Journal of Information Technology and Cyber Security

Explainable Artificial Intelligence Analysis of Transfer Learning Models for Alzheimer’s Disease MRI Classification

Dea Amanda Salsabila, Ghaluh Indah Permata Sari, Fajar Astuti Hermawati — Tue, 27 Jan 2026 00:00:00 +0000

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.

RPG-Based Educational Game for Personal Data Security Awareness in Elementary School Students: A Design and Usability Study

Wed, 28 Jan 2026 00:00:00 +0000

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.

Classification of Volcanic Status Events Using Autocorrelation and Support Vector Machine Methods

Fridy Mandita, Muhammad Arif Fajriyansah — Mon, 02 Feb 2026 00:00:00 +0000

Volcanic eruption disasters occur frequently in Indonesia due to the high density of active volcanoes, posing persistent risks to surrounding communities and infrastructure. Effective mitigation of these hazards is challenged by limitations in monitoring systems, particularly related to instrumentation coverage and the availability of expert human resources. One critical aspect of volcanic monitoring is the accurate classification of seismic activity, which reflects subsurface volcanic processes and supports timely hazard assessment. This study addresses the challenge of reliably classifying volcanic seismic events by proposing an integrated framework that combines autocorrelation-based signal characterization with Support Vector Machine (SVM)–based multi-class classification, supported by Z-score normalization during data preprocessing. The framework is designed to enhance feature consistency and robustness against noise commonly present in volcanic seismic signals. To evaluate its effectiveness, three SVM kernel functions—linear, polynomial, and radial basis function (RBF)—are systematically assessed under identical experimental conditions. The results demonstrate that the polynomial SVM kernel with a degree of two provides the most reliable classification performance, achieving an accuracy of 0.9605. In addition, the application of Z-score normalization substantially improves model stability and overall performance across all kernel configurations, indicating that feature scaling plays a critical role in SVM-based seismic classification. Performance variations among kernels suggest that non-linear feature representations are better suited to capture the complex characteristics of volcanic seismic signals, while classification errors are primarily influenced by class imbalance in underrepresented event types. These findings indicate that the proposed framework effectively supports automated volcanic seismic signal analysis and has the potential to enhance the reliability of seismic-based volcanic activity monitoring.

A Data Driven Approach for Information Technology Risk Modelling and Visualization: Integrating ISO 31000 and Monte Carlo Simulation

Rahmania Kumalasari, Lutfiyah Dwi Setia, Tri Septianto — Wed, 04 Feb 2026 00:00:00 +0000

Information technology (IT) plays a critical role in enhancing organizational efficiency, accelerating decision-making, and strengthening competitiveness. However, as a core infrastructure, IT also introduces various risks that must be managed effectively to ensure business continuity. This study examines IT risk management at Company XYZ by integrating the ISO 31000 framework with the Monte Carlo Simulation method to quantify potential losses from 18 identified risk categories, including system failure, human error, cyberattacks, and natural disasters. To improve the interpretation and communication of risk outcomes, the research employs interactive data visualization using the Shiny dashboard (R). The simulation results show an average expected annual loss of IDR 478 million, with major risks originating from data corruption, backup failures, and cybercrime, while external factors such as earthquakes and fires also have significant impacts. This integrative approach demonstrates how ISO 31000, Monte Carlo Simulation, and interactive visualization can strengthen data-driven and transparent IT risk management for informed organizational decision-making. However, this study is limited to a single organizational case and simulated data assumptions, which may affect the generalizability of the findings.

Evaluation of Public Satisfaction with OpenSID-Based E-Government Services at the Village Level Using the Customer Satisfaction Index and e-GovQual Dimensions

Adib Pakarbudi, Krisna Febriansyah — Sun, 08 Feb 2026 00:00:00 +0000

E-Government is an important innovation in improving the efficiency, transparency, and quality of public services, including public services at the village government level. However, the implementation of e-government in Purworejo, Pungging District, Mojokerto Regency still faces various problems, such as low digital literacy and limited user support features. This condition is in line with the findings of the UN e-Government Survey 2024 which stated that approximately 22.4% of the global population is still lagging behind in accessing digital services due to infrastructure and literacy gaps, especially in developing regions. This study aims to analyze the level of public satisfaction with e-government services in the OpenSID-based village service system using the Customer Satisfaction Index (CSI) method. This study used a quantitative approach by collecting data through questionnaires from 273 respondents. Satisfaction measurements were carried out based on six dimensions of e-Government Quality (e-GovQual): Ease of Use, Trust, Functionality of the Interaction Environment, Reliability, Content and Appearance of Information, and Citizen Support. The results showed a CSI value of 70%, which is included in the satisfied category. The Trust dimension obtained the highest score of 0.65, followed by Reliability, Functionality of the Interaction Environment and Ease of Use at 0.60 and Content and Appearance of Information at 0.58. In contrast, the Citizen Support dimension had the lowest score of 0.46 and is an aspect that requires attention and improvement. This study provides an empirical contribution in the evaluation of e-government services at the village level by identifying priority service dimensions, especially Citizen Support, as a basis for improving the quality of digital public services and demonstrating the effectiveness of integrating the e-GovQual model and Customer Satisfaction Index in the context of village government.