JISKA (Jurnal Informatika Sunan Kalijaga)
type: channel
description: JISKA (Jurnal Informatika Sunan Kalijaga), abbreviated as JISKa, has been published by the Department of Informatics, Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta, since May 2016. JISKa serves as a platform for the publication of research findings from lecturers, researchers, students, and practitioners in the fields of Informatics, Computer Science, and Information Technology. The journal is published three times a year, in January, May, and September. Until 2024, each issue consisted of seven articles. However, beginning with Volume 10 (January 2025), JISKa publishes ten articles per issue. All articles are open access. This journal has been accredited by the National Journal Accreditation (ARJUNA) Sinta 3 from Volume 6 No 1 January 2021 until Volume 14 No 2 May 2029 according to the decree, SK NOMOR 79/E/KPT/2023 and SK NOMOR 295/C/C3/KPT/2026. The current Sinta Google Citation and h-index are 2223 and 22, respectively. The journal has also been indexed by DOAJ, Dimensions (citations: 233 and mean: 1.06), Crossref, Garuda, scite.ai (citations: 383), and Scilit. Please open the link on the Indexing menu to see the impact of articles that published by JISKa. JISKa opens the opportunity to receive articles any time either in Indonesian or English. Please register account and download the JISKa template file for submitting articles to JISKa. Each article will be reviewed by a minimum of two reviewers with a double-blind peer review method, meaning reviewers do not know the authors' identities and vice versa. The rules and examples of writing details can be seen in the JISKa template file. Until now, JISKa only uses the MS DOC / DOCX format for article submission. In light of the need to enhance the quality of publications in JISKa, which requires a substantial financial commitment, authors who submit manuscripts starting Monday, April 13, 2026, will be subject to an article publishing fee of IDR 500,000 or USD 50, payable upon acceptance of their …
publisher: UIN Sunan Kalijaga Yogyakarta
language: en
publication name: JISKA (Jurnal Informatika Sunan Kalijaga)
issn: 2527-5836
copyright: Authors who publish with this journal agree to the following terms as stated in http://creativecommons.org/licenses/by-nc/4.0 a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
items: Peningkatan Akurasi Temu Kembali Pengetahuan pada Kasus Sakit Kepala Menggunakan Integrasi Case-Based Reasoning dan Natural Language Processing
items: Analisis Ketahanan Lightweight Audio Spectrogram Transformer pada Identifikasi Pembicara Kondisi Berderau
items: Penerapan ResNeXt dan Long Short-Term Memory untuk Deteksi Video Deepfake
items: Optimizing Financial Risk Prediction for Loan Approval Decisions
items: Klasifikasi Penyakit Mata Berdasarkan Citra Fundus Menggunakan Metode Multi-Layer Perceptron
items: Comparative Analysis of Hybrid CNN-ViT and CNN for Brain Tumor Classification
items: Optimizing Iris Plant Classification with Ensemble Models and XAI
items: Optimasi Hyperparameter Ensemble Learning untuk Prediksi Perkembangan Penyakit Diabetes dengan Explainable AI
items: Pengaruh Orientasi Citra MRI pada Klasifikasi Tumor Otak Berbasis GLCM dan SVM
items: Evaluasi Pemanfaatan Online Compiler dan Autograder Sebagai Pendukung Aktivitasi Praktikum Pemograman Mahasiswa
items: Optimasi Deteksi Retakan Jalan Menggunakan Filter Sobel dan Klasifikasi Gaussian Naïve Bayes
items: Model Prediksi Risiko Kanker Serviks dengan Pendekatan Support Vector Machine
items: Sistem Deteksi Gerakan Kecurangan UTBK Real-Time dengan YOLOv8 dan Optical Flow
items: Komparasi Distance Measure pada K-Means dalam Klasterisasi Peserta KB Aktif
items: Comparative Analysis of Camellia and AES Across File Sizes and Types
items: Sistem Deep-Learning Yolov8 untuk Deteksi Penggunaan APD Secara Real-Time
items: Perbandingan Kinerja MobileNetV2 dan VGG16 dalam Klasifikasi Penyakit pada Citra Daun Tanaman Cabai
items: Uji Efektifitas Kompresi Golomb-Rice dan Huffman untuk Metadata EXIF dalam File JPEG
items: Enhancing Diabetes Classification Using a Relaxed Online Maximum Margin Algorithm
items: Evaluasi Keamanan OTP Firebase pada Aplikasi Android: Perbandingan SAST dan IAST dalam Identifikasi Kerentanan
items: Algoritma Random Forest dan Synthetic Minority Oversampling Technique (SMOTE) untuk Deteksi Diabetes
items: Arsitektur Microservice untuk Optimalisasi Aplikasi Eco-Maps dalam Mendukung Kampus Ramah Lingkungan
items: Implementasi Metode YOLOv8 Mendeteksi Komputer Aktif dengan Subjek Layar Monitor
items: Android Malware Threats: A Strengthened Reverse Engineering Approach to Forensic Analysis
items: Deteksi Diabetes Mellitus dengan Menggunakan Teknik Ensemble XGBoost dan LightGBM
items: Perbandingan Sensitivitas Metode SAW, MAUT dan WSM pada Anugerah Mutu Non-Akademik Universitas
items: Enhancing Abstractive Multi-Document Summarization with Bert2Bert Model for Indonesian Language
items: Spatial Decision Support System to Determine the Feasibility of Evacuation Posts in Natural Disasters
items: Analisis Sistem Deteksi Citra untuk Optimalisasi Pengawasan Lalu Lintas Udara Menggunakan Algoritma YOLOv5
items: Analisis Efektivitas Metode Filtering dan Intersection dalam Analisis Data Permukaan Bangunan dengan QGIS

Peningkatan Akurasi Temu Kembali Pengetahuan pada Kasus Sakit Kepala Menggunakan Integrasi Case-Based Reasoning dan Natural Language Processing
type: item
description: Headache disorders represent a significant global neurological challenge, yet medical diagnosis is often hindered by subjective patient complaints and limited facilities. This study proposes an integrated model combining Natural Language Processing (NLP) and Case-Based Reasoning (CBR) to enhance the accuracy of medical decision-making for headache cases. The model utilizes the Random Forest algorithm for NLP classification and Cosine Similarity within the CBR framework to identify case relevance. The dataset consists of medical records for two types of headaches: Cluster headache (G44.0) and Tension-type headache (G44.2). Experimental results demonstrate that data augmentation significantly improves model performance, increasing accuracy from 62% to 69%. For the G44.0 label, the model achieved a precision of 0.84, while the G44.2 label reached a recall of 0.87. Furthermore, the CBR system strengthens the diagnosis with a similarity level of up to 0.82 and continuous learning capabilities through the Retain stage. This integration effectively provides faster, more targeted diagnostic recommendations for medical professionals.
subject: Articles
  topic: section
subject: Headache
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Case-Based Reasoning
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Data Augmentation
  topic: keywords
creator: Agus Mulyanto
rights: Copyright (c) 2026 Agus Mulyanto https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: 265
ending page: 274
doi: 10.14421/jiska.6189

Analisis Ketahanan Lightweight Audio Spectrogram Transformer pada Identifikasi Pembicara Kondisi Berderau
type: item
description: The use of deep learning models for speaker identification on devices with limited computational resources requires significant architectural optimization. This study evaluates the performance and robustness of the Lightweight Audio Spectrogram Transformer (AST) architecture, which has been extremely compressed to 570,536 parameters. The proposed method uses low-resolution Mel-Spectrogram representations (64x64 pixels) as input for a global self-attention mechanism. Testing was conducted using a 5-Fold Cross Validation scheme on a dataset injected with non-stationary environmental noise from the ESC-50 corpus at various Signal-to-Noise Ratio (SNR) levels. Experimental results show that under ideal conditions, the model achieves a solid average validation accuracy of 70.86% ± 2.69% with a Macro Average F1-score of 0.68 ± 0.03. However, the model’s performance degrades sharply to 17.61% at an SNR of 5 dB and drops to 9.21% under extreme conditions at an SNR of 0 dB. These findings reveal a critical trade-off where radical parameter compression leads to the loss of spectral feature redundancy that acts as an implicit noise filter. This study concludes that while lightweight Transformer mechanisms are highly efficient for Edge AI, the integration of pre-processing modules or noise-robust training strategies is an absolute necessity to maintain identification integrity in noisy real-world environments.
subject: Articles
  topic: section
subject: Speaker Identification
  topic: keywords
subject: Audio Spectrogram Transformer
  topic: keywords
subject: Edge AI
  topic: keywords
subject: Robustness Analysis
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: I Kadek Arya Sugianta
creator: Gde Palguna Reganata
rights: Copyright (c) 2026 I Kadek Arya Sugianta, Gde Palguna Reganata https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: 252
ending page: 264
doi: 10.14421/jiska.6170

Penerapan ResNeXt dan Long Short-Term Memory untuk Deteksi Video Deepfake
type: item
description: Deepfake is a form of facial manipulation in videos that utilizes artificial intelligence-based models to generate highly realistic visual content. The increasing spread of Deepfake has the potential to cause misinformation, manipulate public opinion, and enable the misuse of digital content, making reliable detection systems increasingly necessary. This study develops a face-based Deepfake video detection system by combining spatial and temporal analysis within a unified processing framework. ResNeXt is employed to extract visual facial characteristics from each frame, while Long Short-Term Memory (LSTM) is utilized to learn facial pattern changes across frames in a video sequence. The dataset used is sourced from FaceForensics++, consisting of 1000 original videos and 1000 Deepfake videos. All data are processed through frame extraction and face detection stages. Performance evaluation is conducted using accuracy, precision, recall, and F1-score metrics. The experimental results show that ResNeXt, as the baseline model, achieves 77.67% accuracy and 76.66% F1-score, while integrating LSTM improves system performance to 81.00% accuracy and 80.41% F1-score. These findings indicate that the utilization of temporal information contributes to improved stability and accuracy in face-based Deepfake video detection.
subject: Articles
  topic: section
subject: Deepfake
  topic: keywords
subject: Video Detection
  topic: keywords
subject: ResNeXt
  topic: keywords
subject: LSTM
  topic: keywords
subject: Spatial Features
  topic: keywords
subject: Temporal Patterns
  topic: keywords
creator: Chalifa Chazar
creator: Firhan Hafiansyah
creator: Milda Gustiana Husada
creator: Uung Ungkawa
creator: Rizka Milandga Milenio
rights: Copyright (c) 2026 Chalifa Chazar, Firhan Hafiansyah, Milda Gustiana Husada, Uung Ungkawa, Rizka Milandga Milenio https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: 237
ending page: 251
doi: 10.14421/jiska.6085

Optimizing Financial Risk Prediction for Loan Approval Decisions
type: item
description: Accurate financial risk prediction is essential for effective loan approval decision-making, particularly in data-driven financial systems. This study investigates the influence of feature selection strategies on the performance of machine learning models for loan approval prediction using the publicly available Kaggle "Financial Risk for Loan Approval" synthetic dataset, which contains 20,000 applications. Experiments evaluated multiple feature selection paradigms, including filter-based, wrapper-based, embedded, and PCA-informed approaches across six classification models using stratified 10-fold cross-validation and imbalance-aware metrics. The results show that feature selection consistently improves predictive robustness and minority class recognition. Contrary to assumptions favoring complex models, Logistic Regression combined with Lasso regularization achieved the best overall predictive performance, yielding an ROC-AUC of 99.41% and an F1-score of 91.72%. Embedded feature selection methods provided the most favorable balance between accuracy and computational efficiency. These findings indicate that the effectiveness of feature selection depends heavily on its interaction with model complexity, providing empirical guidance for designing robust, interpretable financial risk prediction systems.
subject: Articles
  topic: section
subject: Financial Risk Prediction
  topic: keywords
subject: Loan Approval
  topic: keywords
subject: Feature Selection
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Classification
  topic: keywords
creator: Khalifatur Rauf
creator: Adi Cahyo Kuswijayanto
creator: Ella Kristiantini Susan
rights: Copyright (c) 2026 Khalifatur Rauf, Adi Cahyo Kuswijayanto, Ella Kristiantini Susan https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: 225
ending page: 236
doi: 10.14421/jiska.6071

Klasifikasi Penyakit Mata Berdasarkan Citra Fundus Menggunakan Metode Multi-Layer Perceptron
type: item
description: This research aims to evaluate the performance of the Multi-Layer Perceptron (MLP) for classifying eye diseases from fundus images in the ODIR dataset, which comprises four classes: Normal, Diabetic, Glaucoma, and Cataract. The methodology includes feature extraction using GLCM and Gabor, data pre-processing through cleaning, augmentation, and undersampling, and testing 16 model scenarios with variations in the number of hidden layers (2 and 3) and neuron configurations. The results show that data balance and dataset size are the most influential factors affecting model performance, with the best results achieved through the combination of undersampling and augmentation. The optimal architecture was obtained with the 64–32-neuron configuration, yielding a mean accuracy of 73.06%. Overall, this study concludes that combining a balanced dataset with a proportional MLP architecture significantly improves the model’s ability to classify eye diseases from fundus images.
subject: Articles
  topic: section
subject: Eye Disease Classification
  topic: keywords
subject: MLP
  topic: keywords
subject: Fundus
  topic: keywords
subject: Feature Extraction
  topic: keywords
subject: Classification
  topic: keywords
creator: Laudza Atsila Prasetyo
creator: Okta Qomaruddin Aziz
creator: Tri Mukti Lestari
rights: Copyright (c) 2026 Laudza Atsila Prasetyo, Okta Qomaruddin Aziz, Tri Mukti Lestari https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: 213
ending page: 224
doi: 10.14421/jiska.6022

Comparative Analysis of Hybrid CNN-ViT and CNN for Brain Tumor Classification
type: item
description: The automated categorization of brain cancers from MRI is essential for improving diagnostic precision. Traditional Convolutional Neural Networks (CNNs) are proficient in local feature extraction but are constrained in their ability to capture long-range spatial relationships, hence impairing performance on intricate malignancies. We propose a hybrid parallel architecture that merges a CNN with a Vision Transformer (ViT) to combine local and global feature modeling. We assessed our dual-branch model in comparison to a conventional CNN baseline using a curated dataset of 15,000 MRI images categorized into three classes: glioma, meningioma, and pituitary. The hybrid model exhibited enhanced performance, attaining 98.40% accuracy and 0.0783 loss, in contrast to the baseline's 97.40% accuracy and 0.1187 loss. The substantial decrease in misclassifications was validated by additional metrics, such as enhanced recall for the meningioma category. The integration of local and global variables produces a more precise, stable, and generalizable classification framework, demonstrating significant potential as a basis for dependable AI-driven Clinical Decision Support Systems (CDSS) in neuroradiology.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Medical Image Analysis
  topic: keywords
subject: Vision Transformer
  topic: keywords
creator: Ahmad Fauzi
creator: Achmad Lutfi Fuadi
creator: Agus Heri Yunial
rights: Copyright (c) 2026 Ahmad Fauzi, Achmad Lutfi Fuadi, Agus Heri Yunial https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 127
ending page: 142
doi: 10.14421/jiska.5860

Optimizing Iris Plant Classification with Ensemble Models and XAI
type: item
description: This study aims to improve the performance of Iris plant classification by integrating ensemble learning techniques with Explainable Artificial Intelligence (XAI) to achieve high accuracy while enhancing model interpretability. Random Forest, XGBoost, and AdaBoost algorithms are combined within a Voting Ensemble framework and evaluated using the Iris Plants dataset, which comprises 150 data samples distributed equally across three Iris species (50 samples per class: Iris setosa, Iris versicolor, and Iris virginica). The dataset exhibits a perfectly balanced class distribution, ensuring that no class imbalance correction was required. The Voting Ensemble model was evaluated using a hold-out test set (80:20 split) and further validated through 5-Fold Stratified Cross-Validation, yielding a mean cross-validation accuracy of 95.83% (±2.64%) and a test set accuracy of 93.33%. To enhance model transparency, the SHAP (SHapley Additive Explanations) method is applied to explain the contribution of each feature to the prediction outcomes. The Voting Ensemble model achieved an ROC AUC score of 0.9900 (macro-average), with Precision, Recall, and F1-Score each reaching 0.9333 (macro-average). Feature importance analysis reveals that petal length and petal width are the primary factors in the Iris species classification process. The strong correlation (r = 0.9991) between feature importance scores in the Random Forest model and SHAP values confirms the consistency and reliability of the model’s interpretability. These findings demonstrate that integrating ensemble learning with XAI not only improves predictive performance but also strengthens transparency and trust in machine learning models, particularly for plant classification tasks.
subject: Articles
  topic: section
subject: Iris Plant
  topic: keywords
subject: Ensemble Models
  topic: keywords
subject: XAI
  topic: keywords
subject: Model Performance
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Ahmad Subadri
creator: Ishmah Afiyah
creator: Fiki Sanora
creator: Arya Indrawan
creator: Maria Ulfah Siregar
rights: Copyright (c) 2026 Ahmad Subadri, Ishmah Afiyah, Fiki Sanora, Arya Indrawan, Maria Ulfah Siregar https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: 195
ending page: 212
doi: 10.14421/jiska.5967

Optimasi Hyperparameter Ensemble Learning untuk Prediksi Perkembangan Penyakit Diabetes dengan Explainable AI
type: item
description: This research focuses on optimizing and assessing ensemble learning models for predicting diabetes progression by combining hyperparameter tuning and explainable artificial intelligence techniques. Experiments were conducted using the scikit-learn diabetes dataset, which contains 442 samples with ten numerical features representing patients’ clinical conditions. The data were split into 80% for training and 20% for testing. Two ensemble methods were explored: Random Forest Regressor (bagging) and XGBoost Regressor (boosting). Hyperparameter optimization was carried out using RandomizedSearchCV and BayesianSearchCV under a five-fold cross-validation scheme. Model performance was evaluated using MAE, MSE, RMSE, and R² metrics, while interpretability was examined through SHAP summary plots. The results indicate that BayesianSearchCV consistently delivered superior performance gains compared to random search. In particular, the optimized XGBoost model achieved an R² score of 0.5018, improving by 19.8% over the baseline model (R² = 0.4188), and reduced RMSE from 55.49 to 51.37. SHAP analysis showed that serum triglycerides, body mass index, and blood pressure were the most influential features. Overall, the findings suggest that Bayesian-based hyperparameter optimization can effectively improve ensemble regression performance in medical prediction tasks involving limited datasets.
subject: Articles
  topic: section
subject: Ensemble Learning
  topic: keywords
subject: Hyperparameter Optimization
  topic: keywords
subject: Random Forest
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Diabetes Prediction
  topic: keywords
creator: David Suharjanto
creator: Muhammad Syafiq Akmal
creator: Nur Fikri Khuluq
creator: Muh Naufal Muzhaffar
creator: Maria Ulfah Siregar
rights: Copyright (c) 2026 David Suharjanto, Muhammad Syafiq Akmal, Nur Fikri Khuluq, Muh Naufal Muzhaffar, Maria Ulfah Siregar https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: 182
ending page: 194
doi: 10.14421/jiska.5953

Pengaruh Orientasi Citra MRI pada Klasifikasi Tumor Otak Berbasis GLCM dan SVM
type: item
description: Brain tumors are a global health problem, ranking 12th as a cause of death. MRI is used in the diagnosis of brain tumors because of its ability to display soft tissue structures in detail, but manual interpretation of MRI images by radiologists is still subjective. Therefore, a more objective computer-based classification approach is needed. One factor that could potentially affect classification performance is the difference in MRI image orientation, namely axial, sagittal, and coronal. This study aims to analyze the effect of MRI image orientation on GLCM and SVM-based brain tumor classification. The preprocessing stage includes cropping, noise reduction, and resizing. Feature extraction was performed using GLCM with distance d = 1 at angles of 0°, 45°, 90°, and 135° with contrast, correlation, energy, and homogeneity features. Classification was performed using SVM with Linear, Polynomial, RBF, and Sigmoid kernels. The test results show that the axial orientation produces the highest accuracy of 78% with the Linear kernel, the sagittal orientation achieves an accuracy of 83% with the Polynomial kernel, and the coronal orientation provides the highest accuracy of 86% with the RBF kernel. These findings indicate that the orientation of MRI images affects the performance of texture-based brain tumor classification.
subject: Articles
  topic: section
subject: Brain Tumor
  topic: keywords
subject: MRI Image
  topic: keywords
subject: GLCM
  topic: keywords
subject: SVM
  topic: keywords
subject: Image Processing
  topic: keywords
creator: Yoza Setya Febriyanti
creator: Okta Qomaruddin Aziz
creator: Suhartono Suhartono
rights: Copyright (c) 2026 Yoza Setya Febriyanti, Okta Qomaruddin Aziz, Suhartono Suhartono https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: 169
ending page: 181
doi: 10.14421/jiska.5935

Evaluasi Pemanfaatan Online Compiler dan Autograder Sebagai Pendukung Aktivitasi Praktikum Pemograman Mahasiswa
type: item
description: The automation of programming practicum assessment using an online compiler has become increasingly important to address the limitations of manual evaluation, such as time inefficiency, subjectivity, and the lack of automated feedback support in conventional e-learning systems. This study aims to evaluate the implementation of an online compiler and autograder in a programming practicum through interviews and questionnaires with 91 respondents, comprising lecturers and students. The online compiler and autograder were implemented using the Moodle platform, integrated with the CodeRunner plugin and the JOBE server to automate code evaluation and provide real-time feedback to students. User acceptance was assessed using the Technology Acceptance Model (TAM), which includes three dimensions: perceived ease of use, perceived usefulness, and system acceptance. The evaluation results indicate positive scores across all dimensions, with 78.75% for ease of use, 78.09% for usefulness, and 76.33% for system acceptance. These findings demonstrate that the proposed system effectively improves the assessment process and shows strong potential for adoption in programming education environments.
subject: Articles
  topic: section
subject: Evaluation
  topic: keywords
subject: E-Learning
  topic: keywords
subject: Online Compiler
  topic: keywords
subject: Autograder
  topic: keywords
subject: Technology Acceptance Model
  topic: keywords
creator: Muhammad Galih Wonoseto
creator: Muhammad Jibril
rights: Copyright (c) 2026 Muhammad Galih Wonoseto, Muhammad Jibril https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: 143
ending page: 154
doi: 10.14421/jiska.5864

Optimasi Deteksi Retakan Jalan Menggunakan Filter Sobel dan Klasifikasi Gaussian Naïve Bayes
type: item
description: Manual identification of road damage using simple measuring tools is considered inefficient, subjective, and time-consuming, hindering the infrastructure repair process. This study aims to optimize automatic road crack detection by combining edge detection for feature extraction and Gaussian Naive Bayes (GNB) classification. This research utilizes the Road Surface Classification Dataset (RSCD), consisting of 1000 concrete road images with balanced class proportions. The research process includes image acquisition, segmentation, and preprocessing using the Sobel filter to extract edge features and erosion to refine crack representation. Statistical features in the form of black pixel count and edge length are extracted as model inputs. Experiments were conducted using three data split scenarios (70:30, 80:20, 90:10) validated with the K-Fold Cross Validation method. The test results show that the 90:10 data split scenario yields the most optimal and stable performance, achieving 88% accuracy, 91.67% precision, 84.62% recall, and an F1-Score of 88%. This study optimises the balance between computational efficiency and detection accuracy through a lightweight hybrid approach that integrates edge-based feature extraction with probabilistic classification.
subject: Articles
  topic: section
subject: Road Crack Detection
  topic: keywords
subject: Gaussian Naive Bayes
  topic: keywords
subject: Sobel Filter
  topic: keywords
subject: Image Classification
  topic: keywords
subject: Image Processing
  topic: keywords
creator: Fakhar Muhammad Hidayat
creator: Cahyo Crysdian
creator: Tri Mukti Lestari
rights: Copyright (c) 2026 Fakhar Muhammad Hidayat, Cahyo Crysdian, Tri Mukti Lestari https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: 155
ending page: 168
doi: 10.14421/jiska.5912

Model Prediksi Risiko Kanker Serviks dengan Pendekatan Support Vector Machine
type: item
description: Cervical cancer is one of the leading causes of death in women, especially in developing countries due to delays in early diagnosis. Developing a risk prediction model based on the Support Vector Machine (SVM) algorithm is one way to support a more accurate and efficient early detection process. The research object is medical records of female patients obtained from hospitals in Medan City, with a total of 164 patient data. The development process was carried out through the CRISP-DM stages, which include data cleaning, feature transformation, class balancing with SMOTE, and dimensionality reduction using PCA. The evaluation results showed that the best model was obtained with a PCA configuration with 9 principal components (90% variance) and a test size of 80:20, resulting in an accuracy of 88%, a precision of 88%, a recall of 84%, and an F1-score of 86%. Cross-validation evaluation with 5 folds provided the best average performance and the smallest standard deviation, indicating model stability. The final model was implemented in a web-based system to facilitate digital early detection. This study shows that SVM with the SMOTE and PCA approaches is effective in predicting cervical cancer risk accurately and efficiently.
subject: Articles
  topic: section
subject: Cervical Cancer
  topic: keywords
subject: Prediction
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: SMOTE
  topic: keywords
subject: PCA
  topic: keywords
creator: Juwita Stefany Hutapea
creator: Nisa Hanum Harani
creator: Cahyo Prianto
rights: Copyright (c) 2026 Juwita Stefany Hutapea, Nisa Hanum Harani, Cahyo Prianto https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 114
ending page: 126
doi: 10.14421/jiska.5445

Sistem Deteksi Gerakan Kecurangan UTBK Real-Time dengan YOLOv8 dan Optical Flow
type: item
description: Integrity and honesty are fundamental aspects of education, including the implementation of the Computer-Based Written Examination (UTBK). Conventional exam supervision is considered less effective in monitoring participants’ behavior due to the limitations in human observation capabilities and consistency. This study develops a real-time cheating-detection system based on camera input by integrating the YOLOv8 algorithm with Farnebäck optical flow. The YOLOv8 algorithm identifies participants’ body poses and activities directly from video footage, while Optical Flow analyzes the direction and motion patterns between frames over time. The system is designed to recognize various suspicious poses such as head-turning, bowing, and cheating-related gestures that indicate potential dishonesty. All detection results are automatically recorded in an SQLite database, complete with timestamps and visual evidence. Experimental results show that the system achieves 94.3% accuracy in detecting suspicious movements. The combination of both methods also helps maintain detection stability when keypoints are not consistently captured in some frames. Additionally, the system is equipped with a graphical user interface (GUI) to facilitate easier monitoring and analysis. These results demonstrate that a pose-and-motion analysis-based approach offers an intelligent and efficient solution for enhancing digital supervision of UTBK examinations.
subject: Articles
  topic: section
subject: UTBK Cheating
  topic: keywords
subject: Motion Detection
  topic: keywords
subject: Pose Estimation
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: Optical Flow
  topic: keywords
creator: Muhammad Naufal Ardiansyah
creator: Farrel Zikri Suryahadi
creator: Hendrico Edhent Surya Pratama
creator: Anggraini Puspita Sari
rights: Copyright (c) 2026 Muhammad Naufal Ardiansyah, Farrel Zikri Suryahadi, Hendrico Edhent Surya Pratama, Anggraini Puspita Sari https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 83
ending page: 97
doi: 10.14421/jiska.5365

Komparasi Distance Measure pada K-Means dalam Klasterisasi Peserta KB Aktif
type: item
description: The rapid population growth in Indonesia poses significant challenges to public welfare, economic stability, and sustainable development. The Family Planning program aims to regulate population growth through various contraceptive methods; however, participation rates often differ across regions. Understanding these variations is crucial for designing targeted interventions. This study investigates how different distance measures in the K-Means clustering algorithm affect the segmentation quality of KB participants in Kalirejo Village, Lawang District. Eight distance metrics—Euclidean, Manhattan, Minkowski, Chebyshev, Mahalanobis, Bray-Curtis, Canberra, and Cosine—were compared using standardized data from the local BKKBN office (January–September). Cluster validity was evaluated using the Silhouette Coefficient across k=2–10. Results show that the Manhattan distance with k=2 achieved the best clustering quality (SC = 0.7191), effectively distinguishing participant groups by contraceptive method preference. The study highlights the importance of selecting suitable distance measures to improve data-driven policy and decision-making in family planning management.
subject: Articles
  topic: section
subject: K-Means
  topic: keywords
subject: Clustering
  topic: keywords
subject: Silhouette Coefficient
  topic: keywords
subject: Distance Measure
  topic: keywords
subject: Manhattan
  topic: keywords
creator: Mochammad Anshori
creator: Afifah Vera Ferencia Fitria Ningrum
creator: Risqy Siwi Pradini
rights: Copyright (c) 2026 Mochammad Anshori, Afifah Vera Ferencia Fitria Ningrum, Risqy Siwi Pradini https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 32
ending page: 43
doi: 10.14421/jiska.5006

Comparative Analysis of Camellia and AES Across File Sizes and Types
type: item
description: Data security is a critical aspect of modern information systems that requires processing cryptographic efficiency and resilience. This study compares two widely used symmetric encryption algorithms, named Camellia and AES, based on their performance and resistance to standard attack methods. An experimental approach was applied using 72 files across eight commonly used formats (*.mp3, *.jpg, *.png, *.pdf, *.docx, *.xls, *.pptx, and .txt) in three predefined sizes: 100 KB, 1 MB, and 10 MB. Each file underwent encryption and decryption in a controlled environment, with metrics such as processing time, CPU usage, and RAM consumption recorded. Simulated Dictionary, Birthday, and Brute-Force attacks were conducted to assess algorithm robustness. Results show that AES performs faster, especially on large files, but with higher memory usage. Camellia demonstrated more consistent RAM usage and stronger resistance, successfully withstanding all attacks except one brute-force case on a small plaintext file. AES suffered multiple breaches on structured files of smaller sizes. The findings suggest that algorithm selection should consider workload characteristics and system constraints. The main contribution of this research lies in its comprehensive dataset and empirical comparison, providing practical insights to support encryption algorithm choices in real-world applications.
subject: Articles
  topic: section
subject: Cryptographic Efficiency
  topic: keywords
subject: Data Encryption
  topic: keywords
subject: Cryptanalysis
  topic: keywords
subject: Algorithm Security
  topic: keywords
subject: Encryption Performance
  topic: keywords
creator: Teja Endra Eng Tju
creator: Fauzi Alfadhillah
rights: Copyright (c) 2026 Teja Endra Eng Tju, Fauzi Alfadhillah https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 98
ending page: 113
doi: 10.14421/jiska.5418

Sistem Deep-Learning Yolov8 untuk Deteksi Penggunaan APD Secara Real-Time
type: item
description: Although workplace safety regulations in construction are clear, many workers are still reluctant to use Personal Protective Equipment (PPE) due to a lack of awareness, work pressure, and limited facilities. As a result, the risk of serious accidents increases. Conventional approaches such as verbal warnings or CCTV monitoring are considered less effective for early detection and prevention of violations. This study proposes an automatic detection system for PPE usage in construction areas using YOLOv8. The model was trained on a secondary dataset of 3,569 images for 100 epochs, with a 60% training, 20% validation, and 20% test split. Testing on 90 real-time frames showed good performance in detecting 8 PPE classes, with an average precision of 0.935, recall of 0.806, and F1-measure of 0.862. The results indicate that the system can classify PPE usage with high accuracy. However, a recall below 1 suggests that some objects, particularly "not wearing glasses" and "not wearing shoes," failed to be detected. The F1-measure of 0.862 reflects a good balance between precision and recall.
subject: Articles
  topic: section
subject: Personal Protective Equipment
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: Object Detection
  topic: keywords
creator: Nelson Mandela Rande Langi
creator: Arif Fadllullah
rights: Copyright (c) 2026 Nelson Mandela Rande Langi, Arif Fadllullah https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 44
ending page: 55
doi: 10.14421/jiska.5051

Perbandingan Kinerja MobileNetV2 dan VGG16 dalam Klasifikasi Penyakit pada Citra Daun Tanaman Cabai
type: item
description: Chili peppers play a crucial role in the Indonesian economy, serving as a significant source of income for many farmers. Price fluctuations influenced by weather conditions make this crop vulnerable to diseases that can impact productivity. However, leaves are key indicators of plant health, revealing early disease symptoms before they spread. This research focuses on detecting diseases in chili plants using neural network architectures via transfer learning, specifically MobileNetV2 and VGG16, to classify chili leaf images. The study aims to identify three disease classes: begomovirus, leaf spots, and healthy leaves. The dataset comprises 3,150 leaf images, split into 70% for training and 30% for testing. Results show that MobileNetV2 achieved an accuracy of 99.47% and VGG16 98.62%, with evaluation using a confusion matrix indicating good performance in disease identification, where MobileNetV2 offers better computational efficiency. Thus, transfer learning can effectively identify leaf diseases in chili plants.
subject: Articles
  topic: section
subject: Chili Diseases
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Image Classification
  topic: keywords
subject: Neural Network
  topic: keywords
subject: Transfer Learning
  topic: keywords
creator: Itsnaini Irvina Khoirunnisa
creator: Abdul Fadlil
creator: Herman Yuliansyah
rights: Copyright (c) 2026 Itsnaini Irvina Khoirunnisa, Abdul Fadlil, Herman Yuliansyah https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 70
ending page: 82
doi: 10.14421/jiska.5075

Uji Efektifitas Kompresi Golomb-Rice dan Huffman untuk Metadata EXIF dalam File JPEG
type: item
description: Compression algorithms are now called modern compression algorithms. This improvement is characterized by the combination of various classical techniques and is even based on machine learning and AI. However, the important part of compression is not only the algorithm, but also knowledge of the internal structure and metadata of the file is required. Like JPEG has a file structure that can be changed, cannot be changed, every marker (header), and EXIF metadata. Lack of knowledge of the file structure can cause data damage and file corruption. This study evaluates the compression of EXIF metadata of JPEG files using the Golomb-Rice and Huffman algorithms. Golomb-Rice can produce compression that affects the k parameter, while Huffman is optimal based on symbol frequency, but requires a code table. This study measures the effectiveness of both algorithms based on the compression ratio (CR). The test results of Golomb-Rice are more effective than those of Huffman. So, it can be concluded that the Golomb-Rice algorithm is superior in the context of compressing EXIF JPEG metadata, while Huffman shows lower efficiency in the tested scenarios.
subject: Articles
  topic: section
subject: Golomb-Rice
  topic: keywords
subject: Huffman
  topic: keywords
subject: EXIF
  topic: keywords
subject: JPEG
  topic: keywords
subject: Compression Ratio
  topic: keywords
creator: Yasir Hasan
rights: Copyright (c) 2026 Yasir Hasan https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 56
ending page: 69
doi: 10.14421/jiska.5060

Enhancing Diabetes Classification Using a Relaxed Online Maximum Margin Algorithm
type: item
description: Diabetes mellitus is a growing global health concern that requires accurate and reliable classification models for early diagnosis and effective management. Traditional machine learning models often struggle with class imbalance, generalization limitations, and high false-positive rates, leading to misdiagnoses and delayed interventions. This study enhances the Relaxed Online Maximum Margin Algorithm (ROMMA) to improve the accuracy of diabetes classification. Using a publicly available dataset from Kaggle, which contains 768 medical records with nine health attributes, the model’s performance was evaluated through a confusion matrix and classification metrics. The Enhanced ROMMA achieved an accuracy of 92%, significantly improving upon the Standard ROMMA’s 85% accuracy. The recall for diabetes detection increased from 0.83 to 0.94, reducing false negatives and ensuring more accurate patient identification. While slight misclassification still exists, this improvement enhances the model’s reliability for clinical applications. Future research should incorporate larger datasets and advanced techniques to enhance robustness and generalizability. This study contributes to the development of more accurate machine learning models for diabetes prediction, ultimately supporting better healthcare decision-making.
subject: Articles
  topic: section
subject: Diabetes Classification
  topic: keywords
subject: ROMMA
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Medical Diagnosis
  topic: keywords
subject: Model Evaluation
  topic: keywords
creator: Dyan Avando Meliala
creator: Arum Kurnia Sulistyawati
creator: Mohammad Diqi
creator: Marselina Endah Hiswati
creator: Tadem Vergi Kristian
rights: Copyright (c) 2025 Dyan Avando Meliala, Arum Kurnia Sulistyawati, Mohammad Diqi, Marselina Endah Hiswati, Tadem Vergi Kristian https://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 267
ending page: 278
doi: 10.14421/jiska.2025.10.3.267-278

Evaluasi Keamanan OTP Firebase pada Aplikasi Android: Perbandingan SAST dan IAST dalam Identifikasi Kerentanan
type: item
description: Application security is crucial for protecting user data from cyber threats, particularly in Android applications that utilize One-Time Password (OTP)-based authentication. This study evaluates the security of Firebase OTP via email using a combination of Static Application Security Testing (SAST) with Mobile Security Framework (MobSF) and Interactive Application Security Testing (IAST) with AppSweep. The results show that the combination of SAST and IAST is superior to single testing methods due to its wider detection coverage. SAST detects vulnerabilities in static code, while IAST identifies exploits in runtime. The testing showed significant improvements, with high-severity vulnerabilities decreasing from 3 cases in OTP-1 to zero in OTP-5, and the security score increasing from 43 (B) to 78 (A) in MobSF. Meanwhile, the number of vulnerabilities in AppSweep decreased from 14 to 9, with all high-severity vulnerabilities resolved. However, this study still has limitations, such as limited dataset coverage and potential bias from the testing tool. For further improvement, additional research can integrate artificial intelligence to automate vulnerability detection, as well as explore biometric-based authentication to enhance system security even further.
subject: Articles
  topic: section
subject: Firebase OTP
  topic: keywords
subject: MobS
  topic: keywords
subject: AppSweep
  topic: keywords
subject: SAST
  topic: keywords
subject: IAST
  topic: keywords
creator: I Gede Surya Rahayuda
creator: Ni Putu Linda Santiari
rights: Copyright (c) 2026 I Gede Surya Rahayuda, Ni Putu Linda Santiari https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 13
ending page: 31
doi: 10.14421/jiska.4909

Algoritma Random Forest dan Synthetic Minority Oversampling Technique (SMOTE) untuk Deteksi Diabetes
type: item
description: Diabetes is one of the challenges in global health. Indonesia ranks 5th in the world with the highest rate of diabetes. This research uses the Random Forest algorithm for diabetes detection. The purpose of this study is to detect diabetes using the Random Forest algorithm, which provides accurate and efficient results in the early diagnosis of diabetic patients. The data used is secondary data, specifically the “Diabetes Dataset,” which consists of 952 data points and has 17 features. The test scenario in this study divides the data into three parts, namely scenario 1 (90:10 ratio), scenario 2 (70:30 ratio), and scenario 3 (50:50 ratio). In each scenario, a comparison is made between using SMOTE and not using it. The best performance results are obtained in scenario 1, which uses SMOTE, producing 97% accuracy, 100% precision, 94% recall, and an F1-score of 97%.
subject: Articles
  topic: section
subject: Detection
  topic: keywords
subject: Diabetes
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Synthetic Minority Oversampling Technique
  topic: keywords
subject: Ensemble
  topic: keywords
creator: Nurussakinah Nurussakinah
creator: Muhammad Faisal
creator: Irwan Budi Santoso
rights: Copyright (c) 2025 Nurussakinah Nurussakinah, Muhammad Faisal, Irwan Budi Santoso https://creativecommons.org/licenses/by-nc/4.0
date: 2025-05-31
publication date: 2025-05-31
starting page: 221
ending page: 234
doi: 10.14421/jiska.2025.10.2.221-234

Arsitektur Microservice untuk Optimalisasi Aplikasi Eco-Maps dalam Mendukung Kampus Ramah Lingkungan
type: item
description: The implementation of environmentally friendly campus concepts has become increasingly crucial in addressing global environmental challenges. Eco-Maps is an application designed to visualize and manage sustainability efforts on campus, including energy management, waste management, and sustainable transportation initiatives. To enhance efficiency and flexibility, this study discusses the application of a microservice architecture in Eco-Maps. This architecture supports faster and more efficient development, testing, and deployment, while enabling horizontal scalability to manage high complexity and large data volumes. By separating application functions into independent services, microservices facilitate maintenance and updates while minimizing the impact of failures in individual services. This study also reviews the integration of containerization technologies, such as Docker and Kubernetes, to support microservice implementation. Through these technologies, the application can be deployed quickly and consistently across various environments, from development to production. System testing was conducted using load testing and stress testing methods, as shown in Tables 3 and 4. The results demonstrate that the average response time across ten iterations was 745.9 ms, with an average CPU usage of 44.38%. These findings confirm that processing load directly affects CPU efficiency and overall system performance.
subject: Articles
  topic: section
subject: Eco-Maps
  topic: keywords
subject: Microservice Architecture
  topic: keywords
subject: Sustainable Campus
  topic: keywords
subject: Docker
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: System Performance
  topic: keywords
creator: Alam Rahmatulloh
creator: Rohmat Gunawan
creator: Randi Rizal
rights: Copyright (c) 2025 Alam Rahmatulloh, Rohmat Gunawan, Randi Rizal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 341
ending page: 350
doi: 10.14421/jiska.2025.10.3.341-350

Implementasi Metode YOLOv8 Mendeteksi Komputer Aktif dengan Subjek Layar Monitor
type: item
description: Computers are one example of technological advances used in education. The use of computers that are not turned off can cause damage to computer components, and the use of electrical energy can increase. Student disobedience in turning off school laboratory computers when finished using them causes teachers to conduct manual checks by visiting each computer laboratory in the school. Deep learning is a machine learning algorithm that uses artificial neural networks. Deep learning is usually used for image recognition, voice identification, and data pattern analysis. Therefore, this study will apply the Deep Learning method, specifically YOLOv8, which aims to detect active computers based on the subject of the monitor screen and is expected to provide information about computers that are still active in the school laboratory. Based on the study's results, which detected 10 active computers, the 200-epoch model was selected with 100% accuracy at a speed of 2ms. Twenty active computers were selected, with 200 epoch models achieving 95% accuracy at a speed of 6ms per epoch. Thirty active computers were selected, with 100 epoch models achieving 96.67% accuracy at a speed of 3ms.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Computers
  topic: keywords
subject: School Laboratory
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: Monitor Screen
  topic: keywords
creator: Frisky Wijaya
creator: Dedy Hermanto
rights: Copyright (c) 2025 Frisky Wijaya, Dedy Hermanto https://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 319
ending page: 330
doi: 10.14421/jiska.2025.10.3.319-330

Android Malware Threats: A Strengthened Reverse Engineering Approach to Forensic Analysis
type: item
description: The widespread adoption of Android devices has rendered them a primary target for malware attacks, resulting in substantial financial losses and significant breaches of user privacy. Malware can exploit system vulnerabilities to execute unauthorized premium SMS transactions, exfiltrate sensitive data, and install additional malicious applications. Conventional detection methodologies, such as static and dynamic analysis, often prove inadequate in identifying deeply embedded malicious behaviors. This study introduces a systematic reverse engineering framework for analysing suspicious Android applications. In contrast to traditional approaches, the proposed methodology comprises six distinct stages: initialization, decompilation, static analysis, code reversal, behavioral analysis, and reporting. This structured process facilitates a comprehensive examination of an application’s internal mechanisms, enabling the identification of concealed malware functionalities. The findings of this study demonstrate that the proposed method attains an overall effectiveness of 84.3%, surpassing conventional static and dynamic analysis techniques. Furthermore, this research generates a detailed list of files containing specific malware indicators, thereby enhancing the effectiveness of future malware detection and prevention systems. These results underscore the efficacy of reverse engineering as a critical tool for understanding and mitigating sophisticated Android malware threats.
subject: Articles
  topic: section
subject: Malware Android
  topic: keywords
subject: Reverse Engineering
  topic: keywords
subject: Android Security
  topic: keywords
subject: Digital Forensics
  topic: keywords
subject: Cybersecurity
  topic: keywords
creator: Ridho Surya Kusuma
creator: Muhammad Dirga Purnomo Putra
rights: Copyright (c) 2025 Ridho Surya Kusuma, M Dirga Purnomo Putra https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-31
publication date: 2025-01-31
starting page: 122
ending page: 138
doi: 10.14421/jiska.2025.10.1.122-138

Deteksi Diabetes Mellitus dengan Menggunakan Teknik Ensemble XGBoost dan LightGBM
type: item
description: Diabetes mellitus is a metabolic disease characterized by elevated blood sugar levels due to impaired insulin secretion, insulin action, or both. The disease has a major impact on public health and contributes to high morbidity and mortality rates in many countries. Prevention and early detection are essential to reduce the adverse effects of this disease. This study aims to analyze and apply machine learning algorithms in detecting diabetes mellitus, focusing on the use of XGBoost and LightGBM algorithms. The dataset used in this study includes various features related to diabetes risk factors, such as age, gender, body mass index (BMI), hypertension, smoking history, and HbA1c and blood glucose levels. Preprocessing was performed to clean and balance the data using the SMOTE-Tomek technique. Next, the model was built and evaluated using the K-Fold cross-validation method to measure the accuracy and stability of the model. The results showed that the XGBoost model achieved 97.31% accuracy, while the LightGBM model produced 97.26% accuracy. Combining the two models through blending techniques resulted in an accuracy of 97.51%, indicating that the combination of models can improve prediction performance. This study shows the great potential of machine learning algorithms, especially XGBoost and LightGBM, in detecting diabetes mellitus accurately and efficiently. Hopefully, the results of this study can contribute to the development of decision support systems for more effective early diagnosis of diabetes.
subject: Articles
  topic: section
subject: Diabetes Mellitus
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: XGBoost
  topic: keywords
subject: LightGBM
  topic: keywords
subject: Early Detection
  topic: keywords
creator: Naufal Adhi Pratama
creator: Danang Wahyu Utomo
rights: Copyright (c) 2026 Naufal Adhi Pratama, Danang Wahyu Utomo https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 1
ending page: 12
doi: 10.14421/jiska.4908

Perbandingan Sensitivitas Metode SAW, MAUT dan WSM pada Anugerah Mutu Non-Akademik Universitas
type: item
description: A Decision Support System works best with a suitable method. Unfortunately, not all methods are equally used. Two rarely used methods are the MAUT and WSM methods. To determine whether a method is more suitable for a case than another, a sensitivity test is conducted. By conducting sensitivity tests between the two methods and other commonly used methods, such as SAW, in the same case, it’s possible to compare the sensitivity percentages of the three. One case that can be helped by a Decision Support System using the three methods is the ANOMIK assessment at universities. The three methods produced the same best alternative, namely Faculty 9. After conducting a sensitivity test, the results showed that the WSM method was the most sensitive, with a value of 4.954%, followed by the SAW method with a value of 4.901%, and finally the MAUT method with a value of 3.844%.
subject: Articles
  topic: section
subject: Decision Support System
  topic: keywords
subject: Simple Additive Weighting
  topic: keywords
subject: Multi Attribute Utility Theory
  topic: keywords
subject: Weighted Sum Model
  topic: keywords
subject: University
  topic: keywords
creator: Muhammad Galih Wonoseto
creator: Muhammad Abu Shaker Hunaif
rights: Copyright (c) 2025 Muhammad Galih Wonoseto, Muhammad Abu Shaker Hunaif https://creativecommons.org/licenses/by-nc/4.0
date: 2025-05-31
publication date: 2025-05-31
starting page: 207
ending page: 220
doi: 10.14421/jiska.2025.10.2.207-220

Enhancing Abstractive Multi-Document Summarization with Bert2Bert Model for Indonesian Language
type: item
description: This study investigates the effectiveness of the proposed Bert2Bert and Bert2Bert+Xtreme models in improving abstract multi-document summarization for Indonesians. This research uses the transformer model to develop the proposed Bert2Bert and Bert2Bert+Xtreme models. This research utilizes the Liputan6 data set, which comprises news data along with summary references spanning 10 years from October 2000 to October 2010, and is commonly used in many automatic text summarization studies. The model evaluation results using ROUGE-1, ROUGE-2, ROUGE-L, and BERTScore indicate that the proposed model exhibits a slight improvement over previous research models, with Bert2Bert performing better than Bert2Bert+Xtreme. Despite the challenges posed by limited reference summaries for Indonesian documents, content-based analysis using readability metrics, including FKGL, GFI, and Dwiyanto Djoko Pranowo, revealed that the summaries produced by Bert2Bert and Bert2Bert+Xtreme are at a moderate readability level, meaning they are suitable for mature readers and align with the news portal’s target audience.
subject: Articles
  topic: section
subject: Bert2Bert
  topic: keywords
subject: Abstractive
  topic: keywords
subject: Multi-Document
  topic: keywords
subject: Summarization
  topic: keywords
subject: Transformer
  topic: keywords
creator: Aldi Fahluzi Muharam
creator: Yana Aditia Gerhana
creator: Dian Sa'adillah Maylawati
creator: Muhammad Ali Ramdhani
creator: Titik Khawa Abdul Rahman
rights: Copyright (c) 2025 Aldi Fahluzi Muharam, Yana Aditia Gerhana, Dian Sa'adillah Maylawati, Muhammad Ali Ramdhani, Titik Khawa Abdul Rahman https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-31
publication date: 2025-01-31
starting page: 110
ending page: 121
doi: 10.14421/jiska.2025.10.1.110-121

Spatial Decision Support System to Determine the Feasibility of Evacuation Posts in Natural Disasters
type: item
description: This study aimed to improve the accuracy of determining the feasibility of evacuation posts after natural disasters using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) within a Spatial Decision Support System (SDSS). A dataset of 50 evacuation posts from the 2021 Mount Semeru eruption was analyzed. The Rank Order Centroid (ROC) method was applied for criteria weighting, and TOPSIS was used to process the data. Results showed 72% accuracy, confirming that TOPSIS is a passable method for assessing post-feasibility based on accessibility, sanitation, and refugee facilities. Although the focus is on evaluating post-disaster evacuation posts, the system can be adapted for use in various other types of disasters. However, it is still dependent on historical data and lacks real-time adaptability. Future research can integrate Artificial Intelligence (AI) and Machine Learning (ML) with real-time data to improve decision-making in disaster management.
subject: Articles
  topic: section
subject: After Natural Disasters
  topic: keywords
subject: Evacuation Posts
  topic: keywords
subject: Management Disasters
  topic: keywords
subject: Spatial Decision Support System
  topic: keywords
subject: Technique For Order Preference By Similarity to Ideal Solution (TOPSIS)
  topic: keywords
creator: Nuril Afni Alviola
creator: Agung Teguh Wibowo Almais
creator: A’la Syauqi
creator: Totok Chamidy
creator: Puspa Miladin Nuraida Safitri A Basid
creator: Anisa Anisa
creator: M. Dafa Wardana
rights: Copyright (c) 2025 Nuril Afni Alviola, Agung Teguh Wibowo Almais, A’la Syauqi, Totok Chamidy, Puspa Miladin Nuraida Safitri A Basid, Anisa Anisa, M. Dafa Wardana https://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 307
ending page: 318
doi: 10.14421/jiska.2025.10.3.307-318

Analisis Sistem Deteksi Citra untuk Optimalisasi Pengawasan Lalu Lintas Udara Menggunakan Algoritma YOLOv5
type: item
description: This study aims to develop an image detection system capable of identifying manned and unmanned aircraft objects to support air traffic surveillance. The increasing flight activity, both from commercial aircraft and drones, requires a more optimal surveillance system to connect the airspace efficiently. In this study, a Convolutional Neural Network (CNN) model utilizing the You Only Look Once version 5 (YOLOv5) method is employed to detect and classify objects in real-time from aircraft images. The methodology employed includes collecting aerial image data, labeling the data, and training object detection models using YOLOv5. The dataset used consists of 2,520 images of manned aircraft (warplanes) and 5,422 images of unmanned aircraft (drones). The experimental results demonstrate that the YOLOv5 model achieves high detection accuracy for both manned and unmanned aircraft, with a relatively fast inference time, thereby supporting the development of an effective air traffic surveillance system. This system is expected to be an integral part of a more sophisticated and responsive air traffic surveillance solution.
subject: Articles
  topic: section
subject: Convolutional Neural Network
  topic: keywords
subject: YOLOv5
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Aircraft Detection
  topic: keywords
subject: Air Traffic Surveillance
  topic: keywords
creator: Astika Ayuningtyas
creator: Imam Riadi
creator: Anton Yudhana
rights: Copyright (c) 2025 Astika Ayuningtyas, Imam Riadi, Anton Yudhana https://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 364
ending page: 376
doi: 10.14421/jiska.2025.10.3.364-376

Analisis Efektivitas Metode Filtering dan Intersection dalam Analisis Data Permukaan Bangunan dengan QGIS
type: item
description: This study evaluates the efficiency of two methods for processing geospatial building surface data, namely Filtering and Intersection, using a case study in Blitar Regency. The data for this research was obtained by comparing two sources: OpenStreetMap (OSM), which has a data completeness rate of 60%, and Google Open Building, with a data completeness rate of 90%. From these two sources, the data with the highest completeness, which is from Google Open Building, was selected for further analysis. The data processing was carried out using QGIS software, chosen for its capability to support various geospatial analysis methods. The comparison of the two methods was based on three main criteria: processing time, resource efficiency, and scalability. The results showed that the Filtering method outperforms in all these aspects. Filtering can complete processing in an average of 1.6 seconds, significantly faster than the Intersection method, which requires an average of 7 minutes and 50 seconds. In terms of resource efficiency, Filtering is also more economical, with an average CPU usage of 18.85% and memory usage of 121.4 MB, compared to the Intersection method’s 34.05% CPU usage and 236.4 MB of memory. Additionally, the Filtering method demonstrated better scalability, capable of handling larger datasets with fewer resources and less time. Therefore, the Filtering method is recommended for geospatial data processing that prioritizes speed, efficiency, and the ability to handle large and complex datasets.
subject: Articles
  topic: section
subject: Filtering
  topic: keywords
subject: Intersection
  topic: keywords
subject: Kabupaten Blitar
  topic: keywords
subject: Comparing
  topic: keywords
subject: QGIS
  topic: keywords
creator: Prana Wijaya Pratama Nandana
creator: Muhammad Faisal
rights: Copyright (c) 2025 Prana Wijaya Pratama Nandana, Muhammad Faisal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 351
ending page: 363
doi: 10.14421/jiska.2025.10.3.351-363