Journal of Information Technology and Cyber Security
Journal of Information Technology and Cyber Security (JITCS) is a refereed international journal whose focus is on exchanging information relating to Information Technology and Cyber Security in industry, government, and universities worldwide. The thrust of the journal is to publish papers dealing with the the design, development, testing, implementation, and/or management of Information Technology and Cyber Security, and also to provide practical guidelines in the development and management of these systems. The journal will publish papers in Information Technology and Cyber Security in the areas of, but not limited to: 1. Enterprise Systems (ES): o Enterprise Resource Planning, o Business Process Management, o Customer Relationship Management, o System Dynamics, o E-business and e-Commerce, o Marketing Analytics, o Supply Chain Management and Logistics, o Business Analytics and Knowledge Discovery, o Production Management, o Task Analysis, o Process Mining, o Discrete Event Simulation, o Service Science and Innovation, and o Innovation in the Digital Economy. 2. Information Systems Management (ISM): o Software Engineering, o Software Design Pattern, o System Analysis and Design, o Software Quality Assurance, o Green Technology Strategies, o Strategic Information Systems, o IT Governance and Audits, o E-Government, o IT Service Management, o IT Project Management, o Information System Development, o Research Methods of Information Systems, o Adoption and Diffusion of Information Technology, o Health Information Systems and Technology, o Accounting Information Systems, o Human Behavior in Information System, o Social Technical Issues and Social Inclusion, o Domestication of Information Technology, o ICTs and Sustainable Development, o Information System in developing countries, o Software metric and cost estimation, o IT/IS audit, and o IT Risk and Management. 3. Data Acquisition and Information Dissemination (DAID): o Open Data, o Social Media, o Knowledge Management, o Social Networks, o Big Data, o Web Services, o Database Management Systems, o Semantics Web and Linked Data, o Visualization Information, o Social Information Systems, o Social Informatics, o Spatial Informatics Systems, and o Geographical Information Systems. 4. Data Engineering and Business Intelligence (DEBI): o Business Intelligence, o Data Mining, o Intelligent Systems, o Artificial Intelligence, o Autonomous Agents, o Intelligent Agents, o Multi-Agent Systems, o Expert Systems, o Pattern Recognition, o Machine Learning, o Soft Computing, o Optimization, o Forecasting, o Meta-Heuristics, o Computational Intelligence, and o Decision Support Systems. 5. IT Infrastructure and Security (ITIS): o Information Security and Privacy, o Digital Forensics, o Network Security, o Cryptography, o Cloud and Virtualization, o Emerging Technologies, o Computer Vision and Image, o Ethics in Information Systems, o Human Computer Interaction, o Wireless Sensor Networks, o Medical Image Analysis, o Internet of Things, o Mobile and Pervasive Computing, o Real-time Systems and Embedded Systems, o Parallel and Distributed Systems, o Cyber attacks, o Machine learning mechanisms for cyber security, o Modern tools for improving cyber security, o Emerging trends in cyber security, o Cyber security in Internet of Things (IoT), and o Cyber security in Cloud.
Articles
32 Documents
RPG-Based Educational Game for Personal Data Security Awareness in Elementary School Students: A Design and Usability Study
Fahlefi, Muhamad Rizal;
Yudatama, Uky;
Sasongko, Dimas;
Nuryanto, Nuryanto;
Nugroho, Setiya;
Hendradi, Purwono
Journal of Information Technology and Cyber Security Vol. 4 No. 1 (2026): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya
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DOI: 10.30996/jitcs.133044
As more and more elementary school-aged children use the internet, they are more likely to be exposed to cybersecurity threats, especially when it comes to keeping their personal information safe. Various educational media have been developed to introduce cybersecurity concepts to children, but most remain passive and do not engage children in simulated real-life digital risk situations. This research addresses this gap by proposing an RPG-based educational game that integrates personal data security concepts into gameplay missions tailored to the cognitive characteristics of children aged 10–12. The goal of this study was to create and assess an educational game that could serve as a substitute learning tool for personal data security. The game was developed using the Game Development Life Cycle framework and implemented using RPG Maker MV. Usability testing involved 20 elementary school students and was carried out through direct observation of 13 game scenes. The success rate indicates the number of students who were able to complete each scene independently. The test results showed that the beginning and end of the game had low success rates, indicating issues with text readability, navigation clarity, and reflective elements. The results showed that iterative improvements in the beta phase improved interface clarity and the gameplay experience. The findings in this study indicate that usability-based improvements have an important role in the design of educational games for children, and RPG-based educational games have the potential to be interactive and contextual personal data security education media.
Explainable Artificial Intelligence Analysis of Transfer Learning Models for Alzheimer’s Disease MRI Classification
Salsabila, Dea Amanda;
Sari, Ghaluh Indah Permata;
Hermawati, Fajar Astuti
Journal of Information Technology and Cyber Security Vol. 4 No. 1 (2026): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya
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DOI: 10.30996/jitcs.133060
Alzheimer’s disease is a progressive neurodegenerative disorder that leads to cognitive decline and requires early and accurate diagnosis to slow disease progression. Magnetic resonance imaging (MRI) is widely used to detect structural brain changes associated with Alzheimer’s disease; however, manual interpretation of MRI scans is time-consuming and subject to observer variability. Deep learning approaches have shown strong potential in automated MRI analysis, but their black-box nature limits clinical trust and interpretability. This study proposes a transfer learning–based deep learning framework for Alzheimer’s disease classification, complemented by explainable artificial intelligence (XAI) techniques to analyze model predictions. A pretrained VGG16 model is employed to classify MRI images into four cognitive impairment categories: no impairment, very mild impairment, mild impairment, and moderate impairment. To enhance transparency, Grad-CAM, Saliency Maps, and Guided Grad-CAM are applied to visualize brain regions that contribute most to model predictions. Experimental results demonstrate that the proposed approach achieves 95.41% accuracy, indicating that a well-balanced network architecture combined with integrated explainability techniques leads to effective, interpretable classification. The visual explanations highlight clinically meaningful brain regions that align with known Alzheimer’s disease–related structural changes. These findings suggest that combining deep transfer learning with explainable artificial intelligence can provide accurate and interpretable decision support for Alzheimer’s disease diagnosis. This study is limited by the use of a single publicly available dataset and two-dimensional MRI slices, which may affect generalizability across clinical environments.