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Journal of Information Technology and Computer Science
Published by Universitas Brawijaya
ISSN : 25409433     EISSN : 25409824     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Information Technology and Computer Science (JITeCS) is a peer-reviewed open access journal published by Faculty of Computer Science, Universitas Brawijaya (UB), Indonesia. The journal is an archival journal serving the scientist and engineer involved in all aspects of information technology, computer science, computer engineering, information systems, software engineering and education of information technology. JITeCS publishes original research findings and high quality scientific articles that present cutting-edge approaches including methods, techniques, tools, implementations and applications.
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Articles 284 Documents
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Participant-Driven Design of Community Program Topics for Aging Communities: A Text Clustering Approach to Activity Preferences Hapsari, Indri
Journal of Information Technology and Computer Science Vol. 11 No. 2: August 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2026112907

Abstract

The rapid growth of Indonesia’s elderly population highlights the need for community programmes that not only extend lifespan but also improve healthspan. This study aims to design ageing activity topics based on participants’ suggestions using Natural Language Processing (NLP) and unsupervised clustering. Data were collected from 213 open-ended responses from participants of the Center for Aging Wellness (CAW), University of Surabaya, covering liked aspects, improvement areas, and future topic suggestions. Text data were pre-processed (lowercasing, removal of punctuation, digits, and stopwords, and spelling normalization), then transformed using TF-IDF and clustered with K-Means. Five values of k (2–6) were tested, with k = 6 selected based on the highest silhouette score and interpretability. Six thematic clusters emerged: knowledge literacy (12.2%), appreciation and learning (35.7%), leisure and social activities (8.9%), brain health and dementia (18.3%), physical and mental health (15.5%), and financial independence (9.4%). These clusters were mapped into broader need categories and aligned with the Indonesia Longitudinal Aging Survey (ILAS) 2023 domains. As an exploratory study, the results support thematic mapping and programme planning rather than prediction. The findings demonstrate that participant feedback can be transformed into an evidence-based roadmap for more targeted and context-sensitive ageing community programmes.
Machine Learning Comparison for Housing Rehabilitation Beneficiary Selection in Surabaya Christy, Cathrine Abigael; Harida Budianti, Luluk; Hary Prasetyo, Daniel; Siswantoro, Joko
Journal of Information Technology and Computer Science Vol. 11 No. 2: August 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2026112908

Abstract

The Surabaya City Government launched the ‘Dandan Omah’ Social Rehabilitation Program for Uninhabitable Houses (RTLH) in 2022. However, beneficiary selection for the 2022–2024 period was conducted manually, which may have led to subjective and less targeted decisions. This study proposed a data-driven decision support approach by comparing Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) across 2, 4, and 5 classification tasks. A dataset of 1700 records comprising 11 features was evaluated using an 80:20 train–test split and  10-fold cross-validation. RF consistently achieved the best performance across all scenarios while SVM demonstrated competitive baseline performance. After tuning, DT surpassed SVM on 5-class tasks and matched RF on 2-class tasks, making optimized DT a viable interpretable alternative when decision accountability was essential. KNN consistently underperformed due to the curse of dimensionality. Overall performance declined as classification complexity increased, attributed to variability in field assessment standards. These findings suggested that RF and optimized DT could support more standardized and objective RTLH beneficiary selection. Future studies employing RF were recommended to identify influential input features to optimize model performance while maintaining explainability. Re-standardization of field assessment protocols was also recommended to improve data quality.
Early Detection of Digital Transaction Fraud in Banking Systems Using the Random Forest Algorithm Dhammayogi, Made Bagus Deva; Cokorda Rai Adi Pramartha
Journal of Information Technology and Computer Science Vol. 11 No. 2: August 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2026112912

Abstract

This research aims to develop methods for preventing and detecting cyberattacks in banking systems by leveraging machine learning techniques.[1] Cybersecurity threats are becoming increasingly complex and pose significant risks to banking institutions and their customers through various attack types including fraud, intrusion, and malware.[2][3], [4]The data science approach utilizing machine learning enables automatic and real-time identification of anomalous patterns in transactional and security log data.[2], [5] This study utilizes transaction datasets and security logs as primary data sources. Random Forest and XGBoost models were selected due to their capability to handle high-dimensional data and class imbalance. Model evaluation was conducted using accuracy, F1-score, and ROC-AUC metrics. The prototype implementation includes a visualization dashboard to assist end-users.[6], [7] Random Forest and XGBoost models are selected due to their capability to handle high-dimensional data and class imbalance.[8], [9] Systematic preprocessing and feature engineering steps will address missing values, normalization, and categorical encoding.[10], [11] Moreover, model evaluation will be conducted using metrics such as accuracy, F1-score, and ROC-AUC to ensure optimal performance.[12] The prototype system implementation includes a visualization dashboard and automatic prediction features for end-users.[13] The expected outcomes of this research are improved early detection and prevention of cyber threats in the banking sector, contributing to the advancement of AI-based security systems in Indonesia.[14] Experimental results demonstrate that tree-based ensemble learning models significantly outperform SVM in handling extreme class imbalance. XGBoost achieved the best performance with an ROC-AUC score of 0.995, followed by Random Forest at 0.992, whereas SVM achieved only 0.935. Model transparency analysis using SHAP confirmed that transaction frequency and spikes in transaction amounts were the most critical predictors for identifying fraud. Overall, this research concludes that integrating XGBoost with the SHAP approach yields a fraud detection system that offers not only high accuracy but also clear interpretability to support decision-making in the digital banking sector.
Multi-Level Ensemble Learning for Facial Expression Recognition on Imbalanced FER2013 Dataset Pratama, Muhammad Fajarivan; Mahmudy, Wayan Firdaus; Muflikhah, Lailil
Journal of Information Technology and Computer Science Vol. 11 No. 2: August 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2026112919

Abstract

Facial expression recognition remains a challenging task in computer vision, particularly due to class imbalance in datasets such as FER2013, where the Happy class accounts for 25.05% and Disgust only 1.52%, leading to biased predictions. This study proposes a Multi-Level Ensemble approach that integrates data diversity (bootstrap sampling in ELM), model diversity (CNN and CNN-ELM), and classifier diversity (ELM with different random seeds). The method is evaluated using Stratified 5-Fold Cross-Validation on 35,887 FER2013 images. Results show that pure ELM achieves 36.37% accuracy, CNN baseline 66.90%, CNN-ELM 67.28%, and ELM ensemble 67.34%. The proposed method achieves the best performance at 68.23%, improving the CNN baseline by +1.33%. Diversity analysis reports a disagreement rate of 21.2%, Q-statistic of 0.9313, and double-fault of 25.5%. These results indicate that the proposed framework effectively improves FER performance under class imbalance conditions.

Page 29 of 29 | Total Record : 284

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