Progresif: Jurnal Ilmiah Komputer
type: channel
description: e-ISSN: 2685-0877
publisher: STMIK Banjarbaru
language: en-US
publication name: Progresif: Jurnal Ilmiah Komputer
issn: 0216-3284
items: Social Network and Sentiment Analysis for Social CRM Optimalization on Indonesian Digital Recruitment Platform
items: Penerapan Algoritma Decision Tree Dalam Deteksi Fraud Transaksi Kartu Kredit
items: Utilizing Explainable AI for Interpreting Machine Learning Model Results in Ceria Credit Scoring
items: Perbandingan Algoritma K-Nearest Neighbor dan Naive Bayes untuk Klasifikasi FoMO Pengguna Media Sosial
items: Meningkatkan Efisiensi Prediksi Risiko Diabetes Melitus dengan Metode Fuzzy Decision Tree
items: Implementasi Convolutional Neural Network Dengan MobileNetV2 Untuk Deteksi Tokoh Wayang Golek Berdasarkan Citra Digital
items: Audit Tata Kelola TI Talent Acquisition KB Bank Dengan COBIT 2019
items: Model Klasifikasi Mental Siswa Menggunakan Algoritma Support Vector Machine
items: Penerapan K-Means Clustering dalam Segmentasi Siswa Berdasarkan Status Sosial Ekonomi
items: Dashboard Prestasi Siswa SMPN 1 Purwakarta: Visualisasi Data dengan Google Looker Studio
items: Model Penilaian Kinerja Tenaga Kontrak Dinas Perdagangan Perindustrian Koperasi dan UKM Berbasis Algoritma Weighted Product
items: Deteksi Skintone Menggunakan Model Warna CIELAB dan Klasifikasi Support Vector Machine
items: Pendekatan Metode UCD dan SUS dalam Redesign UI/UX Aplikasi Mobile iPusnas
items: Analisis Perbandingan Metode Yolo Dan Faster R-CNN Dalam Deteksi Objek Manusia
items: Perbandingan Algoritma NBC dan SVM dalam Analisis Sentimen Terhadap Dampak Kesehatan Rokok Elektrik
items: Evaluation of Information Security Readiness Using the KAMI Index 5.0 and ISO/IEC 27001:2022
items: Evaluasi Pengalaman Pengguna Spotify dan YouTube Music Menggunakan Metode User Experience Questionnaire
items: Penerapan K-Means Clustering Untuk Mengelompokkan Tingkat Konsumsi Listrik Menurut Provinsi di Indonesia
items: Sistem Pendukung Keputusan Kontrak Kerja Karyawan Probation Menggunakan Metode Fuzzy Simple Additive Weighting
items: Klasifikasi Kelayakan Ban Sepeda Motor Menggunakan Metode Convolutional Neural Network
items: Penerapan Metode User-Centered Design dalam Redesign UI/UX Aplikasi Mobile Transjakarta
items: Model sistem Pakar Pendiagnosis Penyakit Pada Sapi Dengan Metode Certainty Factor
items: Eksplorasi Sentimen Publik di Media Sosial terhadap Isu RUU TNI Menggunakan Pendekatan Machine Learning
items: Penerapan CNN Pada Klasifikasi Kepribadian Anak Sekolah Dasar Berdasarkan Citra Tulisan Tangan
items: ESP32-CAM Camera Integration and Android Control for Portable Real-Time Surveillance Solution
items: Aplikasi Pendataan Kesehatan Anak Pada Posyandu Anggur Satu Berbasis Web Menggunakan Metode Agile
items: Perbandingan Algoritma K-Means dan DBSCAN dalam Pengelompokkan Data Penjualan Elektronik
items: Evaluasi Kualitas Sistem Informasi Kepegawaian di BKPSDM Kota Salatiga Berdasarkan ISO/IEC 25010
items: Efektivitas Game RPG Maker MV dalam Edukasi Lingkungan pada Siswa Sekolah Dasar
items: Analisis Komparatif Unjuk Kerja Model Vision Transformers Dengan ConvNeXt Dalam Rekognisi Citra Warangka Keris Bali
items: Audit Aplikasi SRIKANDI Menggunakan Framework COBIT 2019 Menggunakan Domain MEA
items: Penerapan Metode Support Vector Machine Untuk Memprediksi Kelulusan Tepat Waktu
items: Sistem Pengenalan Wajah Dengan Metode Template Matching Berdasarkan Struktur Wajah Dan Kornea Mata
items: Penerapan Metode FIFO Untuk Mengelola Persediaan Bahan Baku Pada PT MPM
items: Analisis Optimalisasi Meminimumkan Biaya Operasional Menggunakan Program Linier Melalui Metode Simpleks (Studi Kasus pada Pabrik Tahu Dan Tempe)
items: Aplikasi Web Pencatatan Keuangan UMKM Dengan Pendekatan Agile Pada Toko Perlengkapan Bayi
items: Penerapan Fuzzy Logic pada Sistem Controlling Suhu dan Kelembapan Solar Dryer Berbasis Internet of Things
items: Deteksi Keaslian Uang Rupiah Menggunakan Metode Canny Edge Detection dan K-Mean Clustering
items: Pengembangan Sistem ERP untuk Distribusi Produk PT Solomon Indo Global: Modul Shipping, Inventory, dan User Management
items: Klasifikasi Body Mass Index Berbasis Estimasi Dimensi Tubuh melalui Pengolahan Citra Digital
items: Analisis Sentimen terhadap Dedy Mulyadi Berdasarkan Komentar Youtube Menggunakan Metode Naïve Bayes
items: Menelusuri Narasi Imunologi Melalui Eksplorasi Komik Augmented Reality Untuk Pendidikan Sekolah Dasar
items: Evaluasi Kelayakan Platform Ucapan Digital dengan Metode Heuristic Evaluation
items: Sistem Rekomendasi Kuliner Karanganyar Menggunakan Metode Hybrid Recommendation
items: Sistem Informasi Absensi Guru Dengan Face Recognition Berbasis Web Menggunakan Metode Lock GPS
items: Prediksi Keberhasilan Menindaklanjuti Pelanggan pada Dealer Mobil dengan Komparasi Algoritma Random Forest dan XGBoost
items: Pengukuran Penerimaan Aplikasi BRImo Melalui Technology Acceptance Model di Kecamatan Banyubiru
items: Prediksi Risiko Kredit Nasabah Menggunakan Algoritma Data Mining: Studi Kasus pada PT Toyota Astra Finance
items: Perancangan UI/UX Sistem Administrasi Surat Instansi Pemerintah Daerah Menggunakan Metode Design Thinking

Social Network and Sentiment Analysis for Social CRM Optimalization on Indonesian Digital Recruitment Platform
type: item
description: The rapid growth of digital recruitment platforms in Indonesia has generated a large volume of user content on social media, serving as a vital data source for Social Customer Relationship Management (Social CRM) strategies. Consequently, the strategic insights that can be drawn may be limited. This study applied an integrated analytical approach combining Social Network Analysis (SNA) and lexicon-based sentiment analysis to evaluate public interactions regarding Jobstreet, Glints, and Dealls. The research methodology involved collecting data from platform X (previously known as Twitter) during the period of April 1-30, 2025, which was then analyzed using SNA with Gephi to identify influential actors through centrality metrics, alongside sentiment analysis to measure emotional polarity. The main findings revealed that Jobstreet possessed the healthiest conversational ecosystem, characterized by positive and neutral sentiment from its central actors. Glints exhibited sentiment polarization, and Dealls showed reputational vulnerability due to dominant negative sentiment from its influential users. It was concluded that the integration of these two methods provides a robust framework for designing more responsive and data-driven Social CRM strategies. Keywords: Social Network Analysis; Sentiment Analysis; Social CRM; Digital Recruitment; Lexicon-Based Features. Abstrak Perkembangan pesat platform rekrutmen digital di Indonesia telah menghasilkan volume besar konten pengguna di media sosial, yang menjadi sumber data vital untuk strategi Social Customer Relationship Management (Social CRM). Sehingga hal ini dapat menyebabkan insight strategis yang bisa diambil menjadi terbatas. Penelitian ini menerapkan pendekatan analitis terpadu yang menggabungkan Social Network Analysis (SNA) dan analisis sentimen berbasis leksikon untuk mengevaluasi interaksi publik mengenai Jobstreet, Glints, dan Dealls. Metodologi penelitian melibatkan pengumpulan data dari platform X (sebelumnya d…
creator: Seibah Humayyah
creator: Johannes Hamonangan Siregar
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-03
publication date: 2025-08-03
volume: 21
number: 2
starting page: 388
ending page: 400
doi: 10.35889/progresif.v21i2.2871

Penerapan Algoritma Decision Tree Dalam Deteksi Fraud Transaksi Kartu Kredit
type: item
description: Credit card fraud poses a serious threat in digital financial systems. Manual detection of suspicious transactions has become ineffective due to detecting fraudulent transactions using the Decision Tree algorithm. The dataset used was obtained from Kaggle and underwent preprocessing and attribute selection. The model was tested under four data split scenarios: 90:10, 80:20, 70:30, and 60:40. Performance evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The results show that the 60:40 data split yielded the best performance, with an accuracy of 97,47%, precision of 86,34%, and recall of 78,67%. These findings indicate that the Decision Tree algorithm can produce highly accurate classification results even without applying data balancing techniques. Kata kunci: Credit Card; Fraud Detection; Decision Tree; Data Mining. Abstrak Penipuan dalam transaksi kartu kredit merupakan ancaman serius dalam sistem keuangan digital. Deteksi secara manual terhadap transaksi yang mencurigakan menjadi tidak efektif seiring dengan meningkatnya volume data. Penelitian ini bertujuan untuk mengembangkan model klasifikasi untuk mendeteksi transaksi fraud menggunakan algoritma Decision Tree C4.5. Dataset yang digunakan diperoleh dari Kaggle dan telah melalui proses praproses dan seleksi atribut. Pengujian dilakukan dengan empat skenario pembagian data training dan data testing, yaitu 90:10, 80:20, 70:30, dan 60:40. Evaluasi performa dilakukan menggunakan confusion matrix dengan metrik akurasi, presisi, dan recall. Hasil menunjukkan bahwa pembagian data 60:40 memberikan performa terbaik dengan nilai akurasi sebesar 97,47%, presisi 86,34%, dan recall 78,67%. Model ini menunjukkan bahwa algoritma Decision Tree mampu memberikan hasil klasifikasi yang sangat baik bahkan tanpa teknik penyeimbangan data. Kata kunci: Kartu Kredit; Deteksi Penipuan; Decision Tree; Data Mining
creator: Gladisya Devina Agustine
creator: Irwansyah Irwansyah
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-03
publication date: 2025-08-03
volume: 21
number: 2
starting page: 401
ending page: 410
doi: 10.35889/progresif.v21i2.2751

Utilizing Explainable AI for Interpreting Machine Learning Model Results in Ceria Credit Scoring
type: item
description: This study aims to improve the transparency of machine learning models in credit scoring using various Explainable Artificial Intelligence (XAI) methods. The methods used include SHAP, BRCG, ALE, Anchor, and ProtoDash to explain the prediction results of machine learning models, namely logistic regression, XGBoost, and random forest. This study applies a quantitative approach with a comparative method, where Ceria loan application data from Bank Rakyat Indonesia (BRI) is analyzed using a machine learning model, then evaluated using the Explanation Consistency Framework (ECF). The results show that the XAI method can improve understanding of model decisions, with SHAP and ALE effective for global explanations, while Anchor and ProtoDash provide in-depth insights at the individual level. Evaluation using ECF shows that the post-hoc method has high consistency, although Anchor has limitations in the aspect of axiom identity. In conclusion, the XAI method can help improve trust and transparency in credit scoring at BRI. Keywords: Explainable Artificial Intelligence; Credit Scoring; Machine Learning; Model Interpretability; Explanation Consistency Framework Abstrak Penelitian ini bertujuan untuk meningkatkan transparansi model pembelajaran mesin dalam penilaian kredit menggunakan berbagai metode Explainable Artificial Intelligence (XAI). Metode yang digunakan antara lain SHAP, BRCG, ALE, Anchor, dan ProtoDash untuk menjelaskan hasil prediksi model pembelajaran mesin yaitu regresi logistik, XGBoost, dan random forest. Penelitian ini menggunakan pendekatan kuantitatif dengan metode komparatif, dimana data pengajuan pinjaman Ceria dari Bank Rakyat Indonesia (BRI) dianalisis menggunakan model machine learning, kemudian dievaluasi menggunakan Explanation Consistency Framework (ECF). Hasilnya menunjukkan bahwa metode XAI dapat meningkatkan pemahaman keputusan model, dengan SHAP dan ALE efektif untuk penjelasan global, sementara Anchor dan ProtoDash memberikan wawasan mendalam …
creator: Roni Eka Setiawan
creator: Toni Wijanarko Adi Putra
creator: Budi Hartono
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-03
publication date: 2025-08-03
volume: 21
number: 2
starting page: 411
ending page: 421
doi: 10.35889/progresif.v21i2.2769

Perbandingan Algoritma K-Nearest Neighbor dan Naive Bayes untuk Klasifikasi FoMO Pengguna Media Sosial
type: item
description: The intensive use of social media among students poses a risk of triggering Fear of Missing Out (FoMO), which negatively affects mental health and learning focus. This study aims to develop a classification model to detect FoMO tendencies among students at SMAN 11 Kabupaten Tangerang. A quantitative approach was used, employing the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms. The analyzed variables include gender, duration of social media use, access frequency, desire to stay updated, and its impact on productivity. Data were collected from 244 respondents and processed through pre-processing, modeling, and evaluation stages. Validation results show that KNN achieved the highest accuracy at 94.69%, while Naïve Bayes reached 93.06%. These findings indicate that KNN is more effective in detecting FoMO tendencies based on numerical data and has the potential to support early intervention efforts in educational settings. Keywords: Fear of Missing Out; K-Nearest Neighbor; Social Media; Classification; Naive Bayes Abstrak Penggunaan media sosial secara intensif di kalangan pelajar berisiko memunculkan gejala Fear ofaMissing Out (FoMO), yang berdampak negatif pada kesehatan mental dan fokus belajar. Penelitian ini bertujuan untuk mengembangkan model klasifikasi kecenderungan FoMO pada pelajar SMAN 11 Kabupaten Tangerang. Metode yang digunakan adalah pendekatan kuantitatif dengan algoritma K-NearestiNeighbor (KNN) dan NaïveiBayes. Variabel yang dianalisis meliputi jenis kelamin, durasi penggunaan media sosial, frekuensi akses, keinginan untuk tetap update, dan pengaruh terhadap produktivitas. Data dikumpulkan dari 244 responden dan diproses melalui pre-processing, modeling, dan evaluasi. Hasil validasi menunjukkan bahwa KNN menghasilkan akurasi tertinggi sebesar 94,69%, sementara Naïve Bayes mencapai 93,06%. Temuan ini menunjukkan bahwa KNN lebih efektif untuk mendeteksi kecenderungan FoMO berbasis data numerik dan berpotensi mendukung pengembangan intervensi dini d…
creator: Muhammad Haromaen
creator: Marthin Piskana
creator: M. Bucci Ryando
creator: Wira Hadinata
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-03
publication date: 2025-08-03
volume: 21
number: 2
starting page: 422
ending page: 433
doi: 10.35889/progresif.v21i2.2784

Meningkatkan Efisiensi Prediksi Risiko Diabetes Melitus dengan Metode Fuzzy Decision Tree
type: item
description: The main problem in Fuzzy Logic (FL)-based prediction models is that the number of rules increases as the data dimension increases, reducing the efficiency of the system in interpretation and prediction. This study aims to unravel the complexity and improve the accuracy value of DM predictions using the Fuzzy Decision Tree (FDT) method based on Iterative Dichotomiser 3 (ID3). The research data was obtained from the National Institute of Diabetes and Digestive and Kidney Diseases with the parameters of glucose, BMI, HDL, and systolic blood pressure. The process includes data fuzzification, the formation of a decision tree with ID3, and the application of two thresholds, namely FCT and LDT. The results showed that the FDT model succeeded in reducing the number of rules by 25%, from 81 rules to 60 rules. The application of ID3-based FDT succeeded in increasing the accuracy value of DM predictions by 80%. The conclusion of the study states that the FDT model is able to unravel the complexity of the prediction model by using a simpler number of rules and can maintain and increase the accuracy value of the DM prediction model. Keywords: Diabetes Melitus; Fuzzy Decision Tree; Fuzzy Logic; Fuzzy Sugeno; Risk Prediction Abstrak Permasalahan utama dalam model prediksi berbasis Fuzzy Logic (FL) adalah meningkatnya jumlah aturan seiring bertambahnya dimensi data, mengurangi efisiensi sistem dalam interpretasi dan prediksi. Penelitian ini bertujuan mengurai kompleksitas dan meningkatkan nilai akurasi prediksi DM menggunakan metode Fuzzy Decision Tree (FDT) berbasis Iterative Dichotomiser 3 (ID3). Data penelitian diperoleh dari National Institute of Diabetes and Digestive and Kidney Diseases dengan parameter glukosa, BMI, HDL, dan tekanan darah sistolik. Proses meliputi fuzzifikasi data, pembentukan pohon keputusan dengan ID3, serta penerapan dua threshold, yaitu FCT dan LDT. Hasil penelitian menunjukkan bahwa model FDT berhasil mengurangi jumlah aturan sebesar 25%, dari 81 atura…
creator: Deddy Kurniawan
creator: Wahyu Yanuarta
creator: Tina Tri Wualansari
creator: Niken Ayu Dwi Febrianti
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-03
publication date: 2025-08-03
volume: 21
number: 2
starting page: 434
ending page: 442
doi: 10.35889/progresif.v21i2.2680

Implementasi Convolutional Neural Network Dengan MobileNetV2 Untuk Deteksi Tokoh Wayang Golek Berdasarkan Citra Digital
type: item
description: Wayang golek is a traditional Pasundan regional performing art played using wooden puppets by a puppeteer, which was recognized by UNESCO on November 7, 2003 as an intangible cultural heritage. However, many people find it difficult to distinguish the characters of wayang golek because of the diversity of the characters. This research aims to implement CNN in developing an image-based golek puppet character identification system, so that the recognition process is carried out quickly and accurately. This research uses 15 golek puppet characters with MobileNetV2 architecture as a feature extractor. The model produces train accuracy of 95% and validation accuracy of 91%. Evaluation results using confusion matrix showed accuracy of 90%, precision 90.47%, recall 90%, and f1-score 89.93%. The results show that the CNN model with MobileNetV2 architecture is effective and optimal in detecting and classifying puppets, so that it can support the preservation of puppet culture through technology. Keyword: Wayang Golek; Convolutional Neural Network; Computer Vision; Image Processing; Website Abstrak Wayang golek adalah seni pertunjukan tradisional daerah Pasundan yang dimainkan menggunakan boneka kayu oleh seorang dalang, yang telah diakui UNESCO pada 7 November 2003 sebagai warisan budaya tak benda. Meskipun demikian, tidak sedikit orang kesulitan dalam membedakan tokoh-tokoh wayang golek karena keberagaman tokohnya. Penelitian ini bertujuan untuk mengimplementasikan CNN dalam mengembangkan sistem identifikasi tokoh wayang golek berdasarkan citra, sehingga proses pengenalan dilakukan secara cepat dan akurat. Penelitian ini menggunakan 15 tokoh wayang golek dengan arsitektur MobileNetV2 sebagai feature extractor. Model menghasilkan train accuracy sebesar 95% dan validation accuracy sebesar 91%. Hasil evaluasi menggunakan confusion matrix menunjukkan akurasi sebesar 90%, precision 90,47%, recall 90%, dan f1-score 89,93%. Hasil penelitian menunjukkan bahwa model CNN dengan arsit…
creator: Siti Nurazizah
creator: Asriyanik Asriyanik
creator: Fathia Frazna Az-Zahra
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-04
publication date: 2025-08-04
volume: 21
number: 2
starting page: 443
ending page: 454
doi: 10.35889/progresif.v21i2.2782

Audit Tata Kelola TI Talent Acquisition KB Bank Dengan COBIT 2019
type: item
description: This study analyzes the IT governance audit of KB Bank’s Talent Acquisition team using the COBIT 2019 framework, supported by SWOT and RACI-Chart analyses to evaluate the effectiveness and efficiency of IT systems in the recruitment process. The findings reveal several key challenges, including limited storage capacity, unscheduled internal audits, inconsistent data entry, dependency on HRIS vendors, and the absence of an Applicant Tracking System (ATS) to optimize candidate management. These challenges hinder operational efficiency, increase the risk of input errors, and slow down the selection process. To address these issues, the study recommends implementing an ATS to enhance automation and data accuracy, scheduling regular audits to ensure optimal IT governance, and expanding the capacity and security of cloud-based storage to improve system integration and accessibility. By adopting a COBIT 2019-based strategy, these measures are expected to enhance IT governance efficiency, support recruitment effectiveness, and strengthen KB Bank’s competitiveness in the industry. Keywords: IT Governance; COBIT 2019; SWOT; RACI-Chart; IT Audit Abstrak Penelitian ini menganalisis audit tata kelola teknologi informasi pada tim Talent Acquisition KB Bank menggunakan kerangka kerja COBIT 2019, dengan dukungan analisis SWOT dan RACI-Chart untuk menilai efektivitas dan efisiensi sistem TI pada proses rekrutmen. Hasil penelitian mengidentifikasi berbagai kendala utama, seperti keterbatasan kapasitas penyimpanan, audit internal yang tidak terjadwal dengan baik, pengisian data yang belum konsisten, ketergantungan pada vendor HRIS, serta tidak menggunakan sistem Applicant Tracking System (ATS) yang dapat mengoptimalkan pengelolaan kandidat. Kendala ini menghambat efisiensi operasional, meningkatkan potensi kesalahan input, serta memperlambat proses seleksi. Untuk mengatasinya, penelitian merekomendasikan implementasi ATS guna meningkatkan otomatisasi dan akurasi pengolahan data, penja…
creator: Laurentius Widi Kristiawan
creator: Christ Rudianto
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-04
publication date: 2025-08-04
volume: 21
number: 2
starting page: 455
ending page: 471
doi: 10.35889/progresif.v21i2.2720

Model Klasifikasi Mental Siswa Menggunakan Algoritma Support Vector Machine
type: item
description: Student mental health plays a vital role in academic performance and social well-being. This study aims to build a classification model using the Support Vector Machine (SVM) algorithm, based on 15 features covering demographic, academic, and behavioral aspects. The dataset, obtained from Kaggle, contains 426 records of junior and senior high school students. Key preprocessing steps include one-hot encoding, feature standardization, train-test splitting (80:20), and handling class imbalance with SMOTE. The model was trained using the Radial Basis Function (RBF) kernel and optimized using Grid Search CV to find the best parameters. Evaluation results show 65% accuracy, with better performance in predicting students without mental health issues (Absence). However, low recall for the Presence class indicates a need for improved strategies to handle data imbalance. This study highlights the potential of machine learning, particularly SVM, as a tool for early mental health detection in students, provided that effective data preprocessing is applied. Keywords: Student mental health; classification; Support Vector Machine; SMOTE; machine learning Abstrak Kesehatan mental siswa berpengaruh besar terhadap prestasi akademik dan kesejahteraan sosial. Penelitian ini bertujuan membangun model klasifikasi kondisi mental siswa menggunakan algoritma Support Vector Machine (SVM) berbasis 15 fitur demografis, akademik, dan perilaku. Dataset yang digunakan berasal dari Kaggle, terdiri atas 426 data siswa SMP dan SMA. Tahapan penelitian meliputi preprocessing dengan one-hot encoding, standarisasi numerik, pembagian data (80:20), serta penanganan ketidakseimbangan data menggunakan SMOTE. Model dilatih menggunakan kernel Radial Basis Function (RBF) dan dioptimasi dengan Grid Search CV. Hasil evaluasi menunjukkan akurasi sebesar 65%, dengan kinerja lebih baik dalam mengenali siswa tanpa gangguan mental (absence) dibandingkan siswa dengan gangguan mental (presence). Rendahnya recall pada k…
creator: Charitas Fibriani
creator: Dian Novita Kristiyani
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-04
publication date: 2025-08-04
volume: 21
number: 2
starting page: 472
ending page: 482
doi: 10.35889/progresif.v21i2.2813

Penerapan K-Means Clustering dalam Segmentasi Siswa Berdasarkan Status Sosial Ekonomi
type: item
description: The accuracy of educational aid distribution remains a challenge, especially when it is not based on structured socioeconomic data. This study aims to group students at SMP Negeri 1 Lunang based on socioeconomic status using the K-Means Clustering algorithm as a segmentation approach. The data used includes parents' income and occupation, number of dependents, social assistance, certificates of poverty, and distance from home to school. After data normalization, clustering and visualization were performed using Principal Component Analysis (PCA). The clustering results yielded three main groups representing different socioeconomic levels: low, medium, and high. Validation using the Silhouette Score yielded a value of 0.2592, indicating that the cluster separation was adequate. These findings suggest that K-Means can serve as a decision-making tool for data-driven aid distribution. This study offers a new approach to student segmentation that simultaneously considers geographical and socioeconomic indicators. Keywords: K-Means; Socioeconomic status; Student segmentation; PCA; Silhouette score Abstrak Ketepatan penyaluran bantuan pendidikan masih menjadi tantangan, terutama ketika tidak berbasis pada data sosial ekonomi yang terstruktur. Penelitian ini bertujuan untuk mengelompokkan siswa SMP Negeri 1 Lunang berdasarkan status sosial ekonomi menggunakan algoritma K-Means Clustering sebagai pendekatan segmentasi. Data yang digunakan mencakup penghasilan dan pekerjaan orang tua, jumlah tanggungan, bantuan sosial, surat keterangan tidak mampu, dan jarak rumah ke sekolah. Setelah data dinormalisasi, dilakukan klasterisasi dan visualisasi menggunakan Principal Component Analysis (PCA). Hasil clustering menghasilkan tiga kelompok utama yang merepresentasikan tingkatan sosial ekonomi berbeda yaitu rendah, menengah dan tinggi. Validasi menggunakan Silhouette Score menunjukkan nilai sebesar 0,2592, menandakan bahwa pemisahan klaster cukup baik. Temuan ini menunjukkan bahwa K-M…
creator: Dewi Eka Putri
creator: Eka Praja Wiyata Mandala
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-04
publication date: 2025-08-04
volume: 21
number: 2
starting page: 483
ending page: 494
doi: 10.35889/progresif.v21i2.2809

Dashboard Prestasi Siswa SMPN 1 Purwakarta: Visualisasi Data dengan Google Looker Studio
type: item
description: Management of non-academic student achievement data, which has been presented in various formats, is difficult to analyze systematically. This research aims to develop a visual dashboard of student achievement based on Google Looker Studio at SMPN 1 Purwakarta. The research uses the CRISP-DM (Cross Industry Standard Process for Data Mining) method which consists of six stages, namely business understanding, data understanding, data preparation, modeling, evaluation and deployment. The data used includes exam results and student participation in various competitions. Dashboards were developed to present information visually, interactively and in real-time to support data-based evaluation and decision making. The development results show that this dashboard is able to integrate student achievement data into one platform that is easy to access and understand by teachers and school officials. The conclusion of this research is that the use of a dashboard based on Google Looker Studio can make a significant contribution to improving the quality of managers. Keywords: Dashboard; Student Achievement; Data Visualization; Google Looker Studio Abstrak Pengelolaan data prestasi siswa non-akademik yang selama ini disajikan dalam berbagai format, sulit dianalisis secara sistematis. Penelitian ini bertujuan untuk mengembangkan dashboard visual prestasi siswa berbasis Google Looker Studio di SMPN 1 Purwakarta. Penelitian menggunakan metode CRISP-DM (Cross Industry Standard Process for Data Mining) yang terdiri dari enam tahapan, yaitu business understanding, data understanding, data preparation, modeling, evaluation, dan deployment. Data yang digunakan mencakup hasil ujian dan keikutsertaan siswa dalam berbagai lomba. Dashboard dikembangkan untuk menyajikan informasi secara visual, interaktif, dan real-time guna mendukung evaluasi dan pengambilan keputusan berbasis data. Hasil pengembangan menunjukkan bahwa dashboard ini mampu mengintegrasikan data prestasi siswa ke dalam satu pla…
creator: Fakhirah Azhar
creator: Nuur Wachid Abdulmajid
creator: Beni Ari Hidayatullah
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-04
publication date: 2025-08-04
volume: 21
number: 2
starting page: 495
ending page: 507
doi: 10.35889/progresif.v21i2.2883

Model Penilaian Kinerja Tenaga Kontrak Dinas Perdagangan Perindustrian Koperasi dan UKM Berbasis Algoritma Weighted Product
type: item
description: The performance assessment of contract workers at DPPKUKM Kapuas Regency is important to improve agency performance. However, the problem is that the contract extension process only relies on competency tests from the Kapuas Regency government. DPPKUKM has not used a computer system for performance assessment, and in some cases, contract workers get the same competency test results. The impact is that DPPKUKM has difficulty in assessing and selecting contract workers who should get contract extensions. This study proposes a Decision Support System (DSS) to help DPPKUKM assess the performance of contract workers. In this study, the DPPKUKM uses the Weighted Product (WP) method to assess the performance of DPPKUKM contract workers with the criteria assessed being Work Discipline, Compliance with Rules, and Ability to Work. The results of the performance test show that after using the system, there is no data on the same contract worker performance values. The assessment system has proven to be able to better differentiate the performance and contribution of each contract worker. Keywords: Performance Assessment; Contract Renewal; Contract Workers; Weighted Product Abstrak Penilaian kinerja tenaga kontrak di DPPKUKM Kabupaten Kapuas penting untuk meningkatkan kinerja instansi. Namun, masalahnya adalah proses perpanjangan kontrak hanya mengandalkan uji kompetensi dari pemerintah Kabupaten Kapuas. DPPKUKM belum menggunakan sistem komputer untuk penilaian kinerja, dan dalam beberapa kasus, tenaga kontrak mendapatkan hasil uji kompetensi yang sama Dampaknya adalah DPPKUKM kesulitan dalam menilai dan memilih tenaga kontrak yang seharusnya mendapatkan perpanjangan kontrak. Penelitian ini mengusulkan Sistem Penunjang Keputusan (SPK) untuk membantu DPPKUKM menilai kinerja tenaga kontrak. Dalam penelitian ini, SPK untuk menilai kinerja tenaga kontrak DPPKUKM menggunakan metode Weighted Product (WP) dengan kriteria yang dinilai adalah Disiplin Kerja, Kepatuhan Terhadap Aturan, K…
creator: Yulia Yudihartanti
creator: Adriel Adriel
creator: Taufiq Taufiq
creator: Eka Chandra Kirana
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-05
publication date: 2025-08-05
volume: 21
number: 2
starting page: 508
ending page: 519
doi: 10.35889/progresif.v21i2.2746

Deteksi Skintone Menggunakan Model Warna CIELAB dan Klasifikasi Support Vector Machine
type: item
description: This study aims to develop a human skin tone detection system using the CIELAB color model and the Support Vector Machine (SVM) classification method. The data used consists of facial images from the SkinTone Classification dataset (Kaggle). The preprocessing stage includes image resizing and conversion to the CIELAB color space. Feature extraction is performed by calculating the mean and standard deviation of each L*, a*, and b* channel. The dataset is then divided into training and testing sets with an 80:20 ratio, and classification is carried out using SVM with a Radial Basis Function (RBF) kernel. System performance is evaluated using accuracy, precision, recall, and F1-score metrics. The system achieved an accuracy of 92.09%, with the best performance in the dark skin tone category, reaching an F1-score of 97.9%. Although the performance for other classes was slightly lower, the system overall demonstrates strong classification capability and potential for practical and accurate skin tone recognition applications. Keyword: Skin color; CIELAB; Feature extraction; Support Vector Machine; Image classification Abstrak Penelitian ini memiliki tujuan untuk mengembangkan sebuah sistem dalam mendeteksi warna kulit manusia dengan pendekatan model warna CIELAB dan metode klasifikasi Support Vector Machine (SVM). Data yang digunakan berupa citra wajah dari SkinTone Classification dataset (Kaggle). Tahapan praproses yang meliputi perubahan ukuran citra dan konversi ke ruang warna CIELAB. Ekstraksi fitur dilakukan dengan menghitung rata-rata serta simpangan baku pada setiap komponen L*, a*, dan b*. Dataset kemudian dipisahkan menjadi data pelatihan dan data pengujian dengan rasio 80:20, lalu diklasifikasikan menggunakan SVM dengan kernel Radial Basis Function (RBF). Evaluasi performa sistem dianalisis menggunakan metrik akurasi, presisi, recall, dan F1-score. Sistem ini berhasil mencapai akurasi sebesar 92,09%, dengan performa terbaik pada kategori warna kulit gelap (dark)…
creator: Shofiyah Shofiyah
creator: Nunik Pratiwi
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-05
publication date: 2025-08-05
volume: 21
number: 2
starting page: 520
ending page: 531
doi: 10.35889/progresif.v21i2.2827

Pendekatan Metode UCD dan SUS dalam Redesign UI/UX Aplikasi Mobile iPusnas
type: item
description: This study aims to improve the quality of user experience when accessing the iPusnas mobile application, a digital library platform available in Indonesia. Based on observations and interviews, several issues were identified, including an overly crowded user interface that lacks visual appeal, a navigation flow that is perceived as unintuitive, and features that are considered less relevant to users’ needs. To address these problems, a redesign process was carried out by applying the User Centered Design (UCD) approach, which emphasizes user involvement at every stage of development. The evaluation of the redesigned version was conducted using the System Usability Scale (SUS) method. The test results showed an increase in the SUS score from 33.5 to 82.83 after the redesign. This improvement indicates that applying the UCD approach, accompanied by SUS evaluation, can effectively enhance the usability level of the iPusnas application. Keywords: iPusnas application; System usability scale; User centered design; Usability Abstrak Penelitian ini dimaksudkan untuk meningkatkan mutu pengalaman pengguna ketika menggunakan aplikasi iPusnas versi mobile, yang merupakan platform perpustakaan digital di Indonesia. Berdasarkan hasil observasi dan wawancara, ditemukan sejumlah permasalahan yang dialami oleh pengguna, antara lain tampilan antarmuka yang dianggap terlalu padat sehingga kurang menarik secara visual, alur navigasi yang dirasa kurang intuitif, serta beberapa fitur yang dinilai kurang relevan dengan kebutuhan pengguna. Untuk menjawab permasalahan tersebut, dilakukan proses perancangan ulang dengan menerapkan pendekatan User Centered Design (UCD), yaitu metode yang berfokus pada pengguna di setiap langkah proses pengembangan. Evaluasi terhadap desain baru dilakukan melalui pengukuran menggunakan metode System Usability Scale (SUS). Hasil pengujian menunjukkan bahwa nilai SUS meningkat dari 33.5 menjadi 82,83 setelah dilakukan redesign. Peningkatan ini mengindikasikan ba…
creator: Oktaviani Ariyaningsih
creator: Mia Kamayani
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-07
publication date: 2025-08-07
volume: 21
number: 2
starting page: 532
ending page: 544
doi: 10.35889/progresif.v21i2.2826

Analisis Perbandingan Metode Yolo Dan Faster R-CNN Dalam Deteksi Objek Manusia
type: item
description: Human object detection is an important component in surveillance systems, behavior analysis, and crowd management in public spaces such as stadiums, shopping malls, and terminals. However, the detection process often faces obstacles such as inconsistent lighting, complex backgrounds, and high object density. This study aims to compare the performance of two object detection algorithms, namely YOLOv10 and Faster R-CNN, in detecting humans. The dataset used is uniform and covers a wide range of environmental conditions to ensure fair and objective evaluation. This research involves the stages of data collection, pre-processing, model training, testing, and performance evaluation. The test results show that YOLOv10 has a performance advantage with an mAP50 value of 0.75, higher than that of Faster R-CNN which obtained an AP50 of 0.67. Based on these findings, YOLOv10 is recommended for use in applications that require real-time human detection with a high level of accuracy. Kata kunci: YOLOV10; Faster R-CNN; Object Detection Abstrak Deteksi objek manusia merupakan komponen penting dalam sistem pengawasan, analisis perilaku, dan pengelolaan keramaian di ruang publik seperti stadion, pusat perbelanjaan, dan terminal. Namun, proses deteksi sering menghadapi kendala seperti pencahayaan yang tidak konsisten, latar belakang kompleks, dan kepadatan objek tinggi. Penelitian ini bertujuan buat membandingkan kinerja dua algoritma deteksi objek, yaitu YOLOv10 dan Faster R-CNN, dalam mendeteksi manusia. Dataset yang digunakan bersifat seragam dan mencakup berbagai kondisi lingkungan untuk memastikan evaluasi yang adil dan objektif. Penelitian ini melibatkan tahapan pengumpulan data, pra-pemrosesan, pelatihan model, pengujian, dan evaluasi performa. Hasil pengujian menunjukkan bahwa YOLOv10 memiliki keunggulan performa dengan nilai mAP50 sebesar 0,75, lebih tinggi dibandingkan Faster R-CNN yang memperoleh AP50 sebesar 0,67. Berdasarkan temuan tersebut, YOLOv10 direkomendasikan untu…
creator: Muhammad Ilham Pratama
creator: Nurchim Nurchim
creator: Eko Purwanto
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-07
publication date: 2025-08-07
volume: 21
number: 2
starting page: 545
ending page: 556
doi: 10.35889/progresif.v21i2.2890

Perbandingan Algoritma NBC dan SVM dalam Analisis Sentimen Terhadap Dampak Kesehatan Rokok Elektrik
type: item
description: There is no optimal method for accurately classifying public opinion, so an analytical approach is needed that is able to capture the nuances of public sentiment regarding the health impacts of e-cigarettes. This study examines public perception of the health impacts of electronic cigarettes using two classification algorithms: NBC and SVM. Data sourced from social media X (formerly Twitter) underwent stages of data cleaning, sentiment labeling, TF-IDF weighting, and data balancing through the SMOTE technique. Performance evaluation was conducted using four key metrics: accuracy, precision, recall, and f1-score. NBC achieved 80.5% accuracy with high recall despite low precision. In contrast, SVM recorded superior performance with 95.2% accuracy and more consistent balance between precision and recall. Therefore, the Support Vector Machine (SVM) algorithm is recommended as a more effective method for analyzing public sentiment regarding electronic cigarettes. Keywords: Electronic Cigarette; Entiment Analysis; Naïve Bayes Classifier; Support Vector Machine. Abstrak Belum adanya metode yang optimal untuk mengklasifikasikan opini publik secara akurat, sehingga diperlukan pendekatan analitik yang mampu menangkap nuansa sentimen masyarakat terhadap dampak kesehatan rokok elektrik. Studi ini mengkaji persepsi publik terhadap dampak kesehatan rokok elektrik dengan menerapkan dua algoritma klasifikasi: NBC dan SVM. Data yang bersumber dari media sosial X (eks Twitter) diproses melalui tahapan pembersihan data, pelabelan sentimen, pembobotan menggunakan TF-IDF, serta penyeimbangan data menggunakan teknik SMOTE. Evaluasi performa dilakukan menggunakan empat metrik utama: accuracy, precision, recall, dan f1-score. NBC memperoleh akurasi sebesar 80,5% dengan recall tinggi meskipun precision-nya rendah. Sebaliknya, SVM mencatat performa superior dengan akurasi 95,2% serta keseimbangan precision dan recall yang lebih konsisten. Oleh karena itu, algoritma Support Vector Machine (SV…
creator: Hani Rahmawati
creator: Isa Faqihuddin Hanif
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-07
publication date: 2025-08-07
volume: 21
number: 2
starting page: 557
ending page: 572
doi: 10.35889/progresif.v21i2.2903

Evaluation of Information Security Readiness Using the KAMI Index 5.0 and ISO/IEC 27001:2022
type: item
description: Information security evaluation is a crucial process for organizations to assess their level of readiness and to establish clear security management roles and responsibilities. Higher education institutions, such as Tuanku Tambusai University of Heroes, have significant responsibilities in safeguarding sensitive data. The institution maintains extensive records that include students' personal information, academic transcripts, financial data, and information relating to faculty and staff. Given that multiple organizational units access this information, ensuring data protection and compliance with privacy regulations is paramount. This research purposes to assess the readiness and maturity of information security at Tuanku Tambusai Heroes University using the KAMI Index version 5.0, using the ISO/IEC 27001:2022 standard. This assessment covers six information security domains and shows maturity levels ranging from I+ to II, which indicates that the institution is at an early stage of development, with only the fundamental framework established. In addition, the completeness of the ISO/IEC 27001 implementation was assessed as “Unqualified”, with a score of 379, corresponding to maturity levels I+ to II. These findings highlight the need for targeted improvements to meet the requirements for ISO/IEC 27001:2022 certification. Key word: Evaluation; ISO/IEC 27001:2022; Information Security; KAMI Index Abstrak Mengevaluasi keamanan informasi sangat penting bagi organisasi karena membantu menilai tingkat kesiapan keamanan informasi dan menentukan peran dan tanggung jawab manajer keamanan. Institusi pendidikan tinggi, seperti Universitas Pahlawan Tuanku Tambusai, bertanggung jawab untuk menjaga informasi sensitif. Institusi ini menyimpan sejumlah besar data mahasiswa, termasuk rincian pribadi, catatan akademis, informasi keuangan, dan data mengenai dosen dan staf. Karena banyak unit mengakses data ini, sangat penting untuk memastikan perlindungan dan kepatuhannya terhadap p…
creator: Beri Novriyadi
creator: Muhammad Jazman
creator: Megawati Megawati
creator: M. Afdal
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-07
publication date: 2025-08-07
volume: 21
number: 2
starting page: 573
ending page: 585
doi: 10.35889/progresif.v21i2.2882

Evaluasi Pengalaman Pengguna Spotify dan YouTube Music Menggunakan Metode User Experience Questionnaire
type: item
description: Despite the popularity of Spotify and YouTube Music in Indonesia, reviews from the Google Play Store show several complaints regarding bugs and errors in the application's features and functions that affect the user experience. Using the User Experience Questionnaire (UEQ) method, this research aims to evaluate and compare the user experience of the two applications through a quantitative approach from a scale of attractiveness, clarity, efficiency, reliability, stimulation and novelty. 253 valid respondent data shows that both applications received positive ratings on all UEQ scales. However, benchmark results show that Spotify gets good scores on five scales, while YouTube Music only scores on one scale. The T-test results show significant differences between the two applications on the 5 UEQ scales, except on the reliability scale. Overall, this research provides insight into user experience and confirms the importance of continuous development of UX aspects for application developers in improving user experience. Keywords: Spotify; YouTube Music; User Experience Questionnaire; User experience Abstrak Terlepas dari kepopuleran Spotify dan YouTube Music di Indonesia, ulasan dari Google Play Store menunjukkan beberapa keluhan terkait bug dan error pada fitur serta fungsionalitas aplikasi yang memengaruhi pengalaman pengguna. Dengan metode User Experience Questionnaire (UEQ), penelitian ini bertujuan untuk mengevaluasi dan membandingkan pengalaman pengguna kedua aplikasi melalui pendekatan kuantitatif dari skala daya tarik, kejelasan, efisiensi, keandalan, stimulasi, dan kebaruan. 253 data responden yang valid menunjukkan kedua aplikasi mendapatkan evaluasi positif pada seluruh skala UEQ. Namun, Hasil benchmark menunjukkan Spotify dinilai excellent pada lima skala, sementara YouTube Music hanya pada satu skala. Hasil uji T-test menunjukkan perbedaan signifikan antara kedua aplikasi pada 5 skala UEQ, kecuali skala keandalan. Secara keseluruhan, penelitian ini memberi…
creator: Stefanus Francois Saro Harefa
creator: Christ Rudianto
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-07
publication date: 2025-08-07
volume: 21
number: 2
starting page: 586
ending page: 598
doi: 10.35889/progresif.v21i2.2831

Penerapan K-Means Clustering Untuk Mengelompokkan Tingkat Konsumsi Listrik Menurut Provinsi di Indonesia
type: item
description: Electricity consumption in Indonesia continues to increase every year, but its distribution is not evenly distributed across provinces. Therefore, an analysis is needed to group regions based on electricity consumption patterns to help plan more efficient distribution. This study aims to group regions in Indonesia based on their electricity usage patterns, considering customer types such as households, industry, business, social, government buildings, and street lighting. The clustering process is performed using the k-means clustering method. The data used is official PLN data from 2019 to 2024. The analysis process is performed using RapidMiner, with steps of data preprocessing, application of K-Means, and evaluation of the results using the Davies-Bouldin Index (DBI) method. The results of the study show that the provinces in Indonesia are divided into two clusters, namely Cluster 0 with 29 provinces with low-medium consumption and Cluster 1 with 5 provinces with high consumption, especially in the Java Island region. The DBI value of 0.507 indicates that the resulting clustering is quite optimal. These results are in line with the PLN report and should support more targeted infrastructure planning and power distribution policies. Keywords: Data Mining; K-Means Clustering; Electricity Consumption Abstrak Konsumsi energi listrik di Indonesia terus meningkat setiap tahunnya, namun distribusinya tidak merata di seluruh provinsi. Oleh karena itu, diperlukan analisis untuk mengelompokkan wilayah berdasarkan pola konsumsi energi listrik guna membantu perencanaan distribusi yang lebih efisien. Penelitian ini bertujuan untuk mengelompokkan wilayah-wilayah di Indonesia berdasarkan pola penggunaan listriknya, dengan mempertimbangkan jenis pelanggan seperti rumah tangga, industri, bisnis, sosial, gedung pemerintahan, dan penerangan jalan umum. Proses pengelompokan dilakukan dengan menggunakan metode k-means clustering. Data yang digunakan merupakan data resmi dari PLN tahun…
creator: Yenni Lutfhi Berliana
creator: Irwansyah Irwansyah
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-07
publication date: 2025-08-07
volume: 21
number: 2
starting page: 599
ending page: 612
doi: 10.35889/progresif.v21i2.2876

Sistem Pendukung Keputusan Kontrak Kerja Karyawan Probation Menggunakan Metode Fuzzy Simple Additive Weighting
type: item
description: The evaluation of probationary employees is crucial in determining their eligibility for contract extension. The assessment typically involves aspects such as performance, discipline, skills, interpersonal abilities, integrity, and appearance, but often this assessment remains subjective. This study integrates fuzzy logic to reduce uncertainty and applies the Simple Additive Weighting (SAW) method to assign weights objectively. The developed system enables a more fair and measurable evaluation. Results indicate that employees 1, 2, and 4 are eligible for contract extension. Performance testing yielded an accuracy of 75%, with both precision and recall at 66.7%, demonstrating alignment between the system's recommendations and the HR department's decisions. This method serves as a decision support tool that promotes a more objective and structured decision-making process. Keywords: Employee Evaluation; Decision Support System; Fuzzy-SAW Method Abstrak Evaluasi terhadap karyawan probation penting dilakukan dalam penentuan kelayakan perpanjangan kontrak kerja. Penilaian mencakup aspek kinerja, kedisiplinan, keterampilan intrapersonal, integritas, dan penampilan, namun penilaian ini kerap bersifat subjektif. Penelitian ini menggabungkan logika fuzzy untuk mengurangi ketidakpastian dan metode Simple Additive Weighting (SAW) guna menentukan bobot secara objektif. Sistem yang dikembangkan menghasilkan penilaian lebih adil dan terukur. Hasil menunjukkan bahwa karyawan 1, 2, dan 4 layak diperpanjang kontraknya. Pengujian performa menghasilkan akurasi 75%, dengan precision dan recall sebesar 66,7%, yang menunjukkan kesesuaian sistem dengan keputusan HRD. Metode ini berfungsi sebagai alat pendukung yang membantu proses pengambilan keputusan agar lebih objektif dan terukur. Kata kunci: Penilaian Kinerja Karyawan; Pengambilan Keputusan; Metode Fuzzy-SAW
creator: Ammar Yusuf Ash shidiq
creator: Hafizh Siddiq
creator: Prawido Utomo
creator: Muhammad Bucci Ryando
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-10
publication date: 2025-08-10
volume: 21
number: 2
starting page: 613
ending page: 622
doi: 10.35889/progresif.v21i2.2907

Klasifikasi Kelayakan Ban Sepeda Motor Menggunakan Metode Convolutional Neural Network
type: item
description: Tires is a primary component in motorcycle that plays a crucial role in driving safety and comfort. Damage to tires, such as wear, cuts, or cracks, can reduce traction, disrupt stability, and increase the risk of traffic accidents. Generally, tire condition inspections are conducted conventionally by technicians who may have limitations in accurately detecting damage. This research aims to develop a tire damage classification system using the Convolutional Neural Network (CNN) method with the MobileNetV2 architecture and with transfer learning approach. The dataset used consists of motorcycle tire images categorized into four classes: normal, bald, cutburst, and spotwear. The training process was conducted using a grid search technique to determine the optimal hyperparameter configuration. The best results obtained with a combination of batch size 16, learning rate 0.001, and 43 epochs, yielding a test accuracy of 96.67%, precision of 95%, recall of 95%, and an F1-score of 95%. Keywords: Tire; Convolutional Neural Network; MobileNetV2 Abstrak Ban merupakan komponen utama pada kendaraan sepeda motor yang berperan penting dalam keselamatan dan kenyamanan berkendara. Kerusakan pada ban, seperti keausan, sobekan, atau retakan, dapat mengurangi traksi, mengganggu stabilitas, dan meningkatkan risiko kecelakaan lalu lintas. Pemeriksaan kondisi ban secara umum masih dilakukan secara manual oleh teknisi, yang memiliki keterbatasan dalam hal objektivitas dan akurasi. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi kerusakan ban sepeda motor secara otomatis menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2 dengan pendekatan transfer learning. Dataset terdiri dari citra ban sepeda motor yang diklasifikasikan ke dalam empat kelas, yaitu normal, bald, cutburst, dan spotwear. Proses pelatihan dilakukan melalui metode grid search untuk menentukan konfigurasi parameter terbaik. Hasil terbaik diperoleh pada kombinasi hyperparameter de…
creator: Azzar Rizky
creator: Wijang Widhiarso
creator: Abdul Rahman
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-10
publication date: 2025-08-10
volume: 21
number: 2
starting page: 623
ending page: 633
doi: 10.35889/progresif.v21i2.2917

Penerapan Metode User-Centered Design dalam Redesign UI/UX Aplikasi Mobile Transjakarta
type: item
description: The Transjakarta mobile application plays a vital role in supporting public transportation in Jakarta, but many users report poor usability, complicated navigation, and unintuitive interface design. Based on an initial survey involving 20 active users, the application scored only 26.8 on the System Usability Scale (SUS), placing it in the “Poor” category. This study adopts the User-Centered Design (UCD) approach in the redesign process of the application's UI/UX, involving four key stages: understanding the context of use, identifying user needs, designing appropriate solutions, and evaluating the alignment of those solutions with the identified needs. Evaluation was conducted using the SUS method and A/B Testing. The redesign process led to a significant improvement, raising the SUS score 85.25 and showing that 86% of users preferred the new design. Additional features such as “Top-up Balance” and “Transaction History” also received positive feedback. These findings demonstrate that the UCD approach effectively improves usability and user satisfaction, and can serve as a reference for future developments in public service applications. Keywords: A/B Testing; Redesign; System Usability Scale; Transjakarta; User Centered Design Abstrak Aplikasi mobile Transjakarta merupakan layanan pendukung transportasi publik di Jakarta yang penting, namun masih menghadapi berbagai masalah dalam kemudahan penggunaan, integrasi fitur, dan tampilan antarmuka yang kurang intuitif. Berdasarkan survei awal terhadap 20 pengguna, skor System Usability Scale (SUS) hanya mencapai 26,8 yang termasuk kategori Poor. Penelitian ini menggunakan pendekatan User-Centered Design (UCD) dalam proses perancangan ulang UI/UX aplikasi, yang melibatkan empat tahapan inti: memahami konteks penggunaan, mengidentifikasi kebutuhan pengguna, merancang solusi desain, serta mengevaluasi kesesuaian solusi terhadap kebutuhan tersebut. Evaluasi dilakukan menggunanan SUS dan A/B Testing. Hasilnya menunjukkan pening…
creator: Luthfia Masruroh Syah
creator: Mia Kamayani
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-10
publication date: 2025-08-10
volume: 21
number: 2
starting page: 634
ending page: 647
doi: 10.35889/progresif.v21i2.2900

Model sistem Pakar Pendiagnosis Penyakit Pada Sapi Dengan Metode Certainty Factor
type: item
description: Limited access to veterinary health services, farmers’ lack of knowledge, and the high cost and time required for consultations are major obstacles in handling cattle diseases, especially in remote areas. These issues often lead to delays in diagnosis and treatment, resulting in significant economic losses. This study proposes the development of a computer-based expert system using the Certainty Factor method as a solution to assist farmers in independently diagnosing cattle diseases. The system is equipped with its main feature, a diagnosis process, as well as a chat feature for direct consultation with veterinarians as a novelty element. Testing was conducted on 19 test data by comparing the system’s diagnostic results with those of experts, showing a match rate of 84.21% and 15.79% mismatch due to symptom similarities and differences in severity interpretation. The results demonstrate that the system has fairly good accuracy and is suitable for use as an early diagnostic tool, which is expected to accelerate disease handling and reduce farmers’ economic losses. Keywords: Expert System; Certainty Factor; Cattle Disease Diagnosis; Veterinary Consultation Chat. Abstrak Keterbatasan akses layanan kesehatan hewan, minimnya pengetahuan peternak, serta biaya dan waktu yang tinggi untuk konsultasi menjadi hambatan dalam penanganan penyakit sapi, khususnya di daerah terpencil. Permasalahan ini kerap mengakibatkan keterlambatan diagnosis dan penanganan, sehingga menimbulkan kerugian ekonomi yang signifikan. Penelitian ini mengusulkan pengembangan sistem pakar berbasis komputer menggunakan metode Certainty Factor sebagai solusi untuk membantu peternak mendiagnosis penyakit sapi secara mandiri. Sistem ini dilengkapi dengan fitur utamanya proses diagnosis, serta fitur chat untuk konsultasi langsung dengan dokter hewan sebagai unsur kebaruan. Pengujian dilakukan pada 19 data uji dengan membandingkan hasil diagnosis sistem dan pakar, menunjukkan tingkat kesesuaian sebesar 84,21…
creator: Exalthya Sera
creator: Fadilah Fadilah
creator: Bahar Bahar
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-12
publication date: 2025-08-12
volume: 21
number: 2
starting page: 648
ending page: 661
doi: 10.35889/progresif.v21i2.2853

Eksplorasi Sentimen Publik di Media Sosial terhadap Isu RUU TNI Menggunakan Pendekatan Machine Learning
type: item
description: The developments regarding the revision of the Indonesian National Armed Forces Law (RUU TNI) have sparked various reactions from the public, most of which have been expressed through social media platforms. This research aims to evaluate public opinion on the TNI Bill issue by utilizing machine learning technology and the Support Vector Machine (SVM) algorithm. Data were collected from three social networks, namely YouTube, Instagram, and X (formerly known as Twitter), with a total of 4,530 data points. The stages of data processing include web scraping, data cleaning, text preprocessing, automatic labelling using textblob, word weighting through TF-IDF, and data balancing using the SMOTE method. The sentiment classification results reveal that the majority of public opinions are positive and neutral, with the highest model accuracy achieved at parameter C = 1, namely 94.60% for YouTube, 95.80% for Instagram, and 97.33% for X. These findings demonstrate the effectiveness of the SVM approach in categorizing public opinions generated from social media, and imply that social media serves as an important source for understanding public views on national policy issues. Keywords: Machine Learning; Support Vector Machine; SMOTE; Text Mining; TF IDF Abstrak Perkembangan mengenai revisi Undang-Undang Tentara Nasional Indonesia (RUU TNI) telah menimbulkan berbagai reaksi dari masyarakat, yang sebagian besar disampaikan melalui platform media sosial. Penelitian ini bertujuan untuk mengevaluasi pandangan publik terhadap isu RUU TNI dengan memanfaatkan teknologi pembelajaran mesin dan algoritma Support Vector Machine (SVM). Data dikumpulkan dari tiga jejaring sosial, yaitu YouTube, Instagram, dan X (yang sebelumnya dikenal sebagai Twitter), dengan total sebanyak 4.530 data. Tahapan pengolahan data mencakup web scraping, pembersihan data, pra-pemrosesan teks, pelabelan otomatis menggunakan TextBlob, pemberian bobot kata melalui TF-IDF, serta penyeimbangan data dengan metode SMOT…
creator: Sandy Aprilyanto
creator: Rudy Latuperissa
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-24
publication date: 2025-08-24
volume: 21
number: 2
starting page: 662
ending page: 674
doi: 10.35889/progresif.v21i2.2819

Penerapan CNN Pada Klasifikasi Kepribadian Anak Sekolah Dasar Berdasarkan Citra Tulisan Tangan
type: item
description: Indonesia has a rich culture. This creates dynamics in personality formation. In schools, teachers' understanding of students' personalities is key. So far, conventional methods such as observation, interviews and graphology have been used to classify children's personalities, which are less efficient. This study uses the CNN algorithm with the Mobilenetv2 architecture. Dataset was taken from 5th grade students from 3 SDN Palembang with a total of 246 data divided into 2 classes, namely extrovert 101 data and introvert 145 data. Then grayscale preprocessing, normalization, and augmentation. Ratio of training, validation, and test data is 80:10:10. Model was trained with Adam optimizer, learning rate 0.0001, batch size 20, and epochs of 12. The result is a model accuracy of 69.2% with a tendency for the model to classify images as introverts. This study is expected to help teachers gain insight into the best teaching approach in the classroom. Keywords: CNN; Graphology; Elementary School Abstrak Indonesia memiliki budaya yang kaya. Ini menciptakan dinamika dalam pembentukan kepribadian. Di sekolah, pemahaman guru terhadap kepribadian siswa menjadi kunci. Selama ini untuk mengklasifikasi kepribadian anak, digunakan metode konvensional seperti observasi, wawancara dan ilmu grafologi yang kurang efisien. Penelitian ini menggunakan algoritma CNN dengan arsitektur Mobilenetv2. Dataset diambil dari siswa kelas 5 dari 3 SDN Palembang dengan total 246 data yang dibagi menjadi 2 kelas, yaitu extrovert 101 data dan introvert 145 data. Kemudian dilakukan preprocessing grayscale, normalisasi, dan augmentasi. Rasio data latih, validasi, dan uji adalah 80:10:10. Model dilatih dengan Adam optimizer, learning rate 0,0001, batch size 20, dan epoch sebanyak 12. Hasilnya akurasi model sebesar 69,2% dengan kecenderungan model mengklasifikasi citra sebagai introvert. Penelitian ini diharapkan dapat membantu guru mendapatkan pandangan tentang cara pendekatan mengajar yang terbaik di kelas…
creator: Muhammad Ishaq Maulana
creator: Hafiz Irsyad
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-24
publication date: 2025-08-24
volume: 21
number: 2
starting page: 675
ending page: 686
doi: 10.35889/progresif.v21i2.2959

ESP32-CAM Camera Integration and Android Control for Portable Real-Time Surveillance Solution
type: item
description: The development of Internet of Things (IoT) technology has encouraged the creation of a more flexible and efficient intelligent surveillance system. This research aims to design and implement an ESP32-CAM-based surveillance car prototype capable of real-time visual monitoring via Wi-Fi connection. The system integrates an ESP32-CAM microcontroller, an L298N motor driver, and four DC motors, and is controlled through a specially developed Android application to set the direction of motion and receive live video displays from the camera. Test results show that the system has stable connectivity performance with an average delay of 0.88 seconds, motor control response of 0.48 seconds, and good quality video streaming with an average delay of 1.09 seconds. The effective operational range was recorded up to 30 meters with acceptable connection quality. The system shows potential as a cost-effective, portable, and easily accessible surveillance solution for applications in hard-to-reach or high-risk areas. This research opens up further development opportunities with the integration of artificial intelligence, additional sensors, as well as expansion of the communication range. Keywords: IoT; ESP32-CAM; Real-Time Surveillance; Surveillance RC Car; Video Streaming Abstrak Perkembangan teknologi Internet of Things (IoT) telah mendorong terciptanya sistem pengawasan cerdas yang lebih fleksibel dan efisien. Penelitian ini bertujuan untuk merancang dan mengimplementasikan prototipe mobil pengawasan berbasis ESP32-CAM yang mampu melakukan pemantauan visual secara real-time melalui koneksi Wi-Fi. Sistem ini mengintegrasikan mikrokontroler ESP32-CAM, driver motor L298N, dan empat motor DC, serta dikendalikan melalui aplikasi Android yang dikembangkan khusus untuk mengatur arah gerak dan menerima tampilan video langsung dari kamera. Hasil pengujian menunjukkan bahwa sistem memiliki performa konektivitas yang stabil dengan rata-rata delay 0,88 detik, respons kendali motor sebesar 0…
creator: Zulhipni Reno Saputra Elsi
creator: Dedi Haryanto
creator: Muhammad Ihsan
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-25
publication date: 2025-08-25
volume: 21
number: 2
starting page: 687
ending page: 699
doi: 10.35889/progresif.v21i2.2700

Aplikasi Pendataan Kesehatan Anak Pada Posyandu Anggur Satu Berbasis Web Menggunakan Metode Agile
type: item
description: Posyandu Anggur Satu is a child health service center that has made a significant contribution in reducing mortality rates and improving the nutritional status of the community. This study aims to overcome the problem of manual child health recording at Posyandu Anggur Satu which often causes delays in reporting and potential data loss. To answer these problems, a web-based information system has been designed and built that supports the digital and structured data collection process. The development approach uses the Agile method, by directly involving users at each stage of development. Data was collected through interviews, observations, and literature studies, then analyzed to design a system that suits field needs. The results of the study showed that the system was able to improve work efficiency, minimize recording errors, and speed up the reporting process. The conclusion shows that this solution is feasible to be implemented to support the digital transformation of basic health services. Keywords: Posyandu; Agile; Children; Health; Community Abstrak Posyandu Anggur Satu merupakan pusat pelayanan kesehatan anak yang telah memberikan kontribusi signifikan dalam penurunan angka kematian dan peningkatan status gizi masyarakat. Penelitian ini bertujuan untuk mengatasi permasalahan pencatatan kesehatan anak yang masih dilakukan secara manual di Posyandu Anggur Satu, yang sering menyebabkan keterlambatan pelaporan dan potensi kehilangan data. Untuk menjawab permasalahan tersebut, telah dirancang dan dibangun sistem informasi berbasis web yang mendukung proses pendataan secara digital dan terstruktur. Pendekatan pengembangan menggunakan metode Agile, dengan keterlibatan langsung pengguna pada setiap tahapan pengembangan. Data dikumpulkan melalui wawancara, observasi, dan studi pustaka, kemudian dianalisis untuk merancang sistem yang sesuai kebutuhan lapangan. Hasil penelitian menunjukkan bahwa sistem mampu meningkatkan efisiensi kerja, meminimalkan kesalahan pencat…
creator: Oktora Putri Wardani
creator: Siti Saroh
creator: Nova Teguh Sunggono
creator: Refi Nabillah Royadi
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-25
publication date: 2025-08-25
volume: 21
number: 2
starting page: 700
ending page: 711
doi: 10.35889/progresif.v21i2.2872

Perbandingan Algoritma K-Means dan DBSCAN dalam Pengelompokkan Data Penjualan Elektronik
type: item
description: This study aims to compare the performance of the K-Means and DBSCAN algorithms in clustering electronic sales data at CV Rey Gasendra. Clustering methods are used to identify sales patterns based on the transaction intensity of each product. The K-Means algorithm was implemented by determining the optimal number of clusters using the Elbow Method and Silhouette Score evaluation. Meanwhile, DBSCAN was applied using a density-based approach, which does not require specifying the number of clusters in advance and relies on eps and minPts parameters. The results showed that K-Means produced more stable and interpretable clusters with a Silhouette Score of 0.961, whereas DBSCAN excelled in detecting outliers and handling irregular data distributions, despite generating a large amount of noise. Performance evaluation using metrics such as Silhouette Score, Davies-Bouldin Index (DBI), and Sum of Squared Errors (SSE) indicated that K-Means is more suitable for structured product segmentation, while DBSCAN is more effective for exploring unusual sales patterns. These findings can support data-driven business decisions, such as marketing strategies, inventory management, and product recommendations. Keywords: K-Means; DBSCAN; Clustering; Sales data; Product segmentation Abstrak Penelitian ini bertujuan untuk membandingkan kinerja algoritma K-Means dan DBSCAN dalam pengelompokan data penjualan elektronik pada CV Rey Gasendra. Metode clustering digunakan untuk mengidentifikasi pola penjualan berdasarkan intensitas transaksi setiap produk. Algoritma K-Means diimplementasikan dengan penentuan jumlah klaster optimal menggunakan metode Elbow dan evaluasi Silhouette Score. Sementara itu, DBSCAN diimplementasikan dengan pendekatan berbasis kepadatan, tanpa memerlukan jumlah klaster awal, dan menggunakan parameter eps serta minPts. Hasil penelitian menunjukkan bahwa K-Means memberikan hasil clustering yang lebih stabil dan mudah diinterpretasikan dengan Silhouette Score mencapai 0,96…
creator: Tria Setyani
creator: Yulia Indriani
creator: Muhammad Fadli
creator: Erliyan Redy Susanto
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-25
publication date: 2025-08-25
volume: 21
number: 2
starting page: 712
ending page: 721
doi: 10.35889/progresif.v21i2.2775

Evaluasi Kualitas Sistem Informasi Kepegawaian di BKPSDM Kota Salatiga Berdasarkan ISO/IEC 25010
type: item
description: The Personnel Management Information System (SIMPEG) is an important component in supporting the management of personnel data, especially in government environments. This research aims to evaluate the quality of SIMPEG used by BKPSDM Salatiga City by referring to the international standard ISO/IEC 25010. This standard covers eight main aspects in assessing software quality, namely: Functional Suitability, Performance efficiency, compatibility, Usability, Reliability, Security, Maintainability, and Portability. The research method used is a quantitative survey by distributing questionnaires to 25 employees who actively use the system. The evaluation results show that SIMPEG Salatiga is in the "Very Good" category with an average score of 89%. The most prominent aspects are functional suitability and ease of system maintenance (Maintainability). However, several other aspects such as performance efficiency, security and ease of access on various devices also received high marks. These findings show that in general, the system has been able to meet user needs very well, although there are still several things that can be improved. Keywords: SIMPEG; ISO/IEC 25010; Quality evaluation; Personnel system Abstrak Sistem Informasi Manajemen Kepegawaian (SIMPEG) menjadi salah satu komponen penting dalam mendukung pengelolaan data kepegawaian, khususnya di lingkungan pemerintahan. Penelitian ini bertujuan untuk mengevaluasi kualitas SIMPEG yang digunakan oleh BKPSDM Kota Salatiga dengan mengacu pada standar internasional ISO/IEC 25010. Standar ini mencakup delapan aspek utama dalam penilaian kualitas perangkat lunak, yaitu: Functional Suitability, Performance efficiency, compatibility, Usability, Reliability, Security, Maintainability, dan Portability. Metode penelitian yang digunakan adalah survei kuantitatif melalui penyebaran kuesioner kepada 25 pegawai yang aktif menggunakan system. Hasil evaluasi menunjukkan bahwa SIMPEG Salatiga berada dalam kategori “Baik Sekali” dengan …
creator: Cindi Tiffany Wahyono Putri
creator: Johan J.C Tambotoh J.C Tambotoh
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-25
publication date: 2025-08-25
volume: 21
number: 2
starting page: 722
ending page: 731
doi: 10.35889/progresif.v21i2.3008

Efektivitas Game RPG Maker MV dalam Edukasi Lingkungan pada Siswa Sekolah Dasar
type: item
description: Interactive 2D educational game based on RPG Maker MV is an innovative learning media that combines the concept of role-playing game (RPG) with educational material to create an interesting learning experience. The development of digital technology encourages the transformation of education, one of which is through interactive and accessible educational games. This research developed an environmental-themed game for elementary school students using the Luther-Sutopo version of the Multimedia Development Life Cycle (MDLC) method, which includes six stages: concept, design, material collection, manufacture, testing, and distribution. Based on the calculation of the feasibility level, the level of respondent satisfaction obtained from filling out the questionnaire reached 91.81% of the total 40 respondents. This finding proves that this 2D educational game is effective in increasing learning motivation, understanding environmental concepts, and fostering positive student behavior. Thus, the development of RPG Maker MV-based games using MDLC can be an innovative solution to create more interactive and effective learning at the elementary school level. Keywords: 2D educational game; RPG Maker MV; Environmental awareness; Elementary school. Abstrak Game edukasi 2D interaktif berbasis RPG Maker MV adalah media pembelajaran inovatif yang menggabungkan konsep Role-Playing Game (RPG) dengan materi edukasi untuk menciptakan pengalaman belajar yang menarik. Perkembangan teknologi digital mendorong transformasi pendidikan, salah satunya melalui game edukasi yang interaktif dan mudah diakses. Penelitian ini mengembangkan game bertema lingkungan untuk siswa SD menggunakan metode Multimedia Development Life Cycle (MDLC) versi Luther-Sutopo, yang meliputi enam tahap: konsep, desain, pengumpulan bahan, pembuatan, pengujian, dan distribusi. Berdasarkan hasil perhitungan tingkat kelayakan, tingkat kepuasan responden yang diperoleh dari pengisian kuesioner mencapai 91,81% dari total 40 …
creator: Amalia Putri Mardani
creator: Tiara Choirundhini Mawardi
creator: Muhammad Iqbal Hanafri
creator: Eka Uliyanti Putri Br Bangun
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-25
publication date: 2025-08-25
volume: 21
number: 2
starting page: 732
ending page: 743
doi: 10.35889/progresif.v21i2.2913

Analisis Komparatif Unjuk Kerja Model Vision Transformers Dengan ConvNeXt Dalam Rekognisi Citra Warangka Keris Bali
type: item
description: The application of attention mechanisms in image recognition has emerged as a new paradigm in computer vision, serving as a foundational approach in generative AI. Two state-of-the-art models frequently referenced in recent studies are Vision Transformers (ViT), introduced by Google, and ConvNeXt, developed by Meta (Facebook) AI Research. However, their application in recognizing local cultural imagery, such as the warangka (sheath) of the Balinese keris, remains highly limited. The urgency of this study lies in evaluating the effectiveness of AI models in supporting technology-based cultural preservation. This study aims to compare the unjuk kerjance of these two models in handling the classification and recognition of warangka keris (Balinese kris sheaths). The methodology involves data augmentation, feature extraction, patch processing (for ViT), model construction, evaluation, and image recognition analysis using Grad-CAM. The dataset comprises a combination of primary and secondary sources. Primary data were collected through field visits to kris-making workshops in Bali, while secondary data were obtained from previous studies. The kris sheath image classes used in this study include: 'Sesrengatan', 'Kojongan', 'Batun Poh', 'Kekandikan', and 'Beblatungan'. The study successfully developed image classification models, achieving an accuracy of 82% with the ViT model and 97% with the ConvNeXt model. The recognition process effectively highlighted the most significant regions of each image, providing valuable insight for future generative AI research. Keywords: Attention, ConvNeXt, Keris Bali, Vision Transformers Abstrak Penerapan attention dalam rekognisi citra menjadi pendekatan baru dalam pengenalan gambar dan berpotensi menjadi benchmark dalam pengembangan kecerdasan buatan generatif. Dua model terkini yang banyak diteliti adalah Vision Transformers (ViT) dari Google dan ConvNeXt dari Meta AI. Namun, penerapan keduanya dalam pengenalan citra budaya lokal seper…
creator: Ni Putu Widya Yuniari
creator: Gde Wikan Pradnya Dana
creator: I Gede Wira Darma
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-26
publication date: 2025-08-26
volume: 21
number: 2
starting page: 744
ending page: 758
doi: 10.35889/progresif.v21i2.2987

Audit Aplikasi SRIKANDI Menggunakan Framework COBIT 2019 Menggunakan Domain MEA
type: item
description: Digital archive management is an important challenge in supporting efficiency and compliance with bureaucracy in government agencies. The SRIKANDI application is a solution for digital letter archiving, but its implementation in the XYZ Office still faces various obstacles, such as the lack of an automated system that detects user access violations to the application, as well as the lack of a system that directly detects changes in external regulations. This study aims to evaluate the governance of the SRIKANDI application using the COBIT 2019 framework domain MEA. The research method employs a qualitative descriptive approach, involving interviews with application users and observations of the SRIKANDI application. This study focuses on three in the MEA domain, namely MEA01, MEA02, and MEA03. The results of this study indicate that the current capability levels for MEA01 and MEA02 are at level 3, and MEA03 is at level 5. This indicates that the application technology governance is not yet fully optimal and further improvements are still needed. Keyword: SRIKANDI; COBIT 2019; MEA; Capability level Abstrak Pengelolaan arsip digital menjadi tantangan penting dalam mendukung efisiensi dan kepatuhan terhadap birokrasi di instansi pemerintah. Aplikasi SRIKANDI hadir sebagai solusi untuk pengarsipan surat secara digital, namun penerapannya di Dinas XYZ masih menghadapi berbagai kendala, seperti belum tersedianya sistem otomatis yang mendeteksi pelanggaran akses pengguna aplikasi, serta belum adanya sistem yang mendeteksi secara langsung perubahan regulasi ekternal. Penelitian ini bertujuan untuk mengevaluasi tata kelola aplikasi SRIKANDI menggunakan framework COBIT 2019 domain MEA. Metode penelitian menggunakan deskriptif kualitatif, yaitu melalui wawancara dengan pengguna aplikasi dan observasi terhadap aplikasi SRIKANDI. Penelitian ini berfokus pada 3 management objectives pada domian MEA, yaitu MEA01, MEA02, dan MEA03. Hasil dari penelitian ini menunjukkan bahwa capabi…
creator: Dama Septhian Purwanto
creator: Andeka Rocky Tanaamah
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-26
publication date: 2025-08-26
volume: 21
number: 2
starting page: 759
ending page: 767
doi: 10.35889/progresif.v21i2.3078

Penerapan Metode Support Vector Machine Untuk Memprediksi Kelulusan Tepat Waktu
type: item
description: Timely graduation is a key indicator of student academic success in higher education. This study develops a predictive model for on-time student graduation at Universitas Sepuluh Nopember Papua using the Support Vector Machine (SVM) method with a linear kernel. The model examines the influence of academic performance and student status as Indigenous Papuans (OAP) or non-OAP in predicting graduation probability. Model evaluation was conducted using Confusion Matrix, ROC Curve, and Cross-Validation, demonstrating that the model achieved high accuracy of 92% in the initial testing phase, increasing to 97% after cross-validation. The evaluation also showed a Precision of 90%, Recall of 100%, and F1-Score of 95%, confirming the model’s effectiveness in distinguishing students at risk of delayed graduation. With its high predictive accuracy, this model can serve as a data-driven academic decision-making tool to identify at-risk students and implement more targeted academic interventions to improve timely graduation rates. Keywords: graduation prediction; model evaluation; machine learning; Support Vector Machine. Abstrak Kelulusan tepat waktu menjadi indikator utama keberhasilan akademik mahasiswa di perguruan tinggi. Penelitian ini mengembangkan model prediksi kelulusan tepat waktu mahasiswa Universitas Sepuluh Nopember Papua menggunakan metode Support Vector Machine (SVM) dengan kernel linier. Model ini menganalisis pengaruh kinerja akademik dan status mahasiswa sebagai Orang Asli Papua (OAP) atau non-OAP dalam menentukan probabilitas kelulusan tepat waktu. Evaluasi model dilakukan menggunakan Confusion Matrix, ROC Curve, dan Cross-Validation, yang menunjukkan bahwa model memiliki akurasi tinggi sebesar 92% pada tahap pengujian awal dan meningkat menjadi 97% setelah validasi silang. Hasil pengujian juga menunjukkan nilai Precision 90%, Recall 100%, dan F1-Score 95%, yang menegaskan efektivitas model dalam membedakan mahasiswa yang berisiko mengalami keterlambatan kelulu…
creator: Avif Setyawan
creator: Rahmat Haryadi Kiswanto
creator: Heru Sutejo
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-26
publication date: 2025-08-26
volume: 21
number: 2
starting page: 768
ending page: 779
doi: 10.35889/progresif.v21i2.2619

Sistem Pengenalan Wajah Dengan Metode Template Matching Berdasarkan Struktur Wajah Dan Kornea Mata
type: item
description: Implementing an attendance system that relies on facial recognition as a replacement for conventional attendance still presents several technical challenges, such as low detection accuracy in low-light conditions, fluctuations in facial expressions, or when employees wear additional elements such as masks and glasses. To overcome these weaknesses in the facial recognition system, a template matching method is used that focuses on facial structure and corneal features. This technique works by matching the original image with a reference pattern (template) stored in a database, thus enabling identification to be carried out on unique and stable facial areas, such as facial structure and cornea. The results of the template matching method testing aimed at facial and corneal recognition showed satisfactory performance in identifying users, with an accuracy rate reaching 84.44% from a total of 45 tests conducted on 15 employees. In addition, the use of local features such as the cornea has proven to be very helpful in improving identification accuracy, especially in real environments with diverse conditions. Keywords: Attendance system; Facial recognition; Cornea and face; Template matching Abstrak Penerapan sistem absensi yang mengandalkan pengenalan wajah sebagai pengganti absensi konvensional masih menghadirkan beberapa tantangan teknis, seperti akurasi deteksi yang rendah dalam kondisi cahaya minim, fluktuasi ekspresi wajah, atau ketika karyawan mengenakan elemen tambahan seperti masker dan kacamata. Untuk mengatasi kelemahan dalam sistem pengenalan wajah, digunakan metode pencocokan template yang berfokus pada struktur wajah serta fitur kornea mata. Teknik ini berfungsi dengan cara mencocokkan gambar asli dengan pola acuan (template) yang sudah disimpan dalam database, sehingga memungkinkan identifikasi dilakukan pada area wajah yang unik dan stabil, seperti struktur wajah dan kornea. Hasil dari pengujian metode pencocokan template yang ditujukan untuk pengenalan wa…
creator: Regiza Dara Lista
creator: Rozali Toyib
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-26
publication date: 2025-08-26
volume: 21
number: 2
starting page: 780
ending page: 792
doi: 10.35889/progresif.v21i2.3058

Penerapan Metode FIFO Untuk Mengelola Persediaan Bahan Baku Pada PT MPM
type: item
description: In the manufacturing industry, inventory recording that still relies on Excel often leads to various issues, such as stock data discrepanices and a decline in raw material quality due to prolonged storage. This research focuses on designing a FIFO-based inventory information system to optimize accuracy and efficiency in stock management. The system was developed through a system development method consisting of analysis, design, implementation, and evaluation stages. Testing was carried out by running the system using the company’s actual transaction data. The findings of the functional testing demonstrate that all features, form login, master data management, transaction recordin, implementation of the FIFO method, to report generation, function according to specifications. All test scenarios were successful, proving that the system is capable of accurately managing and presenting inventory data and supporting decision-making. Keyword: Raw material inventory; FIFO method; Quality; Stock Abstrak Dalam dunia industri manufaktur, pencatatan persediaan bahan baku yang masih menggunakan excel sering kali menimbulkan berbagai kendala, seperti ketidaksesuaian data stok dan penurunan kualitas bahan baku karena penyimpanan terlalu lama. Penelitian ini difokuskan pada perancangan sistem informasi persediaan berbasis FIFO guna mengoptimalkan akurasi serta efisiensi dalam pengelolaan stok. Sistem dikembangkan menggunakan metode pengujian sistem dengan tahapan analisis, perancangan, implementasi, dan evaluasi. Pengujian ini dilakukan yang dengan menjalankan sistem pada data transaksi aktual Perusahaan. Hasil pengujian fungsionalitas menunjukkan seluruh fitur, mulai dari login, pengelolaan data master, pencatatan transaksi, penerapan metode FIFO, hingga pembuatan laporan, berfungsi sesuai spesifikasi. Semua skenario uji berhasil, membuktikan sistem mampu mengelola dan menyajikan data persediaan secara akurat serta mendukung pengambilan keputusan. Kata Kunci: Persediaan bahan bak…
creator: Avi Mareta
creator: Ade Nur Anissa
creator: Nova Teguh Sunggono
creator: Refi Nabillah Royadi
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-26
publication date: 2025-08-26
volume: 21
number: 2
starting page: 793
ending page: 804
doi: 10.35889/progresif.v21i2.2924

Analisis Optimalisasi Meminimumkan Biaya Operasional Menggunakan Program Linier Melalui Metode Simpleks (Studi Kasus pada Pabrik Tahu Dan Tempe)
type: item
description: This study discusses the optimization of operational costs at Apo Gudang Tofu and Tempe Factory using linear programming with the Simplex Method. The primary issue faced was the rising costs of raw materials and labor, affecting production efficiency. This study aimed to determine the optimal combination of resource allocation to minimize operational costs without reducing production capacity. The Simplex Method was applied in a mathematical model solved using QM for Windows software. The results indicated that balanced tofu and tempeh production achieved a minimum operational cost of Rp1,766,999,000. Sensitivity analysis showed that the obtained solution was stable, with a shadow price of zero, meaning minor constraint changes did not affect the optimization results. These findings support the application of linear programming in the soybean-based food industry and can serve as a reference for similar industries in managing production costs more efficiently. Keywords: Cost optimization; Linear programming; Simplex Method. Abstrak Penelitian ini membahas optimasi biaya operasional pada Pabrik Tahu dan Tempe Apo Gudang menggunakan pemrograman linier dengan metode Simpleks. Permasalahan utama yang dihadapi adalah meningkatnya biaya bahan baku dan tenaga kerja, yang mempengaruhi efisiensi produksi. Penelitian ini bertujuan untuk menentukan kombinasi optimal dalam alokasi sumber daya guna menekan biaya operasional tanpa mengurangi kapasitas produksi. Metode Simpleks diterapkan dalam model matematis yang diselesaikan menggunakan perangkat lunak QM for Windows. Hasil penelitian menunjukkan bahwa produksi tahu dan tempe yang seimbang menghasilkan biaya operasional minimum sebesar Rp1.766.999.000. Analisis sensitivitas menunjukkan bahwa solusi yang diperoleh stabil, dengan shadow price nol, sehingga perubahan kecil dalam kendala tidak berpengaruh terhadap hasil optimasi. Hasil ini mendukung penerapan pemrograman linier dalam industri makanan berbasis kedelai dan dapat menja…
creator: Meylisa Kurdita
creator: Heru Sutejo
creator: Emy Lenora Tatuhey
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-27
publication date: 2025-08-27
volume: 21
number: 2
starting page: 805
ending page: 813
doi: 10.35889/progresif.v21i2.2618

Aplikasi Web Pencatatan Keuangan UMKM Dengan Pendekatan Agile Pada Toko Perlengkapan Bayi
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description: Micro, small, and medium enterprises (MSME) in the baby equipment retail sectore often face challenges in financial management, primarily due to manual and unstructured record-keeping. This leads to difficulties in accurately monitoring the financial condition of the business. This study aims to develop a simple, efficient, and user-oriented web-based financial recording application tailored to the needs of MSME, using the Agile Scrum software development methodology. The development process includes identifying user requirements, creating a product backlog, developing the application in several sprints, followed by testing and evaluation phases. The application was built using Laravel-based web technology and MySQL. Test results show that the application helps MSME actors record daily transactions, categorize types of income and expenses, and generate financial reports automatically. Keywords: Agile; Baby Equipment; Financial Recording; Scrum; Web Application. Abstrak UMKM di sector penjualan perlengkapan bayi sering menghadapi kendala dalam pengelolaan keuangan, terutama karena pencatatan masih dilakukan secara manual dan tidak terstruktur. Hal ini berdampak pada kesulitan dalam memantau kondisi keuangan usaha secara akurat. Penelitian ini bertujuan untuk mengembangkan aplikasi web pencatatan keuangan yang sederhana, efisien, dan sesuai dengan kebutuhan UMKM, dengan menerapkan metode pengembangan perangkat lunak Agile Scrum. Proses pengembangan dimulai dari identifikasi kebutuhan pengguna, penyusunan product backlog, pengembangan aplikasi dalam beberapa sprint, hingga tahap pengujian dan evaluasi. Aplikasi dikembangkan menggunakan teknologi web berbasis Laravel dan MySQL. Hasil pengujian menunjukan bahwa aplikasi ini mampu membantu pelaku UMKM dalam mencatat transaksi harian, mengelompokan jenis pengeluaran dan pemasukan, serta menghasilkan laporan keuangan secara otomatis. Kata kunci: Agile; Perlengakap Bayi; Pencatatan Keuangan; Srum; Aplikasi Web
creator: Isnaini Miftakhul Janah
creator: Muhammad Iqbal Hanafri
creator: Edy Tekat Bronto Waluyo
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-28
publication date: 2025-08-28
volume: 21
number: 2
starting page: 814
ending page: 823
doi: 10.35889/progresif.v21i2.2929

Penerapan Fuzzy Logic pada Sistem Controlling Suhu dan Kelembapan Solar Dryer Berbasis Internet of Things
type: item
description: The utilization of solar dryer technology as a renewable energy-based drying solution still faces challenges, particularly in monitoring and controlling temperature and humidity in remote areas with unstable internet connectivity. This research aims to design and develop a temperature and humidity control system for solar dryers based on the Internet of Things (IoT), implementing fuzzy logic as an adaptive decision-making method. The system employs Arduino Uno, DHT22 sensor, SIM800L, Raspberry Pi, and a GSM modem. Temperature and humidity data are transmitted via SMS to the Raspberry Pi as a gateway, then forwarded to a cloud server and processed using fuzzy logic to automatically control the fan when thresholds are exceeded. The test results show that the system operates in real-time, is responsive and accurate, and reliably performs fuzzy logic-based automatic control even in areas with limited internet access. This system proves effective as a solution for remote IoT-based temperature and humidity monitoring and control. Keywords: Solar Dryer; Fuzzy Logic; Raspberry Pi; Arduino; Internet of Things. Abstrak Pemanfaatan teknologi solar dryer sebagai solusi pengeringan energi terbarukan masih menghadapi kendala, khususnya dalam aspek pemantauan dan pengendalian suhu serta kelembapan di lokasi terpencil yang tidak terjangkau jaringan internet stabil. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem pengendali suhu dan kelembapan solar dryer berbasis Internet of Things (IoT) dengan menerapkan logika fuzzy sebagai metode pengambilan keputusan adaptif. Sistem ini menggunakan perangkat Arduino Uno, sensor DHT22, SIM800L, Raspberry Pi, dan modem GSM. Data suhu dan kelembapan dikirim melalui SMS ke Raspberry Pi sebagai gateway, lalu diteruskan ke server cloud dan diproses oleh logika fuzzy untuk mengendalikan kipas secara otomatis jika ambang batas terlampaui. Hasil pengujian menunjukkan bahwa sistem bekerja secara real-time, responsif, dan akurat, serta m…
creator: Kristian Juri Damai Lase
creator: Antonius Bima Murti Wijaya
creator: Gogor Christmas Setyawan
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-31
publication date: 2025-08-31
volume: 21
number: 2
starting page: 824
ending page: 837
doi: 10.35889/progresif.v21i2.3079

Deteksi Keaslian Uang Rupiah Menggunakan Metode Canny Edge Detection dan K-Mean Clustering
type: item
description: The escalating problem of counterfeit currency in various countries, primarily due to easy access to information on manufacturing methods and advancements in color printing technology, renders traditional '3D' identification methods less effective. This research aims to design an authenticity detection system for Rupiah banknotes using a digital image processing approach. This study utilized a total of 40 Rupiah banknote images as test objects, comprising 10 genuine and 10 counterfeit images for each of the Rp 50,000 and Rp 100,000 denominations. The methodology applied included image acquisition, conversion to grayscale, followed by image segmentation. This system integrates Canny Edge Detection to extract edge details and K-Means Clustering for image grouping. Key features analyzed were Aspect Ratio and Edge Density, which assist in differentiating between genuine and counterfeit banknotes. Test results indicate that the developed system could identify the authenticity of Rupiah banknotes with an average accuracy of 87.50%. This combined approach offers an effective solution for Rupiah banknote authentication. Keyword: Authenticity Rupiah Banknotes; Canny Edge Detection; K-Means Clustering; Aspect Ratio, Edge Density. Abstrak Permasalahan uang palsu yang terus meningkat di berbagai negara, terutama dengan kemudahan akses informasi dan teknologi pencetak warna, menjadikan metode identifikasi tradisional '3D' kurang efektif. Penelitian ini bertujuan untuk merancang sebuah sistem deteksi keaslian uang kertas Rupiah menggunakan pendekatan pengolahan citra digital. Penelitian ini menggunakan total 40 citra uang kertas Rupiah sebagai objek uji, terdiri dari 10 gambar uang asli dan 10 gambar uang palsu untuk masing-masing nominal Rp 50.000 dan Rp 100.000. Metodologi yang diterapkan meliputi akuisisi citra, konversi citra menjadi skala abu-abu, diikuti dengan segmentasi citra. Sistem ini mengintegrasikan Canny Edge Detection untuk mengekstraksi detail tepi dan K-Means Clu…
creator: Selvana Fadila
creator: Dedy Abdullah
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-31
publication date: 2025-08-31
volume: 21
number: 2
starting page: 838
ending page: 847
doi: 10.35889/progresif.v21i2.2922

Pengembangan Sistem ERP untuk Distribusi Produk PT Solomon Indo Global: Modul Shipping, Inventory, dan User Management
type: item
description: Digital transformation is driving companies to integrate technology to improve efficiency. PT Solomon Indo Global, a fast-moving consumer goods company, faces distribution challenges due to a lack of system integration, such as the absence of multi-warehouse stock records, undocumented distribution flows, and unclear partner roles. This study aims to develop an integrated Enterprise Resource Planning (ERP) system with a focus on the User Management, Inventory, and Shipment modules. The methodology employs a 5-Stage ERP Implementation approach (Project Preparation, Business Blueprint, Realization, Final Preparation, Go Live and Support) without a direct implementation phase. Problem identification was conducted through interviews and observations, followed by mapping of the as-is and to-be business processes, and system development using the Scrum method (backlog, sprint, daily Scrum, review, retrospective). The research results produced an integrated ERP system design that includes the main modules of user management, inventory, shipment, and sales support, which has proven to improve the efficiency of the company's distribution process. Keywords: Enterprise Resource Planning; Supply chain; Scrum Method; Logistic Abstrak Transformasi digital mendorong perusahaan mengintegrasikan teknologi guna meningkatkan efisiensi. PT Solomon Indo Global, sektor barang konsumsi cepat, menghadapi tantangan distribusi akibat kurangnya integrasi sistem, seperti tidak adanya pencatatan stok multi-gudang, alur distribusi tidak terdokumentasi, dan peran mitra yang belum jelas. Penelitian ini bertujuan mengembangkan sistem Enterprise Resource Planning (ERP) terintegrasi dengan fokus pada modul User Management, Inventory, dan Shipment. Metodologi menggunakan pendekatan 5 Stages ERP Implementation (Project Preparation, Business Blueprint, Realization, Final Preparation, Go Live and Support) tanpa tahap implementasi langsung. Identifikasi masalah dilakukan melalui wawancara dan observasi, d…
creator: Afif Ghani Zahran
creator: Rizka Hadwiyanti
creator: Agung Brastama Putra
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-31
publication date: 2025-08-31
volume: 21
number: 2
starting page: 848
ending page: 861
doi: 10.35889/progresif.v21i2.3077

Klasifikasi Body Mass Index Berbasis Estimasi Dimensi Tubuh melalui Pengolahan Citra Digital
type: item
description: The issues of impracticality and potential errors in manual height and weight measurements have driven the development of an automated digital image-based system. This study aims to develop a measurement tool using the Canny Edge Detection method in Matlab to classify Body Mass Index (BMI) status (underweight, normal, overweight, obese) without physical contact. Testing was conducted on 40 data samples, yielding an accuracy of 99.99% for height (error 0.37%) and 99.97% for weight (error 1.25%), with a maximum measurement tolerance of ±2 cm for height and ±2 kg for weight to enhance system reliability. The classification results of nutritional status based on BMI estimation showed good performance, with a classification agreement rate of 90% of the total 40 test data, indicating that the system is sufficiently reliable and efficient in classifying BMI categories based on antropometri parameter estimates from digital images. Keywords: Body Mass Index; Image Processing; Canny Edge Detection; Matlab Abstrak Permasalahan ketidakpraktisan dan potensi kesalahan dalam pengukuran manual tinggi dan berat badan mendorong pengembangan sistem berbasis citra digital secara otomatis. Penelitian ini bertujuan membangun alat bantu pengukuran menggunakan metode Canny Edge Detection dalam Guide Matlab guna mengklasifikasikan status Body Mass Index (kurus, normal, gemuk, obesitas) tanpa kontak fisik. Pengujian dilakukan pada 40 sampel data, menghasilkan akurasi 99,99% untuk tinggi badan (error 0,37%) dan 99,97% untuk berat badan (error 1,25%), dengan toleransi maksimal pengukuran ±2 cm untuk tinggi dan ±2 kg untuk berat guna meningkatkan reliabilitas sistem. Hasil klasifikasi status gizi berdasarkan estimasi BMI menunjukan performa yang baik, dengan tingkat kecocokan hasil klasifikasi mencapai 90% dari total 40 data uji yang mengindikasikan bahwa sistem cukup andal dan efisien dalam mengklasifikasikan kategori Body Mass Index berdasarkan estimasi parameter antropometrik dari citra digi…
creator: Sita Juliza
creator: Yovi Apridiansyah
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-31
publication date: 2025-08-31
volume: 21
number: 2
starting page: 862
ending page: 873
doi: 10.35889/progresif.v21i2.3049

Analisis Sentimen terhadap Dedy Mulyadi Berdasarkan Komentar Youtube Menggunakan Metode Naïve Bayes
type: item
description: The increasing spread of public opinion on social media, particularly YouTube, highlights the need for automated sentiment analysis to understand public perception toward political figures. This study analyzes public sentiment toward Dedy Mulyadi, a prominent political figure in Indonesia, using the Naïve Bayes method on 600 YouTube comments collected via web crawling. Data were processed in RapidMiner through text cleaning, manual labeling of 100 comments as training data, and automatic classification. Results show a model accuracy of 66.67%, with perfect recall (100%) for negative sentiment but 0% precision and recall for positive sentiment, indicating training data imbalance. Despite this limitation, the study demonstrates the applicability of Naïve Bayes for small-scale sentiment analysis in political communication contexts. Findings suggest the method’s potential when data distribution is balanced, supporting data-driven political strategies. Keywords: Sentiment analysis; Naïve Bayes; YouTube; Public opinion Abstrak Permasalahan sentimen negatif dan positif terhadap figur publik di media sosial, khususnya YouTube, semakin kompleks seiring meningkatnya partisipasi masyarakat dalam menyampaikan opini. Analisis sentimen diperlukan untuk memahami dinamika opini publik secara otomatis dan mendukung strategi komunikasi politik berbasis data. Penelitian ini menganalisis sentimen terhadap Dedy Mulyadi menggunakan metode Naïve Bayes pada 600 komentar YouTube yang dikumpulkan melalui crawling. Data diproses dengan RapidMiner melalui tahapan pembersihan teks, pelabelan manual (100 komentar), dan klasifikasi otomatis. Hasil menunjukkan akurasi model 66,67%, dengan recall 100% untuk sentimen negatif namun precision dan recall 0% untuk positif, mengindikasikan bias data latih akibat ketidakseimbangan kelas. Meskipun demikian, studi ini menegaskan potensi Naïve Bayes dalam analisis sentimen skala kecil, terutama untuk deteksi sentimen dominan, dengan rekomendasi perbaikan kes…
creator: Nhadya Vita Loca
creator: Dedy Abdullah
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-31
publication date: 2025-08-31
volume: 21
number: 2
starting page: 874
ending page: 884
doi: 10.35889/progresif.v21i2.3102

Menelusuri Narasi Imunologi Melalui Eksplorasi Komik Augmented Reality Untuk Pendidikan Sekolah Dasar
type: item
description: This study developed innovative Augmented Reality (AR) comic-based learning media to facilitate elementary school students' understanding of immunology concepts. The main problems were the complexity of abstract immunology material and the limited availability of interactive media suitable for children. This study aimed to design media that combines visual comic narratives with AR technology to visualize biological processes interactively. The development of this media is carried out through a Research and Development (R&D) approach with the ADDIE stages, namely analysis, design, development, implementation, and evaluation. The validation process involves experts and limited trials on 30 elementary school students. Results indicate 96.7% positive responses regarding ease of understanding and visual appeal, and 100% of students stated that AR animations clarified the concepts. The novelty lies in the integration of printed comics with interactive 2D animations based on AR markers, child-friendly character design, and a multisensory learning experience that combines visual, audio, and real-world interaction. The conclusion indicates that this medium is effective as an educational tool for science, while also providing a more immersive and enjoyable learning experience. Keywords: Augmented Reality comic; Immunology education; Interactive learning media Abstrak Penelitian ini mengembangkan media pembelajaran inovatif berbasis komik Augmented Reality (AR) untuk memudahkan pemahaman konsep imunologi pada siswa sekolah dasar. Permasalahan utama adalah kompleksitas materi imunologi yang abstrak dan keterbatasan media interaktif yang sesuai untuk anak-anak. Penelitian ini bertujuan merancang media yang menggabungkan narasi visual komik dengan teknologi AR untuk memvisualisasikan proses biologis secara interaktif. Pengembangan media ini dilakukan melalui pendekatan Research and Development (R&D) dengan tahapan ADDIE, yaitu analisis, desain, pengembangan, implementasi, dan eva…
creator: Purwanto Saputro
creator: Zacklin Syahrani
creator: Agustinus Sirumapea
creator: Siti Maisaroh
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-27
publication date: 2025-08-27
volume: 21
number: 2
starting page: 885
ending page: 897
doi: 10.35889/progresif.v21i2.2915

Evaluasi Kelayakan Platform Ucapan Digital dengan Metode Heuristic Evaluation
type: item
description: The digital greeting platform of Satya Wacana Christian University (UKSW) was developed as an environmentally friendly solution to replace physical greeting media in ceremonial events. This study aims to evaluate the usability level of the platform using the Heuristic Evaluation method based on Nielsen and Molich’s ten principles. Data were collected through questionnaires distributed to five respondents and in-depth interviews with three of them. The evaluation results indicate five principles with the highest severity levels, namely Help and Documentation (3.8), Visibility of System Status (3.6), Error Prevention (3.6), User Control and Freedom (3.4), and Flexibility and Efficiency of Use (3.2). Identified issues include the absence of user guides, lack of process feedback, weak input validation, limited navigation, and minimal design variations. Recommended improvements involve adding a help page, process indicators, input validation, main navigation buttons, and more varied greeting templates. These findings confirm the effectiveness of heuristic evaluation in identifying interface weaknesses and provide guidance for developing a more user-friendly system. Keywords: Digital greeting platform; Heuristic Evaluation; Usability; User experience Abstrak Platform ucapan digital Universitas Kristen Satya Wacana (UKSW) dikembangkan sebagai solusi ramah lingkungan untuk menggantikan media ucapan fisik pada acara seremonial. Penelitian ini bertujuan mengevaluasi tingkat usability platform tersebut menggunakan metode Heuristic Evaluation berdasarkan sepuluh prinsip Nielsen and Molich. Penilaian dilakukan melalui kuesioner kepada lima responden serta wawancara mendalam terhadap tiga di antaranya. Hasil evaluasi menunjukkan lima prinsip dengan tingkat keparahan tertinggi, yaitu Help and Documentation (3,8), Visibility of System Status (3,6), Error Prevention (3,6), User Control and Freedom (3,4), dan Flexibility and Efficiency of Use (3,2). Permasalahan yang ditemukan melipu…
creator: Aryatriwulan Buli Gala
creator: Hanna Prillysca Chernovita
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-29
publication date: 2025-08-29
volume: 21
number: 2
starting page: 898
ending page: 909
doi: 10.35889/progresif.v21i2.3112

Sistem Rekomendasi Kuliner Karanganyar Menggunakan Metode Hybrid Recommendation
type: item
description: Karanganyar has many culinary tourist destinations, but the number of diverse choices often makes it difficult for users to choose where to eat according to their preferences. Therefore, this research aims to help users find culinary places based on special menus. The recommendation system will be developed using a hybrid approach by combining content-based filtering and collaborative filtering. The content-based method matches the special menu with user input using cosine similarity, while collaborative filtering calculates the score from the normalized rating and the number of reviews using the weighted sum. A weight of 0.6 is given for content-based and 0.4 for collaborative because direct preference for menus is considered more dominant. The test was carried out using 5 different menus using precision and recall, with the test results for 100% recall, and the precision got a score of 75.42% because there are still recommendations displayed in the system that only have similar characteristics but are not really relevant to the user's preferences Keywords: Collaborative filtering; Content-based filtering; Hybrid recommendation; Culinary recommendation; Recommendation system. Abstrak Karanganyar memiliki banyak destinasi wisata kuliner, namun jumlah pilihan yang beragam sering menyulitkan pengguna dalam menentukan tempat makan sesuai preferensi. Oleh karena itu, penelitian ini bertujuan untuk membantu pengguna menemukan tempat kuliner berdasarkan menu spesial. Sistem rekomendasi yang akan dikembangkan menggunakan pendekatan hybrid dengan menggabungkan content-based filtering dan collaborative filtering. Metode content-based mencocokkan menu spesial dengan input pengguna menggunakan cosine similarity, sedangkan collaborative filtering menghitung skor dari rating yang dinormalisasi dan jumlah ulasan dengan menggunakan weighted sum. Bobot 0,6 diberikan untuk content-based dan 0,4 untuk collaborative karena preferensi langsung terhadap menu dinilai lebih dominan. Pengu…
creator: Radya Prameswari Putri
creator: Joni Maulindar
creator: Afu Ichsan Pradana
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-29
publication date: 2025-08-29
volume: 21
number: 2
starting page: 910
ending page: 920
doi: 10.35889/progresif.v21i2.3105

Sistem Informasi Absensi Guru Dengan Face Recognition Berbasis Web Menggunakan Metode Lock GPS
type: item
description: SMA Negeri Padang Lawas Utara needs a more modern attendance system to improve accuracy and efficiency in recording teacher attendance. This research aims to develop a web-based teacher attendance information system using the Face Recognition and GPS Lock methods to ensure valid and accurate attendance. The method used in system development is Rapid Application Development (RAD), which consists of Analysis & Quick Design, Prototype Cycles, Testing, and Deployment stages. The developed system allows teachers to attend only if they are in a predetermined school area through GPS location validation and facial verification. The system is tested to measure the main functionality, namely the accuracy of face identification and the validation of the location of attendance. The test results showed that the system was able to recognize the teacher's face with an accuracy rate of up to 95% in various conditions, and successfully rejected absences that were carried out outside the designated school area. In addition, the system shows a quick response time in attendance recording, which is less than 3 seconds. With this capability, the system contributes to improving the accuracy of attendance recording and reducing the potential for attendance fraud. Schools can monitor teacher attendance in real-time, thus supporting efficiency and effectiveness in attendance management management. Keyword: Face Recognition; GPS Lock; Attendance; Teacher; Web Abstrak SMA Negeri Padang Lawas Utara membutuhkan sistem absensi yang lebih modern untuk meningkatkan akurasi dan efisiensi dalam pencatatan kehadiran guru. Penelitian ini bertujuan untuk mengembangkan sistem informasi absensi guru berbasis web menggunakan metode Face Recognition dan GPS Lock untuk memastikan kehadiran yang valid dan akurat. Metode yang digunakan dalam pengembangan sistem adalah Rapid Application Development (RAD), yang terdiri dari tahapan Analysis & Quick Design, Prototype Cycles, Testing, dan Deployment. Sistem yang d…
creator: Alwi Awilo Haryada
creator: Muhamad Alda
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-30
publication date: 2025-08-30
volume: 21
number: 2
starting page: 921
ending page: 932
doi: 10.35889/progresif.v21i2.3051

Prediksi Keberhasilan Menindaklanjuti Pelanggan pada Dealer Mobil dengan Komparasi Algoritma Random Forest dan XGBoost
type: item
description: The automotive industry is facing intense competition in boosting vehicle sales, where the follow-up process with prospective customers plays a crucial role in sales conversion. This study develops a predictive model for the success of follow-ups at car dealerships by comparing two machine learning algorithms: Random forest and XGBoost. A dataset of Honda car dealership customers from 2023 was processed through a preprocessing stage, including handling data imbalance and encoding categorical data. The models were evaluated using accuracy, precision, recall, and F1-score metrics. The results show that XGBoost outperforms with an accuracy of 91.67%, compared to Random forest's 88.89%. Both models demonstrate balanced performance across positive and negative classes, indicating a significant improvement over previous approaches. This study recommends expanding the dataset and developing a prediction-based decision support system to enhance the marketing effectiveness of car dealerships. Keywords: Machine learning; Random forest; XGBoost Abstrak Industri otomotif menghadapi persaingan ketat dalam meningkatkan penjualan kendaraan, di mana proses tindak lanjut (Follow-up) kepada calon pelanggan menjadi faktor krusial dalam konversi penjualan. Penelitian ini mengembangkan model prediksi keberhasilan Follow-up pada dealer mobil dengan membandingkan dua algoritma machine learning, yaitu Random forest dan XGBoost. Dataset pelanggan dealer mobil Honda tahun 2023 diproses melalui tahap preprocessing, termasuk penanganan ketidakseimbangan data menggunakan encoding data kategorikal. Model dievaluasi menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil menunjukkan XGBoost unggul dengan akurasi 91,67%, lebih baik dibanding Random forest dengan akurasi 88,89%. Kedua model menunjukkan performa yang seimbang pada kelas positif dan negatif, menandai peningkatan signifikan dari pendekatan sebelumnya. Penelitian merekomendasikan perluasan dataset dan pengembangan sistem pen…
creator: Helma Nopijani Heidy
creator: Dimas Eko Putro
creator: Muhammad Fadli
creator: Erliyan Redy Susanto
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-29
publication date: 2025-08-29
volume: 21
number: 2
starting page: 933
ending page: 939
doi: 10.35889/progresif.v21i2.2776

Pengukuran Penerimaan Aplikasi BRImo Melalui Technology Acceptance Model di Kecamatan Banyubiru
type: item
description: The digital transformation in banking services encourages people to shift to mobile banking, including the BRImo application. Nevertheless, adoption in Banyubiru District remains relatively low as many still rely on conventional transactions. This study aims to analyze BRImo acceptance using the Technology acceptance model (TAM) with variables Perceived Ease of Use (PEU), Perceived Usefulness (PU), Attitude Toward Using (ATU), and Behavioral Intention (BI). A quantitative survey involving 50 respondents was conducted, and the data were processed using Partial Least Square (PLS-SEM). The results indicate that PEU and ATU significantly influence BRImo acceptance, while PU has a positive but limited effect on BI. The findings conclude that ease of use and positive attitudes are the key factors driving rural communities to adopt BRImo mobile banking services. Keywords: BRImo; Mobile banking; Technology acceptance model; Partial Least Square (PLS-SEM); Banyubiru Abstrak Transformasi digital di sektor perbankan mendorong masyarakat untuk beralih ke layanan mobile banking, salah satunya aplikasi BRImo. Namun, tingkat pemanfaatan di Kecamatan Banyubiru masih rendah karena sebagian besar masyarakat lebih memilih transaksi manual. Penelitian ini bertujuan menganalisis penerimaan BRImo dengan menggunakan kerangka Technology acceptance model (TAM) yang melibatkan variabel Perceived Ease of Use (PEU), Perceived Usefulness (PU), Attitude Toward Using (ATU), dan Behavioral Intention (BI). Metode yang digunakan adalah survei kuantitatif dengan 50 responden, kemudian data diolah melalui Partial Least Square (PLS-SEM). Hasil menunjukkan bahwa PEU dan ATU berpengaruh signifikan terhadap penerimaan BRImo, sedangkan PU berpengaruh positif tetapi terbatas terhadap BI. Penelitian ini menyimpulkan bahwa kemudahan penggunaan dan sikap positif merupakan faktor kunci dalam mendorong masyarakat pedesaan untuk mengadopsi layanan mobile banking BRImo. Kata Kunci: BRImo; Mobile banking; Technolog…
creator: Majid Ridho Al Rizal
creator: Yani Rahardja
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-30
publication date: 2025-08-30
volume: 21
number: 2
starting page: 940
ending page: 951
doi: 10.35889/progresif.v21i2.3160

Prediksi Risiko Kredit Nasabah Menggunakan Algoritma Data Mining: Studi Kasus pada PT Toyota Astra Finance
type: item
description: This study aims to develop a credit risk prediction model for customers at PT Toyota Astra Financial Services using data mining algorithms, specifically Random Forest and XGBoost. In response to the challenge of non-performing loans (NPL), machine learning-based predictive models offer an effective solution to identify potential risks early. The research utilizes historical customer data encompassing demographic information, employment status, and loan history. After data preprocessing, the models were evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results indicate that XGBoost outperformed other models with an accuracy of 91.67% and an F1-score of 0.89 for the positive class. These findings demonstrate that applying machine learning algorithms can significantly enhance credit selection efficiency and reduce potential losses from defaulted loans. Keywords: Credit Risk; Machine learning; Random Forest; XGBoost, Data mining. Abstrak Penelitian ini bertujuan untuk membangun model prediksi risiko kredit nasabah pada PT Toyota Astra Financial Services dengan memanfaatkan algoritma data mining, khususnya Random Forest dan XGBoost. Dalam menghadapi tantangan kredit macet, model prediktif berbasis machine learning dapat memberikan solusi yang efektif untuk mengidentifikasi potensi risiko sejak dini. Penelitian ini menggunakan data historis nasabah yang mencakup informasi demografi, status pekerjaan, dan riwayat pinjaman. Setelah melalui tahap pra-pemrosesan data, model dievaluasi menggunakan metrik akurasi, presisi, recall, F1-score, dan ROC-AUC. Hasil menunjukkan bahwa XGBoost memiliki performa terbaik dengan akurasi sebesar 91,67% dan F1-score 0,89 pada kelas positif. Temuan ini menunjukkan bahwa penerapan algoritma machine learning dapat meningkatkan efisiensi seleksi kredit dan mengurangi potensi kerugian akibat kredit bermasalah. Kata kunci: Risiko Kredit; Machine learning; Random Forest; XGBoost; Data mining
creator: Icha Winadya Permadani
creator: Raka Sulistyo
creator: Muhammad Fadli
creator: Erliyan Redy Susanto
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-30
publication date: 2025-08-30
volume: 21
number: 2
starting page: 952
ending page: 959
doi: 10.35889/progresif.v21i2.2909

Perancangan UI/UX Sistem Administrasi Surat Instansi Pemerintah Daerah Menggunakan Metode Design Thinking
type: item
description: The letter administration system plays a crucial role in supporting bureaucratic governance. However, the Forestry Service of UPT KPHP Mentaya Hulu - Seruyan Tengah still faces challenges such as delayed disposition, data duplication, and difficulties in tracking letters. Existing applications have not been fully effective since employees experience adaptation issues caused by suboptimal interface design and user experience. This study aims to design a UI/UX prototype for letter administration system that aligns with user needs, supports digitalization, and enhances management effectiveness. Development employed the Design Thinking method consisting of five stages: empathize, define, ideate, prototype, and test. System evaluation was conducted using the System Usability Scale (SUS) involving 26 respondents from various positions. The results showed an average score of 84.04 in the Excellent category, indicating the system is easy to use, understandable, and acceptable. These findings confirm that a user-centered design approach effectively improves administrative management in government institutions. Key Words: Design Thinking; UI/UX; System Usability Scale; Letter administration; Government institution Abstrak Sistem administrasi surat berperan penting dalam mendukung tata kelola birokrasi. Namun, pada Dinas Kehutanan UPT KPHP Mentaya Hulu, Seruyan Tengah masih ditemukan hambatan berupa keterlambatan disposisi, duplikasi data, serta kesulitan pelacakan surat. Keberadaan aplikasi serupa belum optimal karena pegawai mengalami kendala adaptasi akibat rancangan antarmuka dan pengalaman pengguna UI/UX yang kurang sesuai. Penelitian ini bertujuan merancang prototype UI/UX sistem administrasi surat yang selaras dengan kebutuhan pengguna, mendukung proses digitalisasi, dan meningkatkan efektivitas pengelolaan surat. Pengembangan dilakukan dengan metode Design Thinking yang meliputi lima tahap, empathize, define, ideate, prototype, dan test. Evaluasi sistem menggunakan ins…
creator: Elvina Giovanni Amelinda
creator: Dwi Hosanna Bangkalang
rights: Copyright (c) 2025 Progresif: Jurnal Ilmiah Komputer
date: 2025-08-30
publication date: 2025-08-30
volume: 21
number: 2
starting page: 960
ending page: 974
doi: 10.35889/progresif.v21i2.3199