laravel; Mobile Abstrak Sektor lingkungan merupakan salah satu bidang yang mengalami transformasi signifikan berkat kemajuan teknologi informasi. Digitalisasi pengelolaan limbah melalui aplikasi berbasis web dan mobile dapat meningkatkan efisiensi, transparansi, serta partisipasi masyarakat dalam mendukung pengelolaan sampah yang lebih modern. Penelitian ini bertujuan untuk mengembangkan apllikasi pengelolaan Bank Sampah Srikandi Mandiri. Metode pengembangan sistem yang digunakan adalah model Waterfall. Metode ini diterapkan secara linier melalui tahapan analisis kebutuhan, perancangan, implementasi pembangunan aplikasi Srikandi Mobile berbasis Flutter dan Laravel (dilengkapi fitur registrasi, login, riwayat transaksi, setor dan tarik tunai, serta permintaan penjemputan sampah), pengujian, hingga integrasi dengan dashboard admin berbasis Filament. Hasil pengembangan menunjukkan bahwa aplikasi ini mampu memfasilitasi komunikasi dua arah secara real-time dan aman antara warga dan pengelola. Berdasarkan hasil pengujian, seluruh fitur utama aplikasi, yaitu Autentikasi Pengguna, Pengambilan Saldo, Pengambilan Sampah, Setor Sampah, dan Riwayat Transaksi, telah berfungsi dengan baik dan sesuai dengan hasil yang diharapkan.
Pengembangan Model Long Short-Term Memory Berbasis MediaPipe Pose untuk Klasifikasi dan Penilaian Gerakan Push-Up
The counting and assessment of push-up movements are often inaccurate and subjective because they are done manually. Existing automated approaches generally only consider the top and bottom positions. This makes the assessment incomplete, so that imperfect movements can be counted as correct. This study aims to develop a Long Short-Term Memory (LSTM) model using MediaPipe Pose landmark extraction to detect and classify push-up movements based on the overall movement pattern. Data was collected in the form of videos validated by experts and processed into 3,600 video data sets with 10 movement classes. The video data was extracted to produce normalized keypoint coordinates and joint angles, and padding was added to equalize the data length. The model was trained using the K-Fold Cross Validation method with eight different architectures. The results showed that the best model achieved an average testing accuracy of 92.35% and an F1-Score of 92.53%. These findings indicate that the combination of MediaPipe Pose and LSTM can effectively recognize and classify push-up movements. Keywords: MediaPipe Pose;Llong short-term memory; Classification; Push-up; Time series Abstrak Penghitungan dan penilaian gerakan push-up sering tidak akurat serta bersifat subjektif akibat dilakukan secara manual. Pendekatan otomatis yang ada umumnya hanya mempertimbangkan posisi atas dan bawah. Hal ini membuat penilaian tidak secara keseluruhan sehingga gerakan yang tidak sempurna dapat terhitung benar. Penelitian ini bertujuan mengembangkan model Long Short-Term Memory (LSTM) dengan memanfaatkan ektraksi landmark MediaPipe Pose untuk mendeteksi dan mengklasifikasi gerakan push-up berdasarkan keseluruhan pola gerakan. Data dikumpulkan dalam bentuk video yang divalidasi oleh ahli dan diolah hingga mejadi 3.600 data video dengan 10 kelas gerakan. Data video diektraksi hingga menghasilkan koordinat keypoint dan sudut sendi yang dinormalisasi, serta ditambahkan padding untuk menyamakan panjang data. Model dilatih dengan metode K-Fold Cross Validation dengan delapan arsitektur berbeda. Hasil penelitian menunjukkan performa model terbaik memperoleh rata-rata akurasi testing 92,35% dan F1-Score 92,53%. Temuan ini menunjukkan kombinasi MediaPipe Pose dan LSTM dapat dengan baik mengenali dan mengklasifikasi gerakan push-up.
Model Aplikasi Mobile Pusat Kebugaran Fitur QR Code Akses dan Monitoring Nutrisi
Fitness centers face operational challenges due to manual management systems, leading to inefficiency and decreased member satisfaction. This study aimed to develop a functional mobile application as a solution to these problems. The application was designed with modern features to improve efficiency and user experience, including a QR code-based access system, a nutrition monitoring feature, online class and trainer booking, and a BMI calculator for health tracking. The Waterfall method was applied, with data collected through observation and interviews. The system design used UML diagrams such as use case, activity, and sequence diagrams. The application was built using the Flutter framework for cross-platform development, Node.js for backend services, and MySQL as the database. Testing using the black-box method showed that all features worked according to specifications, including user authentication, QR code access control, trainer and class booking, BMI calculation, and daily nutrition monitoring. Keywords: Fitness center; QR code; Nutrition monitoring; BMI; Mobile application; Flutter Abstrak Pusat kebugaran menghadapi tantangan operasional akibat sistem pengelolaan manual yang menimbulkan inefisiensi dan menurunkan kepuasan anggota. Penelitian ini bertujuan mengembangkan aplikasi mobile fungsional sebagai solusi terhadap permasalahan tersebut. Aplikasi dikembangkan dengan fitur modern untuk meningkatkan efisiensi dan pengalaman pengguna, meliputi sistem akses berbasis kode QR, fitur monitoring nutrisi, pemesanan kelas dan pelatih daring, serta kalkulator BMI untuk pelacakan kesehatan. Metode pengembangan menggunakan pendekatan Waterfall dengan data diperoleh melalui observasi dan wawancara. Desain sistem memanfaatkan diagram UML seperti use case, activity, dan sequence diagram. Aplikasi dibangun menggunakan Flutter untuk pengembangan lintas platform, Node.js untuk layanan backend, serta MySQL sebagai basis data. Pengujian dilakukan dengan metode black-box untuk memastikan seluruh fitur berfungsi sesuai spesifikasi. Hasil menunjukkan bahwa seluruh fitur, termasuk autentikasi pengguna, kontrol akses QR code, pemesanan pelatih dan kelas, perhitungan BMI, serta pemantauan nutrisi harian, berjalan dengan baik.
Klasifikasi Tingkat Kematangan Buah Kelapa Sawit Menggunakan EfficientNet-B7
Determining the ripeness level of oil palm fresh fruit bunches (FFB) is a crucial factor affecting oil yield and quality; however, field assessment is still largely performed manually and is prone to subjectivity and errors. This study aims to develop an image-based classification system for oil palm fruit ripeness using a Convolutional Neural Network (CNN) with the EfficientNet-B7 architecture. The proposed method applies transfer learning and fine-tuning on the public dataset “An Ordinal Dataset for Ripeness Level Classification in Oil Palm Fruit Quality Grading,” which contains 4,728 images across five ripeness classes. The methodology includes image preprocessing, normalization, and data augmentation techniques such as rotation, flipping, and zooming. The model is trained using the Adam optimizer and evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the proposed model achieves an accuracy of 93.23% with stable performance across all classes. These findings indicate that EfficientNet-B7 is effective for oil palm fruit ripeness classification and has strong potential to be implemented as a decision-support system for more objective and consistent harvest timing. Keywords: EfficientNet-B7; Convolutional Neural Network; Ripeness classification Abstrak Penentuan tingkat kematangan tandan buah segar (TBS) kelapa sawit merupakan faktor penting yang memengaruhi rendemen dan kualitas minyak sawit, namun proses penilaiannya di lapangan masih dilakukan secara manual sehingga rentan terhadap subjektivitas dan kesalahan. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi tingkat kematangan buah kelapa sawit berbasis citra digital menggunakan algoritma Convolutional Neural Network (CNN) dengan arsitektur EfficientNet-B7. Metode yang digunakan meliputi transfer learning dan fine-tuning pada dataset publik “An Ordinal Dataset for Ripeness Level Classification in Oil Palm Fruit Quality Grading” yang terdiri dari 4.728 citra dalam lima kelas kematangan. Tahapan penelitian mencakup preprocessing citra, normalisasi, serta augmentasi data berupa rotasi, flip, dan zoom. Model dilatih menggunakan optimizer Adam dan dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model mencapai accuracy sebesar 93,23% dengan performa klasifikasi yang stabil pada seluruh kelas. Berdasarkan hasil tersebut, EfficientNet-B7 terbukti efektif untuk klasifikasi tingkat kematangan buah sawit dan berpotensi diterapkan sebagai sistem pendukung keputusan dalam penentuan waktu panen yang lebih objektif.
Evaluasi Penerimaan Pengguna Terhadap E-Commerce Shopee Melalui Pendekatan UTAUT 2
Electronic Commerce (E-commerce) has become a significant phenomenon in modern commerce, expanding the way buying and selling transactions are conducted through online platforms. To understand user acceptance of E-commerce, this study uses the UTAUT 2 (Unified Theory of Acceptance and Use of Technology 2) Method. The UTAUT 2 method explores the factors that influence user acceptance of technology by integrating variables such as ease of use, perceived benefits, social norms, and other factors. Using this framework, this study aims to analyze users' attitudes, perceptions, and intentions towards E-commerce, as well as to identify factors that influence the acceptance and use of E-commerce in the context of today's digital marketplace. Through the UTAUT 2 approach, it is expected that the results of this study can provide valuable insights for E-commerce platform developers to improve user experience and increase widespread adoption of E-commerce. Keyword: E-Commerce; UTAUT 2; User Acceptance Abstrak Electronic Commerce (E-commerce) telah menjadi fenomena yang signifikan dalam perdagangan modern, memperluas cara transaksi jual beli dilakukan melalui platform daring. Untuk memahami penerimaan pengguna terhadap E-commerce, penelitian ini menggunakan Metode UTAUT 2 (Unified Theory of Acceptance and Use of Technology 2). Metode UTAUT 2 mengeksplorasi faktor-faktor yang memengaruhi penerimaan pengguna terhadap teknologi dengan mengintegrasikan variabel-variabel seperti kemudahan penggunaan, manfaat yang dirasakan, norma sosial, dan faktor-faktor lainnya. Dengan menggunakan kerangka kerja ini, penelitian ini bertujuan untuk menganalisis sikap, persepsi, dan niat pengguna terhadap E-commerce, serta untuk mengidentifikasi faktor-faktor yang memengaruhi penerimaan dan penggunaan E-commerce dalam konteks pasar digital saat ini. Melalui pendekatan UTAUT 2, diharapkan hasil penelitian ini dapat memberikan wawasan yang berharga bagi pengembang platform E-commerce untuk meningkatkan pengalaman pengguna dan meningkatkan adopsi E-commerce secara luas.
Prediksi Keberhasilan Studi Mahasiswa Menggunakan Metode Iterative Dichotomiser 3
Student academic success is an important indicator in evaluating the quality of higher education in Indonesia. This study aims to predict student academic success based on several variables, such as second-year GPA, number of credits passed, and student's province of origin, using the Iterative Dichotomiser 3 (ID3) algorithm. The data used were 650 graduates of the Information Systems Department of the Faculty of Information Technology, Satya Wacana Christian University, from 2013 to 2017. The ID3 classification results indicate that second-year GPA is the most significant attribute in determining academic success, while province of origin also influences the likelihood of students graduating or failing. Model evaluation using a confusion matrix yielded an accuracy of 85.38%, a precision of 88.8%, a recall of 89.8%, and F-measure of 89.3. These findings demonstrate that the ID3 method can be used in predicting student academic success. Keywords: Academic success; Student; Prediction; Iterative Dichotomiser 3 Abstrak Keberhasilan studi mahasiswa merupakan indikator penting dalam mengevaluasi kualitas pendidikan perguruan tinggi di Indonesia. Penelitian ini bertujuan untuk memprediksi keberhasilan studi mahasiswa berdasarkan sejumlah variabel seperti IPK tahun kedua, jumlah SKS lulus, dan provinsi asal mahasiswa dengan algoritma Iterative Dichotomiser 3 (ID3). Data yang digunakan berasal dari lulusan Fakultas Teknologi Informasi Program Studi Sistem Informasi Universitas Kristen Satya Wacana (UKSW) tahun 2013–2017 sebanyak 650 mahasiswa. Hasil klasifikasi ID3 menunjukkan bahwa IPK tahun kedua merupakan atribut paling signifikan dalam menentukan keberhasilan studi, sedangkan provinsi asal juga berpengaruh terhadap kecenderungan kelulusan atau kegagalan mahasiswa. Evaluasi model dilakukan menggunakan confusion matrix, hasilnya menunjukkan akurasi sebesar 85.38%, precision sebesar 88.8%, recall sebesar 89.8% dan F-measure sebesar 89.3%. Temuan ini menunjukkan bahwa metode ID3 dapat digunakan dalam memprediksi keberhasilan studi mahasiswa.
Klasifikasi Tujuan Penggunaan AI Oleh Mahasiswa Dengan Algoritma K-Nearst Neighbor
Artificial intelligence (AI) technology is developing rapidly, particularly through platforms like ChatGPT and Gemini AI, which are widely used by students for various purposes. This fact highlights the importance of a more in-depth analysis of students' AI usage patterns. This study aims to classify the purposes of AI use by students using the K-Nearest Neighbor (K-NN) algorithm. The study was conducted using two classification approaches: multi-class classification and binary classification. Data were obtained from questionnaires with 305 respondents and processed using Python on the Google Colab platform using preprocessing, normalization, and data encoding stages. Test results show that the K-NN algorithm achieved a high accuracy of 77% in the binary classification scenario (Productive/Career and Entertainment/Personal), while in the multi-class classification scenario the highest accuracy only reached 34%. This finding indicates that K-NN performance is strongly influenced by the complexity of the number of classes and is more optimally applied to classifications with a limited number of classes. Keywords: K-Nearest Neighbor; AI usage classification; students; Binary Classification; Multi-class Classification. Abstrak Teknologi kecerdasan buatan ataupun Artificial Intelligence (AI) berkembang pesat, terutama melalui platform seperti ChatGPT dan Gemini AI yang banyak digunakan oleh mahasiswa untuk berbagai tujuan. Fakta ini menunjukkan pentingnya analisis yang lebih mendalam mengenai pola penggunaan AI oleh mahasiswa. Penelitian ini bertujuan untuk mengklasifikasikan tujuan penggunaan AI oleh mahasiswa memakai algoritma K-Nearest Neighbor (K-NN). Penelitian dilakukan melalui dua pendekatan klasifikasi, yaitu multi-class classification dan binary classification. Data didapatkan dari kuesioner terhadap 305 responden dan diolah memakai Python pada platform Google Colab menggunakan tahap pra-pemrosesan, normalisasi, dan pengkodean data. Hasil pengujian menunjukan algoritma K-NN mencapai akurasi tinggi yaitu 77% pada skenario klasifikasi biner (Produktif/Karir dan Hiburan/Personal), sementara pada skenario klasifikasi multi-kelas akurasi tertinggi hanya mencapai 34%. Temuan ini menandakan bahwa kinerja K-NN sangat dipengaruhi oleh kompleksitas jumlah kelas dan lebih optimal diterapkan pada klasifikasi dengan jumlah kelas yang terbatas.
Aplikasi Mobile Berbasis Geolocation untuk Monitoring Harga dan Stok Liquefied Petroleum Gas
The distribution and monitoring of Liquefied Petroleum Gas (LPG) prices in Indonesia is often hampered by limited access to accurate information on prices and stock availability at the retail level. The absence of an integrated information system makes it difficult for the public to access data transparently, which has a negative impact on the smoothness of the supply chain and the effectiveness of monitoring, especially when there are fluctuations in supply. This study aims to develop a geolocation-based mobile application to facilitate the public, shop owners, and agents in monitoring LPG prices and availability directly based on location. The system development applies the Waterfall method, which includes needs analysis, system design using use case diagrams, implementation, and functional testing. Based on the results of black box testing and user trials, the application has been proven to function well in integrating maps to display retailer locations and provide accurate data. It is concluded that this system has successfully fulfilled the research objectives and has significant potential to support information transparency and the digitization of LPG distribution management. Keyword: Geolocation; LPG; Price Monitoring; Transparency; Mobile Application Abstrak Distribusi dan pengawasan harga Liquefied Petroleum Gas (LPG) di Indonesia seringkali terkendala oleh keterbatasan informasi yang akurat mengenai harga dan ketersediaan stok di tingkat pengecer. Absennya sistem informasi terintegrasi menyulitkan masyarakat mengakses data secara transparan, yang berdampak negatif pada kelancaran rantai pasok dan efektivitas pengawasan, khususnya saat terjadi fluktuasi pasokan. Penelitian ini bertujuan mengembangkan aplikasi mobile berbasis geolocation untuk memfasilitasi masyarakat, pemilik toko, dan agen dalam memantau harga serta ketersediaan LPG secara langsung berdasarkan lokasi. Pengembangan sistem menerapkan metode Waterfall yang meliputi analisis kebutuhan, perancangan sistem menggunakan use case diagram, implementasi, hingga pengujian fungsional. Berdasarkan hasil pengujian black box dan uji coba pengguna, aplikasi terbukti berfungsi baik dalam mengintegrasikan peta untuk menampilkan lokasi pengecer serta menyediakan data akurat. Disimpulkan bahwa sistem ini berhasil memenuhi tujuan penelitian dan berpotensi signifikan mendukung keterbukaan informasi serta digitalisasi tata kelola distribusi LPG.
Peningkatan Akurasi Deteksi Penyakit Malaria Menggunakan Transfer Learning pada Arsitektur CNN
Malaria is a disease that requires fast and accurate diagnosis. This study compares three CNN architectures VGG16, ResNet50, and InceptionV3 for malaria peripheral blood smear classification using the public NIH dataset. The pipeline includes standardized preprocessing, moderate augmentation, and transfer learning with early stopping (monitoring val_recall). Evaluation on a stratified test set covers accuracy, precision, recall, F1, ROC-AUC, PR-AP, confusion matrix, and paired statistics (McNemar). VGG16 yields the best performance at the 0.50 threshold (AUC 0.9833; AP 0.9846; Recall 0.8955; F1 0.9308) and significantly outperforms InceptionV3 (X2(1)=111.06; p<1x110-24). Bootstrap uncertainty (1.000 resamples) gives Recall mean 0.8956 (95% CI 0.8843–0.9077) and F1 mean 0.9309 (95% CI 0.9239–0.9377). Findings support a VGG16-based model as a feasible pre-screening module in resource-constrained settings, emphasizing sensitivity to reduce false negatives. Keywords: Malaria; Image classification; VGG16; RO-AUC; McNemar Abstrak Malaria merupakan penyakit yang memerlukan diagnosis cepat dan akurat. Penelitian ini membandingkan tiga arsitektur CNN VGG16, ResNet50, dan InceptionV3 untuk klasifikasi citra apusan darah tepi malaria berbasis dataset publik NIH. Pipeline meliputi praproses terstandar, augmentasi moderat, dan transfer learning dengan early stopping (monitor val_recall). Evaluasi dilakukan pada himpunan uji terstratifikasi mengukur akurasi, presisi, recall, F1, ROC-AUC, PR-AP, confusion matrix, serta uji statistik berpasangan (McNemar). VGG16 menunjukkan kinerja terbaik pada ambang 0.50 (AUC 0.9833; AP 0.9846; Recall 0.8955; F1 0.9308) dan unggul signifikan atas InceptionV3(X2(1)=111.06; p<1x110-24). Estimasi ketidakpastian berbasis bootstrap (1.000 ulangan) menghasilkan Recall mean 0.8956 (CI95% 0.8843–0.9077) dan F1 mean 0.9309 (CI95% 0.9239–0.9377). Temuan ini mendukung model berbasis VGG16 sebagai modul pra-skrining otomatis di lingkungan berdaya komputasi terbatas, dengan penekanan pada sensitivitas untuk meminimalkan salah-negatif.
Rancang Bangun Aplikasi Marketplace Properti Berbasis Web dan Mobile
Property search and management at PT. Graha Medina Cipta faced challenges from inefficient conventional processes that hindered marketing. To address this problem, a property marketplace application based on Android, supported by a web admin system, was designed and built. The research method used was an iterative and incremental approach, involving needs analysis, system design (UI/UX, database, API), frontend implementation with Flutter (for the mobile app) and React.js/HTML (for the web admin), and backend implementation with Node.js and MySQL. The research resulted in a functional prototype where the frontend application was successfully built with a modern and responsive user interface. Authentication functionality (login and registration) was successfully integrated with a real database, and the web admin panel was developed to support data management. Keywords: Property Marketplace; Flutter; Node.js; MySQL; Web Admin Abstrak Pencarian dan manajemen properti di PT. Graha Medina Cipta menghadapi tantangan proses konvensional yang tidak efisien dan menghambat pemasaran. Untuk mengatasi permasalahan ini, dirancang dan dibangun sebuah aplikasi marketplace properti berbasis Android yang didukung oleh sistem web admin. Metode penelitian yang digunakan adalah pendekatan iteratif dan inkremental, yang melibatkan tahapan analisis kebutuhan, perancangan sistem (UI/UX, database, API), implementasi frontend dengan Flutter (untuk aplikasi mobile) dan React.js/HTML (untuk web admin), serta implementasi backend dengan Node.js serta MySQL. Hasil penelitian menunjukkan sebuah prototipe fungsional di mana aplikasi frontend telah berhasil dibangun dengan antarmuka pengguna yang modern dan responsif. Fungsionalitas autentikasi (login dan registrasi) telah berhasil diintegrasikan dengan database nyata, serta web admin panel telah dikembangkan untuk mendukung manajemen data.
Analisis Pembangunan Knowledge Management System Pada Unit Kerja Asdep Dumas Kemensetneg RI Dengan Framework COBIT 5.0
The very rapid development of information technology in the era of disruption has changed various work processes, including the management of public complaints at the Ministry of State Secretariat of the Republic of Indonesia, the number of which continues to increase every year. To increase the speed and accuracy of analysis, the Assistant Deputy for Public Complaints developed a Knowledge Management System as a means of accessing and managing case references. This research evaluates the suitability of system development to organizational needs and assesses the maturity level of IT processes using a COBIT 5-based audit. The approach used is a mixed method through literature study, distributing questionnaires to 15 Dumas analysts, and interviews with relevant officials. The research results show that the maturity level is at level 4 (managed), while the organization's target is level 5. The gap value found in EDM02 was 0.9, EDM04 was 0, APO04 was 0.67, and APO12 was 0.89, indicating the need for process improvement to achieve the expected maturity level. Keywords: Cobit 5.0; Knowledge Management System; Maturity level; Dumas Abstrak Perkembangan teknologi informasi yang sangat pesat pada era disrupsi telah mengubah berbagai proses kerja, termasuk pengelolaan pengaduan masyarakat di Kementerian Sekretariat Negara RI yang jumlahnya terus meningkat setiap tahun. Untuk meningkatkan kecepatan dan ketepatan analisis, Asisten Deputi Pengaduan Masyarakat mengembangkan Knowledge Management System sebagai sarana akses dan pengelolaan referensi kasus. Penelitian ini mengevaluasi kesesuaian pengembangan sistem dengan kebutuhan organisasi serta menilai tingkat kematangan proses TI menggunakan audit berbasis COBIT 5. Pendekatan yang digunakan merupakan metode campuran melalui studi pustaka, penyebaran kuesioner kepada 15 analis dumas, dan wawancara dengan pejabat terkait. Hasil penelitian menunjukkan tingkat kematangan berada pada level 4 (managed), sementara target organisasi adalah level 5. Nilai kesenjangan yang ditemukan EDM02 sebesar 0,9, EDM04 sebesar 0, APO04 sebesar 0,67, dan APO12 sebesar 0,89 mengindikasikan perlunya peningkatan proses untuk mencapai tingkat kematangan yang diharapkan.
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Pengembangan Sistem Informasi Manajemen Kepegawaian Berbasis Web dan Mobile pada PT Anugrah Salman Medika
This study is motivated by the fragmented implementation of employee administrative processes, such as attendance, leave submission, and payroll management, which leads to low efficiency and potential data inconsistency. This study focuses on the development of an integrated web and mobile based employee information system to improve the effectiveness of personnel data management. The Waterfall model is applied in the system development process, consisting of requirements analysis, system design, implementation, and testing as the application development stages. The developed system integrates geolocation-based attendance, digital leave submission and approval, and digital payroll slips in PDF format. System testing was conducted using a black box approach to examine whether system functions align with user requirements. The testing results indicate that the main system features operate properly and have the potential to improve data management efficiency, information accuracy, and administrative transparency within the organization. Keywords: Management information system; Location-based attendance; Employee leave; Digital payroll slip; Web and mobile application Abstrak Penelitian ini dilatarbelakangi oleh masih terpisahnya proses administrasi kepegawaian, seperti absensi, pengajuan cuti, dan penggajian, yang menyebabkan rendahnya efisiensi dan potensi ketidaksesuaian data. Fokus penelitian ini adalah pengembangan sistem informasi kepegawaian terintegrasi berbasis web dan mobile sebagai upaya meningkatkan efektivitas pengelolaan data kepegawaian. Model Waterfall digunakan dalam pengembangan sistem dengan tahapan analisis kebutuhan, perancangan sistem, implementasi, dan pengujian sebagai alur pengembangan aplikasi. Sistem yang dikembangkan mengintegrasikan fitur absensi berbasis geolocation, pengajuan dan persetujuan cuti secara digital, serta penyajian slip gaji digital dalam format PDF. Pengujian terhadap sistem dilakukan dengan pendekatan black box guna memeriksa kesesuaian fungsi sistem terhadap kebutuhan pengguna. Berdasarkan hasil pengujian tersebut, fitur-fitur utama pada sistem dapat berjalan dengan baik dan berpotensi memberikan peningkatan pada efisiensi pengelolaan data, ketepatan informasi, serta keterbukaan proses administrasi kepegawaian di perusahaan.
Perbandingan Algoritma Machine Learning untuk Prediksi Potensi Tsunami di Pesisir Barat Lampung
Indonesia has a high level of vulnerability to tsunami disasters due to seismic activity in subduction zones. This condition requires a fast and accurate tsunami potential prediction system to support effective disaster mitigation. This study aims to compare the performance of five machine learning algorithms in predicting tsunami potential along the West Coast of Lampung, namely Random Forest, K-Nearest Neighbor, Naïve Bayes, Neural Network, and Decision Tree. The dataset consists of 352 earthquake records from 2023–2024 obtained from BMKG, using parameters such as magnitude, depth, and epicentral distance. Model evaluation was conducted using a Confusion Matrix with performance metrics including accuracy, precision, recall, and F1-score. The results indicate that the Random Forest algorithm achieved the best performance with an accuracy of 100% and balanced precision, recall, and F1-score values. These findings are expected to support the development of a machine learning-based tsunami early warning system that is adaptive to local geophysical characteristics. Keywords: Earthquake; Machine Learning; Random Forest; Tsunami prediction Abstrak Indonesia memiliki tingkat kerentanan yang tinggi terhadap bencana tsunami akibat aktivitas gempa bumi di zona subduksi. Kondisi ini menuntut tersedianya sistem prediksi potensi tsunami yang cepat dan akurat untuk mendukung mitigasi bencana. Penelitian ini bertujuan membandingkan kinerja lima algoritma machine learning dalam memprediksi potensi tsunami di Pesisir Barat Lampung, yaitu Random Forest, K-Nearest Neighbor, Naïve Bayes, Neural Network, dan Decision Tree. Data yang digunakan berupa 352 catatan gempa bumi periode 2023–2024 yang bersumber dari BMKG dengan parameter magnitudo, kedalaman, dan jarak episentrum. Evaluasi model dilakukan menggunakan Confusion Matrix dengan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Random Forest memberikan performa terbaik dengan tingkat akurasi 100% serta keseimbangan nilai presisi, recall, dan F1-score. Temuan ini diharapkan dapat mendukung pengembangan sistem peringatan dini tsunami berbasis machine learning yang adaptif terhadap karakteristik geofisika lokal.