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 10 No 3 September 2025 according to the decree, SK NOMOR 79/E/KPT/2023 and its certificate. The current Sinta Google Citation and h-index are 2074 and 21, 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. JISKa does not charge publication fees nor submission fees. The journal welcomes submissions on the following areas: Artificial Intelligence – Advances in machine learning, deep learning, neural networks, expert systems, reinforcement learning, and AI-driven automation. Computer Networks – Inv…
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: Comparative Analysis of Hybrid CNN-ViT and CNN for Brain Tumor Classification
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
items: Prediksi Kualitas Udara Menggunakan Metode CatBoost
items: Comparison of KNN and Random Forest Algorithms on E-Commerce Service Chatbot
items: Analisis Sentimen Ulasan Pengguna Aplikasi Alfagift Menggunakan Random Forest
items: Class Weighting Approach for Handling Imbalanced Data on Forest Fire Classification Using EfficientNet-B1
items: Revitalizing Art with Technology: A Deep Learning Approach to Virtual Restoration
items: Analisis Ketertarikan Pengguna Microsoft Excel Online untuk Pengolahan Data Silsilah Keluarga Menggunakan TAM dan TPB
items: Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions
items: Predicting Olympic Medal Trends for Southeast Asian Countries Using the Facebook Prophet Model
items: Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification
items: Klasifikasi Hewan Anjing, Kucing, dan Harimau Menggunakan Metode Convolutional Neural Network (CNN)

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

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) 2025 I Gede Surya Rahayuda, Ni Putu Linda Santiari https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-13
publication date: 2025-11-13
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) 2025 Naufal Adhi Pratama, Danang Wahyu Utomo https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-13
publication date: 2025-11-13
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

Prediksi Kualitas Udara Menggunakan Metode CatBoost
type: item
description: Air is essential for life, but industrial activities, forest fires, cigarette smoke, and transportation contribute to air pollution. AirVisual AQI 2024 data ranks Jakarta in 11th place globally, with the highest level of pollution, reaching 127, which is unhealthy for sensitive groups and poses a risk of causing serious illnesses, including skin and respiratory diseases. This research uses the CatBoost method to predict the air quality index using Jakarta SPKU data taken from Kaggle. The data is processed through pre-processing and divided into four models with different comparisons of training and testing data. Each model was tested with the parameters iteration, depth, learning rate, and l2_leaf_reg, using GridSearchCV to find the optimal combination. The results show that the model with 90% training data and 10% testing data provides the best accuracy of 97%, due to the larger proportion of training data. This research demonstrates that the CatBoost method can yield accurate air quality predictions, which is crucial in supporting efforts to mitigate the impact of pollution and enhance public health.
subject: Articles
  topic: section
subject: Prediction
  topic: keywords
subject: Air Quality
  topic: keywords
subject: Gradient Boosting
  topic: keywords
subject: CatBoost
  topic: keywords
subject: GridSearchCV
  topic: keywords
creator: Mohamad Arif Abdul Syukur
creator: Suhartono Suhartono
creator: Totok Chamidy
rights: Copyright (c) 2025 Mohamad Arif Abdul Syukur Syukur, Suhartono Suhartono , Totok Chamidy https://creativecommons.org/licenses/by-nc/4.0
date: 2025-05-31
publication date: 2025-05-31
starting page: 249
ending page: 258
doi: 10.14421/jiska.2025.10.2.249-258

Comparison of KNN and Random Forest Algorithms on E-Commerce Service Chatbot
type: item
description: Technology has a profound influence on our lives, with the expansion of e-commerce being a significant outcome that warrants attention. Given the prevalence of smartphones equipped with messaging apps and fast networks, people often utilize these platforms to communicate with sellers, offering a convenient way for sellers to engage efficiently with a diverse customer base. Recognizing this trend, there is a need for digital transformation of services to improve operational efficiency. Thus, this study aimed to compare the efficiency of classification algorithms in e-commerce service chatbots. The researcher employed machine learning techniques, specifically KNN and Random Forest algorithms, in this case. To assess the feasibility of the application, the chatbot results will be tested using the confusion matrix method to determine accuracy. From this study, it was found that the KNN method, combined with calculating word weight using TF-IDF, produces an accuracy value of 71.4%, thus confirming its feasibility.
subject: Articles
  topic: section
subject: Chatbot
  topic: keywords
subject: E-Commerce
  topic: keywords
subject: NLP
  topic: keywords
subject: KNN
  topic: keywords
subject: Random Forest
  topic: keywords
creator: Fardan Zamakhsyari
creator: Bagas Adi Makayasa
creator: R. Abudullah Hamami
creator: Muhammad Tulus Akbar
creator: Andi Cahyono
creator: Amirullah Amirullah
creator: Muhammad Zida Hisyamuddin
creator: Maria Ulfah Siregar
rights: Copyright (c) 2025 Fardan Zamakhsyari, Bagas Adi Makayasa, R. Abudullah Hamami, Muhammad Tulus Akbar, Andi Cahyono, Amirullah Amirullah, Muhammad Zida Hisyamuddin, Maria Ulfah Siregar https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-31
publication date: 2025-01-31
starting page: 100
ending page: 109
doi: 10.14421/jiska.2025.10.1.100-109

Analisis Sentimen Ulasan Pengguna Aplikasi Alfagift Menggunakan Random Forest
type: item
description: Alfagift is a mobile application developed by Alfamart to support online ordering, featuring promotions, transactions, ordering, and delivery from the nearest point based on the consumer’s address. User feedback on the Google Play Store reveals mixed sentiments, including both positive and negative responses, which developers can use as material to improve the application’s quality. This study focuses on assessing the sentiment of Alfagift app user reviews using the Random Forest algorithm. A total of 4,379 review data points were collected from the Google Play Store and grouped into two categories: positive and negative sentiment. The research steps include data collection, data labeling, data preprocessing, word weighting, dividing the data into training and testing sets, implementing the Random Forest algorithm, and model evaluation. The test results show that the Random Forest algorithm achieves an accuracy of 97.6% and an AUC of 0.98, which falls into the category of excellent classification. This research is expected to contribute to application developers’ understanding of user perceptions, enabling them to improve application quality and increase overall user convenience.
subject: Articles
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Alfagift
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Text Mining
  topic: keywords
subject: Review
  topic: keywords
creator: M. Bagus Prayogi
creator: Gustina Masitoh
rights: Copyright (c) 2025 M. Bagus Prayogi, Gustina Masitoh https://creativecommons.org/licenses/by-nc/4.0
date: 2025-05-31
publication date: 2025-05-31
starting page: 158
ending page: 170
doi: 10.14421/jiska.2025.10.2.158-170

Class Weighting Approach for Handling Imbalanced Data on Forest Fire Classification Using EfficientNet-B1
type: item
description: Wildfires pose a threat to ecosystems and human safety, necessitating the development of effective monitoring techniques. Detecting forest fires based on images of forest conditions could be a breakthrough. However, the model built from imbalanced data yields low accuracy. This research addresses the challenge of class imbalance in multiclass classification for forest fire detection using the EfficientNet-B1 model. This research examines the implementation of class weighting to improve model performance, with a particular focus on minority classes, specifically Fire and Smoke. A dataset of 7,331 training images was categorized into four classes. The results showed that employing the class weighting method achieved an accuracy of 90%. The training duration of 14 minutes and 45 seconds outperforms the data augmentation method in terms of time efficiency. This study contributes to the development of more effective methods for forest fire monitoring and provides insights for future research in machine learning applications in environmental contexts.
subject: Articles
  topic: section
subject: Image Classification
  topic: keywords
subject: Imbalanced Data
  topic: keywords
subject: Efficientnet-B1
  topic: keywords
subject: Forest Fire Detection
  topic: keywords
creator: Arvinanto Bahtiar
creator: Muhammad Ihsan Prawira Hutomo
creator: Agung Widiyanto
creator: Siti Khomsah
rights: Copyright (c) 2025 Arvinanto Bahtiar, Muhammad Ihsan Prawira Hutomo, Agung Widiyanto, Siti Khomsah https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-31
publication date: 2025-01-31
starting page: 63
ending page: 73
doi: 10.14421/jiska.2025.10.1.63-73

Revitalizing Art with Technology: A Deep Learning Approach to Virtual Restoration
type: item
description: This study evaluates CycleGAN’s performance in virtual painting restoration, with a focus on color restoration and detail reproduction. We compiled datasets categorized by art style and condition to achieve accurate restorations without altering the original reference materials. Various paintings, including those with a yellow filter, are used to create effective training datasets for CycleGAN. The model utilized cycle consistency loss and advanced data augmentation techniques. We assessed the results using PSNR, SSIM, and Color Inspector metrics, focusing on Claude Monet’s “Nasturtiums in a Blue Vase” and Hermann Corrodi’s “Prayers at Dawn.” The findings demonstrate superior color recovery and preservation of intricate details compared to other methods, confirmed through quantitative and qualitative evaluations. Key contributions include the application of CycleGAN for art restoration, model evaluation, and framework development. Practical implications extend to art conservation, digital library enhancement, art education, and broader access to restored works. Future research may explore dataset expansion, complex architectures, interdisciplinary collaboration, automated evaluation tools, and improved technologies for real-time restoration applications. In conclusion, CycleGAN holds promise for digital art conservation, with ongoing efforts aimed at integrating across fields for effective cultural preservation.
subject: Articles
  topic: section
subject: Art Restoration
  topic: keywords
subject: CycleGAN
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: PSNR
  topic: keywords
subject: SSIM
  topic: keywords
creator: Nurrohmah Endah Putranti
creator: Shyang-Jye Chang
creator: Muhammad Raffiudin
rights: Copyright (c) 2025 Nurrohmah Endah Putranti, Shyang-Jye Chang, Muhammad Raffiudin https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-31
publication date: 2025-01-31
starting page: 87
ending page: 99
doi: 10.14421/jiska.2025.10.1.87-99

Analisis Ketertarikan Pengguna Microsoft Excel Online untuk Pengolahan Data Silsilah Keluarga Menggunakan TAM dan TPB
type: item
description: The use of web-based applications such as Microsoft Excel Online has increased, including for recording family genealogy data. This study aims to analyze the factors influencing the intention and behavior of using this application based on the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and their combined framework. The constructs examined include perceived ease of use, perceived usefulness, attitude, subjective norm, perceived behavioral control, intention, and behavior. This quantitative study collected primary data through questionnaires distributed to family members using Microsoft Excel Online. Data analysis was conducted using SEM-PLS (Structural Equation Modeling-Partial Least Squares) with the assistance of SmartPLS version 4.1.0.2. The results indicate that perceived ease of use and perceived usefulness positively and significantly affect attitude, while attitude, subjective norm, and perceived behavioral control positively influence behavioral intention. Furthermore, behavioral intention has a positive effect on actual usage behavior. These findings suggest that Microsoft Excel Online is reliable for recording family genealogy data and supports technology acceptance among users.
subject: Articles
  topic: section
subject: Technology Acceptance Model
  topic: keywords
subject: Theory of Planned Behavior
  topic: keywords
subject: Microsoft Excel Online
  topic: keywords
subject: Genealogy
  topic: keywords
subject: Family
  topic: keywords
creator: Fathur Rachman Nufaily
creator: Maria Ulfah Siregar
rights: Copyright (c) 2025 Fathur Rachman Nufaily, Maria Ulfah Siregar https://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 279
ending page: 293
doi: 10.14421/jiska.2025.10.3.279-293

Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions
type: item
description: The K-Means algorithm is a fundamental tool in machine learning, widely utilized for data clustering tasks. This research aims to enhance the performance of the K-Means algorithm by integrating the Purity method, with a specific focus on clustering regions renowned for oil palm production in North Aceh. Oil palm cultivation is a vital agricultural sector in North Aceh, contributing significantly to the local economy and employment. This study examines two clustering techniques: the conventional K-Means algorithm and an optimized version, Purity K-Means. Integrating the Purity method enhances the efficiency of K-Means by reducing the number of required convergence iterations. The data used for clustering analysis is sourced from the Department of Agriculture and Food in North Aceh Regency and pertains to oil palm production in 2023. The findings indicate that the Purity K-Means approach notably reduces the iteration count and improves cluster quality. The average Davies-Bouldin Index (DBI) for standard K-Means is 0.45, whereas the Purity K-Means method lowers it to 0.30. Furthermore, applying the Purity method reduced the number of K-Means iterations from 15 to just 3. These results highlight an enhancement in clustering performance and overall efficiency.
subject: Articles
  topic: section
subject: K-Means Algorithm
  topic: keywords
subject: Purity Method
  topic: keywords
subject: Data Clustering
  topic: keywords
subject: Oil Palm Production
  topic: keywords
subject: Davies-Bouldin Index (DBI)
  topic: keywords
creator: Novia Hasdyna
creator: Rozzi Kesuma Dinata
creator: Balqis Yafis
rights: Copyright (c) 2025 Novia Hasdyna, Rozzi Kesuma Dinata, Balqis Yafis https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-31
publication date: 2025-01-31
starting page: 1
ending page: 15
doi: 10.14421/jiska.2025.10.1.1-15

Predicting Olympic Medal Trends for Southeast Asian Countries Using the Facebook Prophet Model
type: item
description: The Olympics are a world-class sporting event held every four years, serving as a meeting place for all athletes worldwide. The Olympics are held alternately in different countries. The Olympics were first held in Athens in 1896 and have now reached the 33rd Olympics, which will be held in Paris in 2024. Significant work has been conducted to develop prediction models, with a primary focus on enhancing the accuracy of predicting Olympic outcomes. However, low-performance regression algorithms are the main problem with prediction. By integrating custom seasonality with the Facebook Prophet prediction model, this study aims to enhance the accuracy of Olympic predictions. The proposed new model involves several steps, including preparing the data and initializing and fitting the Facebook-Prophet model with several parameters such as seasonal mode, annual seasonality, and prior scale. The model is tested using the Olympic dataset (1994–2024). The evaluation results indicate that this prediction model provides a reliable estimate of the total medals earned. On the Olympic Games (1994-2024) dataset, the model exhibits a very low error, as indicated by its MAE, MSE, and RMSE, and achieves an R² score of 0.99, which is close to perfect. This research shows that the model is effective in improving prediction accuracy.
subject: Articles
  topic: section
subject: Custom Seasonality
  topic: keywords
subject: Facebook-Prophet
  topic: keywords
subject: Forecasting
  topic: keywords
subject: Olympic Medals
  topic: keywords
subject: Time Series
  topic: keywords
creator: Bagus Al Qohar
creator: Yulizchia Malica Pinkan Tanga
creator: Putri Utami
creator: Maylinna Rahayu Ningsih
creator: Much Aziz Muslim
rights: Copyright (c) 2025 Bagus Al Qohar, Yulizchia Malica Pinkan Tanga , Putri Utami, Maylinna Rahayu Ningsih, Much Aziz Muslim https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-31
publication date: 2025-01-31
starting page: 16
ending page: 32
doi: 10.14421/jiska.2025.10.1.16-32

Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification
type: item
description: Heart disease is one of the leading causes of death worldwide. According to data from the World Health Organisation (WHO), the number of victims who die from heart disease reaches 17.5 million people every year. However, the method of diagnosing heart disease in patients is still not optimal in determining the proper treatment. Along with technology development, various models of machine learning algorithms and data processing techniques have been developed to find models that can produce the best precision in classifying heart disease. This research aims to create a machine learning algorithm model for categorizing heart disease, thereby enhancing the effectiveness of diagnosis and facilitating the determination of appropriate treatment for patients. This research also aims to overcome the limitations of accuracy in existing diagnosis methods by identifying models that can provide the best results in processing and analyzing health data, particularly in terms of heart disease classification. In this study, the XGBoost model was identified as the most superior, with an accuracy of 99%. These results demonstrate that the XGBoost model achieves a higher accuracy rate than previous methods, making it a promising solution for enhancing the accuracy of future heart disease diagnosis and classification.
subject: Articles
  topic: section
subject: Heart Disease
  topic: keywords
subject: SMOTE
  topic: keywords
subject: XGBoost
  topic: keywords
subject: KNN
  topic: keywords
subject: SVM
  topic: keywords
creator: Ahmad Ubai Dullah
creator: Aditya Yoga Darmawan
creator: Dwika Ananda Agustina Pertiwi
creator: Jumanto Unjung
rights: Copyright (c) 2025 Ahmad Ubai Dullah, Aditya Yoga Darmawan, Dwika Ananda Agustina Pertiwi, Jumanto Unjung https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-31
publication date: 2025-01-31
starting page: 48
ending page: 62
doi: 10.14421/jiska.2025.10.1.48-62

Klasifikasi Hewan Anjing, Kucing, dan Harimau Menggunakan Metode Convolutional Neural Network (CNN)
type: item
description: Animal classification is a complex challenge due to variations in shape, color, and patterns across species. Traditional methods, which rely on manual feature extraction, are often ineffective in handling such complexities. Therefore, this study employs Convolutional Neural Networks (CNNs) as a more accurate approach for automatic feature extraction and image classification. This research aims to develop an animal image classification model, specifically for dogs, cats, and tigers, utilizing CNNs. The dataset consists of 4,800 images obtained from Kaggle, which were divided into training, testing, and validation sets. The CNN model was built using TensorFlow/Keras, trained for 50 epochs, and evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The experimental results show that the model achieved an overall accuracy of 88%, with the highest performance in tiger classification (99% accuracy). However, distinguishing between dogs and cats remains a challenge, with an accuracy of 81% for both classes. The findings indicate that CNNs are effective in automatically classifying animal images, although challenges persist in differentiating visually similar species. This study lays the groundwork for further enhancements, such as refining the model architecture or utilizing data augmentation techniques to boost classification accuracy.
subject: Articles
  topic: section
subject: Klasifikasi hewan, Convolutional Neural Network (CNN), Deep learning, Pengolahan citra, Identifikasi spesies
  topic: keywords
creator: Murdifin Murdifin
creator: Shofwatul Uyun
rights: Copyright (c) 2025 Murdifin Murdifin, Shofwatul Uyun https://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 331
ending page: 340
doi: 10.14421/jiska.2025.10.3.331-340