e-ISSN: 2685-0877
Sistem Monitoring dan Kontrol Suhu Kandang Ayam Kalkun
Turkey farming requires stable temperature and humidity management to maintain optimal growth and health. Manual monitoring often causes delays in handling environmental changes, which can reduce productivity. This study designs and implements a temperature and humidity monitoring and control system for turkey cages based on the Internet of Things (IoT). The system utilizes a NodeMCU ESP8266 microcontroller, a DHT11 sensor, and a relay module to automatically control a heating lamp according to temperature conditions. Data are transmitted to a server and displayed on a web-based dashboard accessible remotely, with additional notifications sent via WhatsApp. The development process applied a prototyping method, including hardware and software design, experiment and black-box testing. The results show that the system successfully maintains cage temperature within the ideal range of 28–32°C, displays real-time temperature and humidity data, and achieved a user satisfaction level of 91.25%. This system is considered effective in assisting farmers to monitor turkey cages more efficiently and responsively. Keywords: Internet of Things; Turkey farming; Temperature monitoring; Humidity; NodeMCU ESP8266. Abstrak Peternakan kalkun membutuhkan pengelolaan suhu dan kelembaban kandang yang stabil agar pertumbuhan dan kesehatan ternak tetap optimal. Pemantauan manual sering menimbulkan keterlambatan dalam penanganan perubahan suhu sehingga berpotensi menurunkan produktivitas. Penelitian ini merancang dan mengimplementasikan sistem monitoring serta kontrol suhu kandang kalkun berbasis Internet of Things (IoT). Sistem memanfaatkan mikrokontroler NodeMCU ESP8266, sensor DHT11, dan modul relay untuk mengendalikan lampu pemanas secara otomatis sesuai kondisi suhu. Data dikirimkan ke server dan ditampilkan pada dashboard web yang dapat diakses jarak jauh, serta dilengkapi notifikasi melalui WhatsApp. Metode pengembangan menggunakan pendekatan prototyping, meliputi perancangan perangkat keras, perangkat lunak, pengujian eksperimen dan pengujian Black box. Hasil pengujian menunjukkan sistem mampu menjaga suhu kandang dalam rentang ideal 28–32°C, menampilkan data suhu dan kelembaban secara real-time, serta memperoleh tingkat kepuasan pengguna sebesar 91,25%. Sistem ini dinilai efektif membantu peternak dalam memantau kondisi kandang kalkun secara lebih efisien dan responsif. Kata kunci: Internet of Things ; Kalkun; Monitoring suhu; Kelembaban; NodeMCU ESP8266.
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Density-Based Spatial Clustering for Assessing Public Service Accessibility in Jombang Regency
The problem of unequal accessibility to public services between subdistricts in Jombang can hinder medium, long-term equitable development. Good accessibility indicates justice and improvements in quality of life in the area. In some subdistricts, school and health facilities are easy to reach, whereas in other areas, mountainous regions or border areas, accessibility is very low. This study aims to map accessibility clusters, analyze the data, and provide recommendations for priority intervention area using DBSCAN. The variables include population density, ratios of health facility, education and administrative facilities per area, geospatial public datasets such as road network density, elevation/slope and rivers. The results show three accessibility clusters (high, medium, and low) with variations in geographical constraints such as hills and major rivers that affect the distribution of services. The visualization shows that low-accessibility areas are generally located in peripheral and mountainous regions, whereas high accessibility is concentrated in the regency center. Keywords: DBSCAN; Spatial clustering; Public service accessibility; Geospatial Abstrak Permasalahan ketimpangan aksesibilitas layanan publik antar kecamatan di Jombang dapat menghambat pemerataan pembangunan jangka menengah dan jangka panjang. Aksesibilitas yang baik mengindikasikan keadilan dan peningkatan kualitas hidup di wilayah tersebut. Di beberapa kecamatan, fasilitas sekolah dan kesehatan mudah dijangkau, sedangkan di daerah lain, pegunungan atau perbatasan, aksesibilitas sangat rendah. Kondisi ini menunjukkan adanya ketimpangan spasial yang perlu dianalisis lebih lanjut. Penelitian ini bertujuan untuk memetakan cluster aksesibilitas, menganalisis data hasil dan memberi rekomendasi lokasi prioritas intervensi. Clustering ini menggunakan metode DBSCAN. Variabel mencakup kepadatan penduduk, rasio fasilitas kesehatan, pendidikan, administrasi per wilayah, geospasial public dataset berupa kepadatan jaringan jalan, hambatan seperti elevasi/lereng dan sungai besar. Hasilnya terdapat tiga klaster aksesibilitas, yaitu tinggi, menengah, dan rendah, dengan variasi hambatan geografis seperti perbukitan dan sungai besar yang memengaruhi distribusi layanan. Visualisasi memperlihatkan bahwa daerah aksesibilitas rendah umumnya berada di wilayah pinggiran dan pegunungan, sedangkan aksesibilitas tinggi terkonsentrasi di pusat kabupaten. Kata kunci: DBSCAN; Spasial clustering; Aksesibilitas layanan public; Geospasial
Penerapan Algoritma Random Forest Berbasis Shap Feature Importance dan GridsearchCV Untuk Deteksi Phishing
The rapid growth of internet users in Indonesia has increased the risk of cyberattacks, particularly phishing. Phishing is a digital fraud attempt that disguises links to resemble official websites in order to steal users’ sensitive information. This study aims to develop a phishing link detection model using a machine learning approach. The dataset consists of 11,430 URL entries from Mendeley Data, including features such as URL length, suspicious symbols, and subdomain levels. The Random Forest algorithm was chosen for its ability to handle high-dimensional data and resist overfitting. Feature selection was performed using SHAP (Shapley Additive Explanations) to assess feature contributions, while model optimization was conducted with GridSearchCV. The best configuration, RF + GS + SHAP Threshold-P10, achieved an accuracy of 0.9650 and an F1-score of 0.9651, producing an accurate, efficient, and interpretable phishing detection model. Keywords: Phishing; Random Forest; GridSearchCV; SHAP; Machine Learning Abstrak Pesatnya pertumbuhan pengguna internet di Indonesia meningkatkan risiko serangan siber, salah satunya phishing. Phishing merupakan upaya penipuan digital dengan menyamarkan tautan agar menyerupai situs resmi untuk mencuri informasi sensitif pengguna. Penelitian ini bertujuan membangun model deteksi tautan phishing menggunakan pendekatan machine learning. Dataset yang digunakan berisi 11.430 entri URL dari Mendeley Data, mencakup fitur seperti panjang URL, simbol mencurigakan, dan tingkat subdomain. Algoritma random forest dipilih karena mampu menangani data berdimensi tinggi serta tahan terhadap overfitting. Seleksi fitur dilakukan dengan SHAP (Shapley Additive Explanations) untuk menilai kontribusi fitur, sedangkan optimasi parameter model menggunakan GridSearchCV. Hasil penelitian menunjukkan konfigurasi RF + GS + SHAP Threshold-P10 memberikan akurasi 0,9650 dan F1-score 0,9651, menghasilkan model yang akurat, efisien, dan transparan dalam mendeteksi tautan phishing. Kata kunci: Phishing; Random Forest; GridSearchCV; SHAP; Machine Learning
Model Arsitektur Multi-Tenant Berbasis Role-Based Access Control pada Sistem Manajemen Waralaba
Franchise Management Systems (FMS) in Micro, Small, and Medium Enterprises (MSMEs) that use a multi-investor model frequently face fundamental issues such as information asymmetry and lack of accountability, mainly due to an inadequate single-owner architecture that hinders investors from validating transaction data. This research applied the Design Science Research (DSR) methodology to design a new FMS architecture model supported by three pillars: a multi-tenant architecture for data isolation, a granular Role-Based Access Control (RBAC) model for separating authority, and a database snapshotting pattern to ensure the integrity of historical data and financial reports. Functional and User Acceptance Testing (UAT) confirmed that the proposed artifact successfully provided real-time data transparency for investors, operational efficiency for branch managers, and centralized monitoring capability for central management. The main contribution of this study is the end-to-end integration of these three pillars, which is crucial for creating verified accountability within the franchise ecosystem. Keywords: Franchise Management System; Multi-Tenant Architecture; Role-Based Access Control; Data Transparency; Design Science Research. Abstrak Sistem manajemen waralaba (FMS) pada UMKM dengan model multi-investor menghadapi masalah fundamental asimetri informasi dan kurangnya akuntabilitas akibat arsitektur single-owner yang tidak memadai, sehingga investor kesulitan memvalidasi data transaksi. Penelitian ini telah menerapkan metodologi Design Science Research (DSR) untuk merancang, mengimplementasikan, dan mengevaluasi sebuah artefak berupa model arsitektur FMS baru. Arsitektur ini ditopang oleh tiga pilar utama: arsitektur multi-tenant untuk isolasi data, model Role-Based Access Control (RBAC) granular untuk memisahkan wewenang, dan pola database snapshotting untuk integritas data historis. Hasil pengujian fungsional dan UAT secara konklusif memvalidasi bahwa artefak yang diusulkan berhasil menyediakan transparansi data real-time bagi investor, efisiensi operasional, dan kapabilitas pemantauan terpusat. Kontribusi utama penelitian ini adalah integrasi end-to-end dari ketiga pilar tersebut, yang krusial untuk menciptakan akuntabilitas yang terverifikasi dalam ekosistem waralaba. Kata kunci: Franchise Management System; Arsitektur Multi-Tenant; Role-Based Access Control; Transparansi Data; Design Science Research.
Pemanfaatan Fitur Tambahan Emosi Untuk Deteksi Hate Speech Media Sosial Bahasa Indonesia
This study examines the importance of incorporatring emotion features and enhancing the temporal robustness of hate-speech detection models to improve classification accuracy. The research aims to analyze the impact of emotion features on an IndoBERT based model and to evaluate the model’s adaptability using an unsupervised self-learning approach. The dataset consists of two corpora, a public dataset from 2019 and twitter data from 2025, each divided into training, validation, and test sets with an 80%, 10%, 10% split. Model performance is evaluated using accuracy, precision, recall and F1-score calculated from confusion matrix. Experimental results show that adding emotion features increases accuracy by 1-2% across all scenarios. In cross-temporal testing, the supervised model performance declines duet o linguistic shifts whereas the self-learning method improves accuracy up to 77.67%. These findings indicate that emotion features and self-learning effectively enhance the model’s ability to adapt to evolving language and social context. Keyword: Emotion; Hate speech detection; IndoBERT Abstrak Penelitian ini membahas pentingnya penambahan fitur emosi dan peningkatan ketahanan model deteksi ujaran kebencian terhadap perubahan bahasa lintas waktu guna memperkuat akurasi klasifikasi. Tujuan penelitian adalah menganalisis pengaruh fitur emosi pada model berbasis IndoBERT dan mengevaluasi kemampuan adaptasi model menggunakan pendekatan unsupervised self-learning. Data menggunakan dua korpus yaitu dataset publik tahun 2019 dan data Twitter tahun 2025, yang masing-masing dibagi menjadi data latih dan data latih, validasi, dan uji dengan proporsi 80%, 10%, dan 10%. Model dievaluasi menggunakan accuracy, precision, recall, dan F1-score yang dihitung melalui confusion matrix. Hasil pengujian menunjukkan bahwa penambahan fitur emosi meningkatkan akurasi sebesar 1-2% di seluruh skenario. Pada pengujian lintas waktu, performa model supervised menurun akibat perubahan konteks linguistik, namun metode self-learning meningkatkan akurasi hingga 77.67%. temuan ini menunjukkan bahwa fitur emosi dan self-learning efektif meningkatkan adaptasi model terhadap dinamika bahasa serta konteks sosial. Kata kunci: Seteksi ujaran kebencian; Emosi; IndoBERT
Prediksi Safety Stock Menggunakan Algoritma Support Vector Regression Dengan Optimasi Hyperparameter
Inventory management plays an essential role in ensuring smooth supply chain operations and preventing both stockouts and overstocking. One effective approach to determining the appropriate level of safety stock is the use of machine learning–based prediction methods. This study aims to predict safety stock values using the Support Vector Regression (SVR) method with hyperparameter optimization through GridSearch. The dataset used is a public bike rental dataset, which includes variables such as time, weather, season, and holidays. The research stages include data preprocessing, Exploratory Data Analysis (EDA), implementation of the SVR model, and model performance evaluation using MAE, MAPE, and R² metrics. The performance of the Support Vector Regression (SVR) algorithm with a Radial Basis Function (RBF) kernel and optimal parameters demonstrates strong predictive accuracy. Based on the Mean Absolute Percentage Error (MAPE), the model achieved prediction accuracies of 83.8%, 80.1%, and 81.7% on the training, validation, and testing datasets, respectively, indicating its effectiveness in modeling non-linear data. This model is capable of generating more precise safety stock predictions, thereby supporting decision-making in inventory planning and reducing the risk of stock shortages. Keywords: Safety Stock; Support Vector Regression; Demand Prediction; Hyperparameter Tuning Abstrak Manajemen persediaan memiliki peran penting dalam menjaga kelancaran rantai pasok dan menghindari kekurangan maupun kelebihan stok. Salah satu pendekatan yang efektif untuk menentukan jumlah safety stock adalah dengan memanfaatkan metode prediksi berbasis machine learning. Penelitian ini bertujuan untuk memprediksi nilai safety stock menggunakan metode Support Vector Regression (SVR) dengan optimasi hyperparameter tuning melalui teknik GridSearch. Data yang digunakan merupakan dataset publik penyewaan sepeda yang mencakup variabel waktu, cuaca, musim, dan hari libur. Tahapan penelitian meliputi preprocessing data, analisis Exploratory Data Analysis (EDA), penerapan model SVR, serta evaluasi kinerja model menggunakan metrik MAE, MAPE, dan R². Hasil penelitian menunjukkan bahwa model SVR dengan kernel RBF dan parameter optimal menghasilkan tingkat akurasi prediksi yang baik. Berdasarkan nilai MAPE, model mencapai akurasi 83,8% pada data latih, 80,1% pada data validasi, dan 81,7% pada data uji, yang mengindikasikan kemampuan SVR dalam memodelkan data non-linear. Model ini mampu memprediksi kebutuhan safety stock lebih baik, sehingga dapat membantu pengambilan keputusan dalam perencanaan persediaan dan mengurangi risiko kekurangan stok. Kata kunci: Safety Stock; Support Vector Regression; Prediksi Permintaan; Hyperparameter Tuning
Klasifikasi Mutu Biji Kopi Menggunakan Metode CNN-SVM Berdasarkan Cacat Fisik dan Warna
The determination of coffee bean quality in Indonesia is generally still done manually based on physical defects and color, which is subjective and time-consuming. This study aims to develop a digital image-based green coffee bean quality classification model using the Convolutional Neural Network and Support Vector Machine (CNN-SVM) method. CNN is used as a feature extractor with a ResNet-50 architecture, while SVM functions as a classifier using a Radial Basis Function (RBF) kernel. The dataset consists of 10 classes of coffee bean defects and is divided into 80% training data and 20% test data. The test results show an accuracy value of 77.68%, precision of 80.04%, recall of 77.15%, and f1-score of 77.47%. This approach proves that the combination of CNN and SVM can improve the accuracy and stability of the model. This finding is a novelty in the development of an efficient and objective artificial intelligence-based automatic coffee quality sorting system. Keywords: Coffee Bean Classification; CNN-SVM; ResNet-50 Abstrak Penentuan mutu biji kopi di Indonesia umumnya masih dilakukan secara manual berdasarkan cacat fisik dan warna, yang bersifat subjektif dan memerlukan waktu lama. Penelitian ini bertujuan untuk mengembangkan model klasifikasi mutu biji kopi hijau berbasis citra digital menggunakan metode Convolutional Neural Network dan Support Vector Machine (CNN-SVM). CNN digunakan untuk ekstraksi fitur dengan arsitektur ResNet-50, sedangkan SVM berfungsi untuk klasifikasi menggunakan kernel Radial Basis Function (RBF). Dataset terdiri dari 10 kelas cacat biji kopi dan dibagi menjadi 80% data latih serta 20% data uji. Hasil pengujian menunjukkan nilai akurasi sebesar 77,68%, presisi 80,04%, recall 77,15%, dan f1-score 77,47%. Pendekatan ini membuktikan bahwa kombinasi CNN dan SVM mampu meningkatkan akurasi dan stabilitas model. Temuan ini menjadi kebaruan dalam pengembangan sistem sortasi mutu kopi otomatis yang efisien dan objektif berbasis kecerdasan buatan. Kata kunci: Klasifikasi Biji Kopi; CNN-SVM; ResNet-50
Analisis Kesuksesan Video Game Simulasi Depresi dan Kekeluargaan Menggunakan Model D&M ISS
Depression is a critical mental health problem that is often difficult to understand empathetically, creating a need for interactive media to communicate psychological experiences. This study analyzes the success of a simulation video game addressing depression and family themes using the DeLone and McLean Information System Success Model. A quantitative experimental method involved 124 participants aged 18–21 who played Ruang Hampa and completed questionnaires based on their gameplay experience. The variables examined included System Quality, Information Quality, Service Quality, Intention to Use, User Satisfaction, and Net Benefits, analyzed using multiple linear regression. The study reveals that Information Quality and Service Quality significantly influence Intention to Use, which subsequently increases Net Benefits. In contrast, System Quality and User Satisfaction do not have a significant direct effect. These findings indicate that narrative and informational elements exert a greater impact than technical system aspects in achieving the success of psychologically themed educational games. Keywords: Video Game; Depression Simulation; Family; DeLone & McLean Model; Mental Health Abstrak Depresi merupakan isu kesehatan mental yang serius dan masih sulit dipahami secara empatik oleh masyarakat, sehingga diperlukan media interaktif untuk menyampaikan pengalaman psikologis. Penelitian ini bertujuan untuk menganalisis kesuksesan video game simulasi bertema depresi dan kekeluargaan menggunakan model DeLone & McLean. Metode eksperimen kuantitatif melibatkan 124 responden berusia 18–21 tahun yang memainkan video game Ruang Hampa dan mengisi kuesioner sesuai pengalaman bermain. Variabel yang dianalisis mencakup Kualitas Sistem, Kualitas Informasi, Kualitas Layanan, Niat Penggunaan, Kepuasan Pengguna, dan Manfaat Bersih, dengan analisis regresi linier berganda. Penelitian ini menemukan bahwa Kualitas Informasi dan Kualitas Layanan berpengaruh positif signifikan terhadap Niat Penggunaan yang kemudian meningkatkan Manfaat Bersih. Sebaliknya, Kualitas Sistem tidak menunjukkan pengaruh signifikan, dan Kepuasan Pengguna tidak berpengaruh langsung terhadap Manfaat Bersih. Simpulan penelitian ini menunjukkan bahwa aspek naratif dan informasional lebih menentukan keberhasilan game edukatif bertema psikologis dibandingkan aspek teknis sistem. Kata kunci: Video Game; Simulasi Depresi; Kekeluargaan; Model DeLone & McLean; Kesehatan Mental
Web Perizinan Tata Ruang dan Pemanfaatan Lahan Dinas PUPR Kudus
Spatial planning and land-use permit services at the Public Works and Spatial Planning Office (Dinas PUPR) of Kudus Regency still encounter obstacles due to manual procedures, including lengthy processing time, limited public access to information, and inefficient archive management. This study aims to develop a web-based spatial planning permit information system that supports permit submission, document management, verification and validation processes, and online monitoring of application status. The research employs a Research and Development method using the Rapid Application Development approach, implemented through requirement analysis, system design, prototype development, black box testing, User Acceptance Testing, and system refinement based on user feedback. System modeling is conducted using Data Flow Diagrams and Entity Relationship Diagrams to ensure alignment between functional requirements, process flows, and database structure. The results indicate that the developed system improves service efficiency, data organization, and transparency of the permitting process, while also enhancing accessibility of information for both the public and administrative staff. The system contributes to the improvement of digital-based public service quality within local government institutions. Keywords: Information system; Spatial planning permit; Land use; Rapid Application Development; Public service Abstrak Pelayanan perizinan tata ruang dan pemanfaatan lahan di Dinas PUPR Kabupaten Kudus masih mengalami hambatan akibat penggunaan prosedur manual, antara lain keterlambatan proses, keterbatasan akses informasi bagi masyarakat, serta pengelolaan arsip yang kurang efisien. Penelitian ini bertujuan mengembangkan sistem informasi perizinan tata ruang berbasis website yang mampu mendukung pengajuan izin, pengelolaan dokumen persyaratan, proses verifikasi dan validasi, serta pemantauan status permohonan secara daring. Metode yang digunakan adalah Research and Development dengan pendekatan Rapid Application Development, yang dilaksanakan melalui tahapan analisis kebutuhan, perancangan sistem, pengembangan prototipe, pengujian black box dan User Acceptance Test, serta penyempurnaan sistem berdasarkan masukan pengguna. Pemodelan sistem dilakukan menggunakan Data Flow Diagram dan Entity Relationship Diagram untuk memastikan kesesuaian fungsi dan struktur data. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu meningkatkan efisiensi pelayanan, keteraturan pengelolaan data, transparansi proses perizinan, serta kemudahan akses informasi bagi masyarakat dan pegawai, sehingga mendukung peningkatan kualitas layanan publik berbasis digital. Kata kunci: Sistem informasi; Perizinan tata ruang; Pemanfaatan lahan; Rapid Application Development; Pelayanan publik
Model Aplikasi Pemesanan Menu Pada Coffee Shop Can Ngopi
Manual ordering at Can Ngopi Coffee Shop often leads to communication errors and irregular queues, which necessitates the digitization of the service. This research aims to develop a website-based menu ordering application to improve operational efficiency. The system development used the Waterfall method, which encompasses the analysis, design, construction, and maintenance phases. Functionality validation was carried out through Black Box Testing. The test results showed that all the functionalities, such as the digital catalog and the shopping cart system, functioned effectively as required. The conclusion of the research confirms that this application effectively speeds up the transaction process, minimizes miscommunication between customers and staff, and facilitates management in monitoring sales reports in a structured manner. Keywords: Menu Ordering Application; Coffee Shop; Waterfall; Black Box Testing Abstrak Pemesanan manual di Coffee Shop Can Ngopi sering memicu kesalahan komunikasi dan antrean tidak teratur, sehingga diperlukan digitalisasi layanan. Penelitian ini bertujuan membangun aplikasi pemesanan menu berbasis website untuk meningkatkan efisiensi operasional. Pengembangan sistem menggunakan metode Waterfall, meliputi tahap analisis, desain, konstruksi, hingga pemeliharaan. Validasi fungsionalitas fitur dilakukan melalui metode Black Box Testing. Hasil pengujian menunjukkan seluruh fitur, seperti katalog digital dan sistem keranjang, berjalan valid sesuai kebutuhan. Simpulan penelitian menegaskan bahwa aplikasi ini efektif mempercepat proses transaksi, meminimalisir miskomunikasi antara pelanggan dan staf, serta mempermudah manajemen dalam pemantauan laporan penjualan secara terstruktur. Kata kunci: Aplikasi Pemesanan Menu; Coffee Shop; Waterfall; Black Box Testing
Penerapan Algoritma Decision Tree dalam Menentukan Kelayakan Penerima Bantuan Program Keluarga Harapan
Determining the eligibility of beneficiaries for the Family Hope Program still faces targeting inaccuracies due to limited data analysis and subjective decision-making. This study aims to develop a Decision Tree–based classification model to objectively determine Family Hope Program eligibility using data-driven approaches. The dataset includes socio-economic attributes such as income, DTKS status, school-age children, pregnant or breastfeeding mothers, elderly members, disabilities, and occupation. The data were processed through cleaning, transformation, and splitting into training and testing sets. Experimental results show that the model achieved a highest accuracy of 96.00%, along with high precision and recall values. The resulting decision tree structure also provides interpretable rules to support decision-making. These findings demonstrate that the Decision Tree method is effective in improving the accuracy and transparency of Family Hope Program beneficiary selection. Keywords: Decision Tree; Family Hope Program; Classification; Data Mining; Social Assistance Abstrak Penentuan kelayakan penerima Program Keluarga Harapan masih menghadapi permasalahan ketidaktepatan sasaran akibat keterbatasan analisis data dan subjektivitas dalam pengambilan keputusan. Penelitian ini bertujuan mengembangkan model klasifikasi berbasis Decision Tree untuk menentukan kelayakan penerima Program Keluarga Harapan secara objektif dan berbasis data. Dataset yang digunakan mencakup atribut sosial ekonomi seperti pendapatan, status DTKS, usia sekolah, ibu hamil atau menyusui, lansia, disabilitas, dan pekerjaan. Data diproses melalui tahap pembersihan, transformasi, dan pembagian data latih dan uji. Hasil pengujian menunjukkan bahwa model mencapai akurasi tertinggi sebesar 96,00%, dengan presisi dan recall yang tinggi. Struktur pohon keputusan juga menghasilkan aturan yang mudah diinterpretasikan sebagai dasar pengambilan keputusan. Hasil ini membuktikan bahwa Decision Tree efektif untuk meningkatkan ketepatan dan transparansi penyaluran bantuan Program Keluarga Harapan. Kata kunci: Decision Tree; Program Keluarga Harapan; Klasifikasi; Data Mining; Bantuan Sosial
Klasifikasi Penulisan Huruf Hijaiyah Menggunakan Algoritma Convolutional Neural Network Pada TPQ I’anatut Tholibin
This research was conducted due to difficulties in recognizing handwritten Hijaiyah letters at TPQ I’anatut Tholibin caused by variations in writing styles and similarities among letters. The study aims to develop a handwritten Hijaiyah letter classification system based on a Convolutional Neural Network (CNN) using the MobileNetV2 architecture. The research employed a Research and Development (R&D) approach, including real-time data collection from students’ handwritten samples, image preprocessing (resizing to 224×224, pixel normalization, and augmentation), model design using transfer learning, training, and testing. Model evaluation was performed using test data that were not involved in the training process, with performance assessed through a confusion matrix and metrics such as accuracy, precision, recall, and F1-score. The experimental results show that the model achieved an accuracy of 78.46% with a macro F1-score of 77.29%, indicating a reasonably good and balanced classification performance across classes. The system was implemented as a web-based application supporting real-time testing through direct writing on a digital canvas, enabling interactive classification. These findings demonstrate that MobileNetV2 is effective for handwritten Hijaiyah letter classification and has potential as an intelligent learning support tool. Keywords: Hijaiyah letters; Convolutional neural network; MobileNetV2; Image classification; Real-time system Abstrak Penelitian ini dilakukan karena pengenalan tulisan tangan huruf hijaiyah di TPQ I’anatut Tholibin masih terkendala variasi bentuk tulisan dan kemiripan antar huruf. Penelitian ini bertujuan mengembangkan sistem klasifikasi tulisan tangan huruf hijaiyah berbasis Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2. Metode yang digunakan adalah Research and Development (R&D) dengan tahapan pengumpulan data tulisan tangan murid secara real-time, pra-pengolahan citra (resizing 224×224, normalisasi piksel, dan augmentasi), perancangan model dengan pendekatan transfer learning, pelatihan, dan pengujian. Pengujian dilakukan menggunakan data uji yang tidak dilibatkan dalam proses pelatihan, dengan evaluasi performa menggunakan confusion matrix dan metrik akurasi, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model mencapai akurasi sebesar 78,46% dengan nilai macro F1-score 77,29%, yang menandakan performa klasifikasi yang cukup baik dan relatif seimbang antar kelas. Sistem diimplementasikan dalam aplikasi web dengan pengujian real-time melalui penulisan langsung pada kanvas digital sehingga klasifikasi dapat dilakukan secara interaktif. Temuan ini menunjukkan MobileNetV2 efektif untuk klasifikasi huruf hijaiyah tulisan tangan dan berpotensi sebagai alat bantu pembelajaran. Kata Kunci: Huruf hijaiyah; Convolutional neural network; MobileNetV2; Klasifikasi citra; Sistem realtime
Prediksi Kelayakan Kredit Nasabah Dengan Penerapan Cost-Sensitive Random Forest
The high risk of credit default and class imbalance in customer data pose major challenges in developing accurate credit scoring systems. This condition causes predictive models to be biased toward the majority class, thereby reducing the ability to detect high-risk borrowers. This study develops a credit scoring model for imbalanced data using the Synthetic Minority Oversampling Technique (SMOTE) and cost-sensitive Random Forest with hyperparameter optimization via GridSearchCV. The dataset consists of 32,581 customer records. Experimental results show that the best configuration with n_estimators = 200 achieves a cross-validation F1-score of 0.813750. On the test data, the model attains an accuracy of 0.927267, precision of 0.911458, recall of 0.738397, and an F1-score of 0.815851, indicating improved and more balanced detection of high-risk borrowers. Keywords: Random Forest; SMOTE; Cost-sensitive learning; GridSearchCV Abstrak Tingginya risiko gagal bayar kredit dan ketidakseimbangan kelas pada data nasabah menjadi tantangan utama dalam pengembangan sistem credit scoring yang akurat. Kondisi ini menyebabkan model prediksi cenderung bias terhadap kelas mayoritas sehingga kemampuan deteksi debitur berisiko menjadi kurang optimal. Penelitian ini mengembangkan model credit scoring pada data tidak seimbang menggunakan Synthetic Minority Oversampling Technique (SMOTE) dan Cost-Sensitive Random Forest dengan optimasi hyperparameter GridSearchCV. Dataset yang digunakan berjumlah 32.581 data nasabah. Hasil pengujian menunjukkan konfigurasi terbaik dengan n_estimators = 200 menghasilkan F1-score validasi silang sebesar 0,813750. Pada data uji, model mencapai akurasi 0,927267, precision 0,911458, recall 0,738397, dan F1-score 0,815851, yang menunjukkan peningkatan kemampuan deteksi debitur berisiko secara lebih seimbang. Kata kunci: Random Forest; SMOTE; Cost-sensitive learning; GridSearchCV.
Pengembangan Aplikasi Sistem Pemesanan Digital Berbasis QR Barcode Menggunakan Metode Prototyping
Digital transformation at Can Ngopi Coffee Shop is urgently needed to address queue bottlenecks and manual recording errors in traditional systems. This study aims to analyze the implementation of a QR Barcode-based digital ordering system to enhance service efficiency. System development utilizes the Prototyping method, encompassing analysis, design, construction, and testing phases. Validation was conducted through User Acceptance Testing (UAT) and essay questionnaires for customers and managers. Testing results indicate that all functional features of the system, integrated with Point of Sales (POS), were accepted and functioned optimally. The system is proven effective in accelerating transaction processes, minimizing data errors, and improving the service quality of culinary MSMEs in the digital era. Keywords: QR Barcode; Prototyping; Service Efficiency; MSMEs, Point of Sales.
Pengembangan Sistem Manajemen Indekos Griya Indira Terintegrasi WhatsApp Gateway dan Fitur Komunitas
The reliance on manual recording in the operational management of Griya Indira has resulted in data redundancy, a high incidence of late payments, and uneven information distribution among residents. This study proposes the development of an integrated boarding house management system that synergizes the WhatsApp Gateway feature for automatic notifications with a digital community module. Utilizing the Rapid Application Development (RAD) method and Black Box Testing for functional verification, the system is designed to address administrative inefficiencies. The findings indicate that the implementation of Cron Job scheduling successfully transmits bill reminders in real-time and accurately validates digital payments. Based on functional testing results, the system is declared valid in executing centralized database mechanisms, sending automatic bill notifications on schedule, and facilitating single-door information distribution through community features according to design requirements. Keywords: Boarding House Management System; WhatsApp Gateway; Rapid Application Development; Auto-Reminder. Abstrak Ketergantungan pada pencatatan manual dalam pengelolaan operasional Griya Indira berdampak pada redundansi data, tingginya insiden keterlambatan pembayaran, serta ketidakmerataan distribusi informasi kepada penghuni. Penelitian ini mengusulkan pengembangan sistem manajemen kos terintegrasi yang menyinergikan fitur WhatsApp Gateway untuk notifikasi otomatis dengan modul komunitas digital. Melalui pendekatan metode Rapid Application Development (RAD) dan verifikasi fungsional Black Box Testing, sistem dirancang untuk menangani inefisiensi administrasi. Temuan penelitian menunjukkan bahwa implementasi penjadwalan Cron Job berhasil mengirimkan pengingat tagihan secara real-time dan memvalidasi pembayaran digital secara akurat. Berdasarkan hasil pengujian fungsional, sistem dinyatakan valid dalam menjalankan mekanisme sentralisasi basis data, pengiriman notifikasi tagihan otomatis sesuai jadwal, serta memfasilitasi distribusi informasi satu pintu melalui fitur komunitas sesuai rancangan kebutuhan. Kata kunci: Sistem Manajemen Indekos; WhatsApp Gateway; Rapid Application Development; Auto-Reminder
Model Alat Pemantau Pemakaian Daya Listrik Dengan Webpage MySQL Menggunakan ESP8266
Real-time monitoring of electric power usage is very important to increase energy efficiency and reduce electricity waste. This research aims to design and implement a model for monitoring electrical power consumption with a MySQL webpage using the ESP8266 module. This system utilizes current and voltage sensors to calculate power consumption, then the data is sent via the Wi-Fi network to the server and stored in a database to be displayed in graphic form on a webpage. The test results show that the system is able to display real-time power usage data using both the LCD display and the webpage to get the same results, while testing with the Buzzer can provide a warning if the available credit is less than IDR 5,000. Keywords: Webpage; MySQL; ESP8266; Electrical Energy Abstrak Pemantauan penggunaan daya listrik secara real-time sangat penting untuk meningkatkan efisiensi energi dan mengurangi pemborosan listrik. Penelitian ini bertujuan merancang dan mengimplementasikan model alat pemantau pemakaian daya listrik dengan webpage MySQL menggunakan modul ESP8266. Sistem ini memanfaatkan sensor arus dan tegangan untuk menghitung konsumsi daya, kemudian data dikirim melalui jaringan Wi-Fi ke server dan disimpan dalam database untuk ditampilkan dalam bentuk grafik pada webpage. Hasil pengujian menunjukkan bahwa sistem mampu menampilkan data pemakaian daya secara real-time baik dengan tampilan LCD maupun webpage mendapatkan hasil yang sama sedangkan pengujian dengan Buzzer bisa memberikan peringatan jika pulsa yang tersedia kurang dari Rp 5.000. Kata kunci: Webpage; MySQL; ESP8266; Energi Listrik
Implementasi Backpropagation Neural Network pada Sistem Electronic Nose untuk Klasifikasi Aroma Teh
Conventional tea aroma quality assessment is subjective and slow. This study aims to design and implement an Arduino Uno-based automatic Electronic Nose (e-nose) system with a TGS sensor array (880, 826, 822, 825) combined with a Backpropagation Neural Network (BPNN) for tea aroma classification. The method includes signal acquisition, normalization, feature extraction, and sensor correlation analysis to form a chemical fingerprint before modeling. Testing with a confusion matrix on three types of tea (black, green, and jasmine) showed performance with an accuracy of 0.71, precision of 0.71, recall of 0.72, and f-measure of 0.71. The results of this study provide an objective, fast, economical, and non-destructive aroma evaluation method and contribute to the development of smart sensor technology to support the competitiveness of Indonesian tea products. The main novelty of this study is the integration of sensor correlation analysis into the modeling pipeline with an end-to-end classification system that combines sensor correlation analysis to optimize the performance of the BPNN model on the tea aroma dataset. Keywords: Arduino; Tea Aroma; Backpropagation; Electronic Nose; TGS Sensor Abstrak Penilaian mutu aroma teh secara konvensional bersifat subjektif dan lambat. Penelitian ini bertujuan merancang dan mengimplementasikan sistem Electronic Nose (e-nose) otomatis berbasis Arduino Uno dengan array sensor TGS (880, 826, 822, 825) yang dikombinasikan Backpropagation Neural Network (BPNN) untuk klasifikasi aroma teh. Metode mencakup akuisisi sinyal, normalisasi, ekstraksi fitur, dan analisis korelasi sensor untuk membentuk chemical fingerprint sebelum pemodelan. Pengujian dengan confusion matrix pada tiga jenis teh (hitam, hijau, wangi melati) menunjukkan performa dengan akurasi 0,71, presisi 0,71, recall 0,72, dan f-measure 0,71. Hasil penelitian memberikan metode evaluasi aroma yang objektif, cepat, ekonomis, dan non destruktif, serta berkontribusi pada pengembangan teknologi sensor cerdas untuk mendukung daya saing produk teh Indonesia. Kebaruan utama penelitian ini adalah pada integrasi analisis korelasi sensor ke dalam pipeline pemodelan dengan sistem klasifikasi end-to-end yang menggabungkan analisis korelasi sensor untuk mengoptimalkan performa model BPNN pada dataset aroma teh. Kata kunci: Arduino; Aroma Teh; Backpropagation; Electronic Nose; Sensor TGS
Rancang Bangun Dashboard Monitoring Pengiriman Bermasalah pada E-Commerce
The rapid growth of e-commerce has increased the volume of orders that must be monitored by sellers, making the handling of delivery delays a major challenge. This study designs and develops a dashboard to monitor problematic shipments on an e-commerce platform by applying a Service Level Agreement (SLA) approach as the basis for evaluating delivery delays. The research adopts a Research and Development (R&D) method with the Waterfall system development model, which includes the stages of requirement analysis, system design, implementation, and functional testing. The developed dashboard is able to automatically classify shipment statuses based on SLA calculations, display indicators of orders at risk of delay, and present delivery data through graphical visualizations. The testing results indicate that the system successfully presents real-time delivery data, proactively detects potential delays, and supports sellers in making operational decisions. This system provides a more effective and measurable SLA-based approach for monitoring e-commerce deliveries. Keywords: Monitoring dashboard; Problematic delivery; Service Level Agreement; E-commerce; Automatic detection. Abstrak Perkembangan e-commerce telah meningkatkan volume pesanan yang harus dipantau oleh seller, sehingga penanganan keterlambatan pengiriman menjadi tantangan utama. Penelitian ini merancang dan membangun sebuah dashboard untuk memonitor pengiriman bermasalah pada platform e-commerce dengan pendekatan Service Level Agreement (SLA) sebagai dasar penilaian keterlambatan pengiriman. Metode yang digunakan adalah Research and Development (R&D) dengan model pengembangan sistem Waterfall, yang meliputi tahapan analisis kebutuhan, perancangan sistem, implementasi, dan pengujian fungsionalitas. Dashboard yang dikembangkan mampu mengklasifikasikan status pengiriman secara otomatis berdasarkan perhitungan SLA, menampilkan indikator pesanan berisiko terlambat, serta menyajikan grafik visualisasi pengiriman. Hasil pengujian menunjukkan bahwa sistem berhasil menampilkan data pengiriman secara real-time, mendeteksi potensi keterlambatan secara proaktif, dan membantu seller dalam pengambilan keputusan operasional. Sistem ini memberikan pendekatan monitoring pengiriman yang lebih efektif dan terukur berbasis SLA. Kata kunci: Dashboard monitoring; Pengiriman bermasalah; Service Level Agreement; E-commerce; Deteksi otomatis
Penerapan Content-Based Filtering dan K-Nearest Neighbor pada Sistem Rekomendasi Wisata Probolinggo
Probolinggo Regency has a wide range of tourism destinations; however, the limited dissemination of tourism information makes it difficult for tourists to determine suitable travel destinations. This study aims to develop a tourism destination recommendation system to assist users in obtaining recommendations based on their preferences. The system is developed using the Content-Based Filtering (CBF) method with TF-IDF weighting and cosine similarity, as well as the K-Nearest Neighbor (KNN) algorithm based on cosine distance. The system process includes tourism data collection, text feature extraction and weighting, similarity calculation, and the presentation of main destination recommendations and similar destinations. System evaluation using precision and recall metrics shows that the Content-Based Filtering (CBF) and K-Nearest Neighbor (KNN) methods achieve precision values of 93.75% and 96.00%, respectively, with both methods obtaining a recall value of 100%, indicating that the system is able to provide relevant tourism destination recommendations. Keywords: Recommendation System; Probolinggo Tourism; Content-Based Filtering; TF-IDF; K-Nearest Neighbor; Cosine Similarity. Abstrak Kabupaten Probolinggo memiliki potensi destinasi wisata yang beragam, namun penyebaran informasi wisata yang belum optimal menyulitkan wisatawan dalam menentukan tujuan perjalanan. Penelitian ini bertujuan mengembangkan sistem rekomendasi destinasi wisata untuk membantu pengguna memperoleh rekomendasi sesuai preferensi. Sistem dikembangkan menggunakan metode Content-Based Filtering (CBF) dengan pembobotan TF-IDF dan cosine similarity serta algoritma K-Nearest Neighbor (KNN) berbasis cosine distance. Proses sistem meliputi pengumpulan data destinasi wisata, ekstraksi dan pembobotan fitur teks, perhitungan tingkat kemiripan, serta penyajian rekomendasi destinasi utama dan destinasi serupa. Pengujian sistem menggunakan metrik precision dan recall menunjukkan bahwa metode content-based filtering (CBF) dan K-Nearest Neighbor (KNN) menghasilkan nilai precision masing-masing sebesar 93,75% dan 96,00%, dengan nilai recall keduanya mencapai 100%, sehingga sistem mampu memberikan rekomendasi destinasi wisata yang relevan. Kata kunci: Sistem Rekomendasi; Pariwisata Probolinggo; Content-Based Filtering; TF-IDF; K-Nearest Neighbor; Cosine Similarity.
CNN-Based Automatic Detection of Corn Leaf Diseases Using Desktop GUI Application
Early detection of corn leaf diseases plays a crucial role in improving agricultural productivity. This study develops an automatic detection system for corn leaf diseases using a Convolutional Neural Network (CNN) implemented in a desktop-based GUI application. The model was developed using a transfer learning approach with a dataset of 2,400 corn leaf images divided into four classes: Blight, Common Rust, Gray Leaf Spot, and Healthy. The research stages included image preprocessing, model training and evaluation, and implementation of the trained model into an offline desktop application. Experimental results show that the CNN model achieved an accuracy of 90.00%, with precision of 90.02%, recall of 90.00%, and an F1-score of 90.00%. The Healthy class demonstrated the best performance, while the Blight class showed the lowest but remained in a good category. The developed system enables fast, practical, and efficient disease detection and has the potential to support objective early diagnosis in agriculture. Keywords: CNN; Deep Learning; Disease Detection; Corn Leaf; Transfer Learning Abstrak Deteksi dini penyakit daun jagung berperan penting dalam meningkatkan produktivitas pertanian. Penelitian ini mengembangkan sistem deteksi otomatis penyakit daun jagung menggunakan metode Convolutional Neural Network (CNN) berbasis aplikasi GUI desktop. Model dikembangkan dengan pendekatan transfer learning menggunakan 2.400 citra daun jagung yang terbagi dalam empat kelas, yaitu Blight, Common Rust, Gray Leaf Spot, dan Healthy. Tahapan penelitian meliputi preprocessing citra, pelatihan dan evaluasi model, serta implementasi model ke dalam aplikasi desktop yang dapat digunakan tanpa koneksi internet. Hasil pengujian menunjukkan akurasi sebesar 90,00%, precision 90,02%, recall 90,00%, dan F1-score 90,00%. Kelas Healthy memiliki performa terbaik, sedangkan kelas Blight terendah namun tetap dalam kategori baik. Implementasi sistem memungkinkan proses deteksi dilakukan secara cepat, praktis, dan efisien. Sistem yang dikembangkan berpotensi mendukung deteksi penyakit secara objektif serta membantu meningkatkan produktivitas pertanian. Kata kunci: CNN; Deep Learning; Deteksi Penyakit; Daun Jagung; Transfer Learning
Penerapan Explainable AI dan Model Stacking Untuk Mengindentifikasi Faktor Risiko Stunting Balita
Stunting remains a nutritional problem in children that is difficult to detect early due to its multifactorial causes and the limitations of conventional analytical approaches. This study aims to develop a stacking-based Machine Learning model for stunting prediction and to implement Explainable Artificial Intelligence (XAI) to interpret the risk factors influencing the prediction outcomes. The methods employed include Decision Tree, Random Forest, and Support Vector Machine (SVM) as base learners, and Logistic Regression as the meta-learner in an ensemble stacking framework, with performance evaluated using accuracy, precision, recall, and F1-score. The results indicate that the Decision Tree algorithm and the Stacking method achieved the best performance with 100% accuracy, while the XAI analysis identified current body weight, birth weight, and age as the primary factors influencing stunting prediction. Keywords: Stunting; Stacking; Machine Learning; Explainable Artificial Intelligence Abstrak Stunting masih menjadi permasalahan gizi pada anak yang sulit dideteksi secara dini karena dipengaruhi oleh berbagai faktor dan keterbatasan analisis konvensional. Penelitian ini bertujuan mengembangkan model stacking berbasis Machine Learning untuk prediksi stunting serta menerapkan Explainable Artificial Intelligence (XAI) dalam menginterpretasikan faktor-faktor risiko yang berpengaruh terhadap hasil prediksi. Metode yang digunakan meliputi algoritma Decision Tree, Random Forest, Support Vector Machine (SVM) sebagai base learner dan Logistic Regression sebagai meta learner pada ensemble stacking, dengan evaluasi menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Decision Tree dan penerapan metode Stacking menghasilkan kinerja terbaik dengan tingkat akurasi 100%, sementara analisis XAI mengidentifikasi bahwa berat badan, BB lahir, dan usia merupakan faktor utama yang memengaruhi prediksi stunting. Kata kunci: Stunting; Stacking; Machine Learning; Explainable Artificial Intelligence
Klasifikasi Motif Kain Batik Nitik Menggunakan Support Vector Machine dengan Ekstraksi Fitur EfficientNet-B0
Nitik Batik is an Indonesian cultural heritage with complex geometric dot patterns, yet its digitalization and preservation efforts remain limited. This study aims to develop an automatic classification system for 60 Nitik Batik motifs using a combination of EfficientNet-B0 as a feature extractor and Support Vector Machine (SVM) as a classifier. The Batik Nitik 960 Dataset was expanded from 960 to 1,920 images with rotation augmentation. Experiments were conducted with 10-fold cross-validation and evaluation on separate test data. Results show that the model without augmentation achieved 55.71% accuracy, 90.06% macro precision, 55.71% recall, and 65.29% F1-score. Blur augmentation with 30% probability reduced accuracy to 49.29% although it decreased overfitting by 6.63%. SVM parameters were set to C=0.3 and gamma=0.01 to improve regularization. This study concludes that the combination of EfficientNet-B0 and SVM is effective for multi-class batik classification, but blur augmentation is unsuitable for detail-rich textile data. Future research recommendations include exploring geometric augmentation and more advanced feature extractor architectures. Kata kunci: Nitik Batik; Image classification; EfficientNet-B0; Support Vector Machine; augmentation. Abstrak Batik Nitik merupakan warisan budaya Indonesia dengan motif geometris berbentuk titik yang kompleks, namun upaya digitalisasi dan pelestariannya masih terbatas. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis untuk 60 motif Batik Nitik menggunakan kombinasi EfficientNet-B0 sebagai ekstraktor fitur dan Support Vector Machine (SVM) sebagai klasifikator. Dataset Batik Nitik 960 Dataset diperluas dari 960 menjadi 1.920 citra dengan augmentasi rotasi. Eksperimen dilakukan dengan skema 10-fold cross-validation dan evaluasi pada data uji terpisah. Hasil menunjukkan bahwa model tanpa augmentasi mencapai akurasi 55,71%, precision macro 90,06%, recall 55,71%, dan F1-score 65,29%. Augmentasi blur 30% justru menurunkan akurasi menjadi 49,29% meskipun mengurangi overfitting sebesar 6,63%. Parameter SVM diatur C=0,3 dan gamma=0,01 untuk meningkatkan regularisasi. Penelitian ini menyimpulkan bahwa kombinasi EfficientNet-B0 dan SVM efektif untuk klasifikasi batik multikelas, namun augmentasi blur tidak sesuai untuk data tekstur kaya detail. Rekomendasi penelitian selanjutnya adalah eksplorasi augmentasi geometris dan arsitektur feature extractor yang lebih advance. Kata kunci: Batik Nitik; Klasifikasi citra; EfficientNet-B0; Support Vector Machine; Augmentasi.
Analisis Quality of Service pada Implementasi Video Call Berbasis SIP dengan Monitoring Zabbix pada Jaringan WLAN
The development of IP-based communication technology has made video call services an efficient solution for real-time communication. This study analyzes the Quality of Service (QoS) performance of SIP-based video call implementation using the Zabbix monitoring system on a Wireless Local Area Network (WLAN). An experimental method was applied by implementing an Issabel PBX SIP server and conducting performance tests under five bandwidth variations, namely 2.5, 5, 10, 15, and 20 Mbps. Each test scenario was conducted for 15 minutes with a monitoring interval of 30 seconds. The evaluated QoS parameters include throughput, delay, jitter, and packet loss, which were assessed based on the TIPHON standard. The results show that higher bandwidth provides better QoS performance, while bandwidth limitation leads to performance degradation, particularly in packet loss and service stability. The evaluation also indicates that a minimum bandwidth of 5 Mbps is required to maintain acceptable video call quality on WLAN networks. Furthermore, this study demonstrates that Zabbix is effective as a continuous monitoring system capable of providing real-time and historical QoS data to support performance analysis and network evaluation. Kata kunci: Quality of Service; Session Initiation Protocol; Video call; Zabbix; WLAN.
Digitalisasi Manjemen Perkembangan Ayam di PT Muria Jaya Raya Kabupaten Kudus
This study examined the development of a broiler chicken management information system within a partnership scheme at PT. Muria Jaya Raya in Kudus Regency. The identified problems included manual and fragmented processes in monitoring chicken conditions, recording feed usage, scheduling vaccinations, and planning harvests. The study aimed to design an integrated information system to manage these processes in a structured manner. The research methodology consisted of requirement analysis, system design, database implementation, and system testing using branch coverage–based white box testing, black box testing, and User Acceptance Test. The testing results indicated that all system functions operated according to the design specifications and user requirements. The system improved data organization, facilitated effective monitoring of chicken growth, and supported data-driven decision making. Based on the testing outcomes, the system was considered suitable for operational use in partnership-based broiler farming management. Keywords: Information system; Broiler chicken; Livestock partnership; Growth monitoring; Farm management Abstrak Penelitian ini mengkaji pengembangan sistem manajemen perkembangan ayam broiler dalam skema kemitraan di PT. Muria Jaya Raya Kabupaten Kudus. Permasalahan yang dihadapi meliputi pemantauan kondisi ayam, pencatatan jenis pakan, penjadwalan vaksinasi, dan perencanaan panen yang masih dilakukan secara manual dan terpisah. Penelitian ini bertujuan merancang sistem informasi yang mampu mengelola data tersebut secara terintegrasi dan terstruktur. Metode penelitian meliputi analisis kebutuhan, perancangan sistem, implementasi basis data, serta pengujian sistem menggunakan white box berbasis branch coverage, black box testing, dan User Acceptance Test. Hasil pengujian menunjukkan bahwa seluruh fungsi sistem berjalan sesuai dengan rancangan dan kebutuhan pengguna. Sistem dinilai mampu meningkatkan keteraturan pencatatan, mempermudah pemantauan perkembangan ayam, serta mendukung pengambilan keputusan berbasis data. Berdasarkan hasil pengujian tersebut, sistem dinyatakan layak digunakan dalam operasional peternakan ayam broiler berbasis kemitraan. Kata kunci: Sistem informasi; Ayam broiler; Kemitraan peternakan; Pemantauan pertumbuhan; manajemen peternakan
Perancangan WhatsApp Bot Berbasis AI Untuk Optimalisasi Layanan Keabsahan Dokumen Pencatatan Sipil
The quality of population administration services is a vital indicator in fulfilling civil rights. Central Java Province provides the SELAKSA service via WhatsApp to facilitate document verification. However, the manual nature of the process results in high officer workloads, leading to a 40% delay in document completion. This research develops an AI-based WhatsApp Bot to automate the SELAKSA service workflow for better responsiveness. The system is designed to handle message classification and data recording automatically, eliminating the need for manual input. Adopting the Waterfall method, the system development integrates n8n and Gemini platforms to generate efficient and accurate interactions. Evaluation using the User Acceptance Test (UAT) method shows a high level of user satisfaction with a score of 4.62 out of 5. Furthermore, technical testing recorded a 100% recall value, indicating that all requests were handled successfully. The implementation of this technology is proven to clear all service queues efficiently and effectively, serving as a reference for digital transformation in other governments. Keywords: Chatbot; WhatsApp; AI; n8n; Government. Abstrak Kualitas layanan administrasi kependudukan menjadi indikator penting dalam pemenuhan hak sipil masyarakat. Provinsi Jawa Tengah memiliki layanan SELAKSA melalui WhatsApp untuk mempermudah verifikasi dokumen. Namun prosesnya bersifat manual menyebabkan beban kerja petugas sangat tinggi sehingga terjadi keterlambatan penyelesaian dokumen hingga 40%. Penelitian ini mengembangkan WhatsApp Bot berbasis AI untuk mengotomatisasi alur layanan SELAKSA agar lebih responsif. Sistem ini dirancang untuk menangani klasifikasi pesan dan pendataan secara otomatis tanpa harus melalui input manual satu per satu. Dengan mengadopsi metode Waterfall, pengembangan sistem mengintegrasikan platform n8n dan Gemini guna menghasilkan interaksi yang efisien dan akurat. Evaluasi menggunakan metode User Acceptance Test (UAT) menunjukkan tingkat kepuasan tinggi dari pengguna dengan skor 4,62 dari 5. Selain itu, pengujian teknis mencatat nilai recall mencapai 100% menunjukkan seluruh permohonan dapat tertangani dengan baik. Implementasi teknologi ini terbukti mampu menuntaskan seluruh antrean layanan secara efisien dan efektif sehingga dapat menjadi acuan bagi transformasi digital di instansi lainnya. Kata Kunci: Chatbot; WhatsApp; AI; n8n; Pemerintahan
Implementasi Penetasan Telur Otomatis berbasis IoT di Dapur Kalkun Undaan Tengah Kudus
The growing demand for turkey meat and eggs is not supported by sufficient chick availability, as most hatching still relies on manual methods with low success rates. This study develops an Internet of Things (IoT)-based turkey egg incubator system to help farmers monitor and control incubation automatically and in real time. The system uses a NodeMCU ESP8266 microcontroller, a DHT11 sensor for temperature and humidity, a relay for the heater, and a servo motor for egg rotation. A web interface enables remote monitoring via the internet. Tests using whitebox, blackbox, and user acceptance methods showed that the system maintains the ideal temperature of 37–38°C, regulates humidity, and performs automatic egg rotation. The system effectively supports incubation management, although improvements such as automatic notifications, backup sensors, and historical data visualization are still recommended for future development. Keywords: turkey egg incubator; Internet of Things (IoT); Monitoring and control; Automated hatching system. Abstrak Permintaan terhadap daging dan telur ayam kalkun terus meningkat, namun ketersediaan anak ayam masih terbatas karena proses penetasan sebagian besar dilakukan secara manual dengan tingkat keberhasilan yang rendah. Penelitian ini bertujuan mengembangkan sistem inkubator telur ayam kalkun berbasis Internet of Things (IoT) untuk membantu peternak memantau dan mengendalikan kondisi inkubasi secara otomatis serta real-time. Sistem dibangun menggunakan mikrokontroler NodeMCU ESP8266 sebagai pengendali utama yang terhubung dengan sensor DHT11 untuk membaca suhu dan kelembaban, relay untuk mengatur pemanas, serta motor servo untuk memutar telur. Antarmuka web disediakan agar pengguna dapat memantau kondisi inkubator dari jarak jauh. Pengujian dilakukan melalui metode whitebox, blackbox, dan User Acceptance Test (UAT), yang menunjukkan bahwa sistem mampu menjaga suhu ideal 37–38°C, mengatur kelembaban sesuai kebutuhan, serta melakukan pemutaran telur secara otomatis selama masa inkubasi. Sistem ini efektif meningkatkan efisiensi penetasan, meski masih perlu pengembangan fitur seperti notifikasi otomatis, cadangan sensor, dan visualisasi data historis. Kata kunci: Inkubator telur kalkun; Internet of things; Monitoring dan kendali; Sistem penetasan otomatis
Analisis TAM: Penerimaan Generative AI pada Pemasaran UMKM Pasar 16 Ilir Palembang
Palembang City has many Micro, Small and Medium Enterprises which are the backbone of the local economy, but there is still little research regarding the acceptance of Generative AI in marketing operations. This research aims to analyze the level of acceptance of Generative AI by MSMEs and its influence on consumer loyalty using the Technology Acceptance Model (TAM) approach. The research was conducted with 130 MSMEs as respondents, using quantitative methods and Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis via SmartPLS software. The research results show that MSMEs accept Generative AI well, especially in creating promotional content, designing slogans, and communicating with consumers. The application of Generative AI has been proven to increase customer loyalty, as can be seen from the increased frequency of repeat purchases. These findings provide strategic insight for MSMEs to optimally maximize the potential of Generative AI in marketing operations. Keywords: Generative AI; Technology Acceptance Model; Micro, Small and Medium Enterprises; Marketing; Consumer Loyalty Abstrak Kota Palembang memiliki banyak Usaha Mikro Kecil dan Menengah (UMKM) yang menjadi tulang punggung ekonomi lokal, namun masih sedikit penelitian mengenai penerimaan Generative AI dalam operasional pemasaran. Penelitian ini bertujuan untuk menganalisis tingkat penerimaan Generative AI oleh UMKM dan pengaruhnya terhadap loyalitas konsumen menggunakan pendekatan Technology Acceptance Model (TAM). Penelitian dilakukan dengan responden sebanyak 130 pelaku UMKM, menggunakan metode kuantitatif dan analisis Partial Least Squares Structural Equation Modeling (PLS-SEM) melalui software SmartPLS. Hasil penelitian menunjukkan bahwa UMKM menerima Generative AI dengan baik, terutama dalam pembuatan konten promosi, perancangan slogan, dan komunikasi dengan konsumen. Penerapan Generative AI terbukti meningkatkan loyalitas pelanggan, terlihat dari meningkatnya frekuensi pembelian berulang. Temuan ini memberikan insight strategis bagi UMKM untuk memaksimalkan potensi Generative AI dalam operasional pemasaran secara optimal. Kata kunci: Generative AI; Technology Acceptance Model; Usaha Mikro Kecil dan Menengah; Pemasaran; Loyalitas Konsumen
Security Assessment of Open-Source Village Governance Systems: A Case Study of OpenSID
Indonesia's OpenSID platform manages sensitive citizen data across thousands of rural administrative units, yet no empirical security assessment exists in academic literature. This study addresses this gap through comprehensive security evaluation using Static Application Security Testing (SAST) and Software Composition Analysis (SCA), with findings mapped to OWASP Top 10 and scored using CVSS v3.1. Analysis identified 402 raw findings, with 170 (42.3%) confirmed as true positives after manual validation. Broken Access Control (105 findings) and Injection vulnerabilities (26 findings) were predominant, with seven Critical or High severity issues detected, including path traversal and known CVE dependencies. The 57.7% false positive rate emphasizes the necessity of manual validation alongside automated scanning. This research provides the first structured security audit of Indonesian governance software and recommends adopting GitHub-native security tools and formal vulnerability disclosure policies. Keywords: OpenSID; Security Assessment; OWASP Top 10; Static Application Security Testing; Village Information System Abstrak Platform OpenSID Indonesia mengelola data sensitif warga di ribuan unit administrasi pedesaan, namun belum ada penilaian keamanan empiris dalam literatur akademik. Penelitian ini mengisi kesenjangan tersebut melalui evaluasi keamanan komprehensif menggunakan Static Application Security Testing (SAST) dan Software Composition Analysis (SCA), dengan temuan dipetakan ke OWASP Top 10 dan dinilai menggunakan CVSS v3.1. Analisis mengidentifikasi 402 temuan mentah, dengan 170 (42,3%) dikonfirmasi sebagai true positive setelah validasi manual. Broken Access Control (105 temuan) dan kerentanan Injection (26 temuan) mendominasi, dengan tujuh masalah tingkat keparahan Critical atau High terdeteksi, termasuk path traversal dan dependensi CVE yang diketahui. Tingkat false positive 57,7% menekankan pentingnya validasi manual bersama pemindaian otomatis. Riset ini menyediakan audit keamanan terstruktur pertama untuk perangkat lunak tata kelola Indonesia dan merekomendasikan adopsi tools keamanan GitHub-native serta kebijakan pengungkapan kerentanan formal. Kata kunci: OpenSID; Asesmen Keamanan; OWASP Top 10; Static Application Security Testing; Sistem Informasi Desa
Segmentasi Citra Dermoskopi Kanker Kulit Menggunakan Metode VGG-SegNet
Skin cancer, particularly melanoma, has a high mortality rate, necessitating reliable early detection based on dermoscopic images. Accurate lesion segmentation is a crucial preprocessing step prior to deep learning–based classification. However, challenges remain in preserving lesion boundary details, handling variations in lesion size, and addressing limited data availability and hyperparameter optimization. This study proposes and evaluates various configurations of a hybrid VGG–SegNet model for skin lesion segmentation using the ISIC 2017 and ISIC 2018 datasets. The methodology includes image and ground truth validation, VGG16-based normalization, input image resolution variations (128×128, 160×160, and 256×256), and training–validation data splits of 50:50 and 70:30. The model is fine-tuned using skip connections and a multi-stage training scheme with a combination of Binary Cross-Entropy, Dice, and Focal Tversky Loss. The best-performing model achieves a Dice Coefficient of 0.899 and an Intersection over Union of 0.8177 on the validation set, demonstrating precise and efficient lesion segmentation. Keywords: Skin Cancer Segmentation; VGG-SegNet; ISIC; Fine Tuning Abstrak Kanker kulit, khususnya melanoma, memiliki tingkat mortalitas tinggi sehingga memerlukan deteksi dini berbasis citra dermoskopi yang andal. Segmentasi lesi yang akurat merupakan tahap penting sebelum proses klasifikasi berbasis deep learning. Namun, tantangan masih muncul pada detail tepi lesi, variasi ukuran, serta keterbatasan data dan optimasi hyperparameter. Penelitian ini mengusulkan dan mengevaluasi berbagai konfigurasi model hibrida VGG–SegNet untuk segmentasi lesi kulit menggunakan dataset ISIC 2017 dan ISIC 2018. Tahapan meliputi validasi citra dan ground truth, normalisasi berbasis VGG16, variasi resolusi citra (128×128, 160×160, 256×256), serta pembagian data latih dan validasi dengan rasio 50:50 dan 70:30. Model di fine tuning menggunakan skip connection dan skema pelatihan bertahap dengan kombinasi Binary Cross-Entropy, Dice, dan Focal Tversky Loss. Model terbaik mencapai Dice Coefficient 0,899 dan IoU 0,8177 pada data validasi, menunjukkan segmentasi yang presisi dan efisien. Kata kunci: Segmentasi Kanker Kulit; VGG–SegNet; ISIC; Fine Tuning