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International Journal of Informatics and Communication Technology (IJ-ICT)
Published by Institute of Advanced Engineering and Science
ISSN : 22528776     EISSN : 27222616     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Communication Technology (IJ-ICT) is a common platform for publishing quality research paper as well as other intellectual outputs. This Journal is published by Institute of Advanced Engineering and Science (IAES) whose aims is to promote the dissemination of scientific knowledge and technology on the Information and Communication Technology areas, in front of international audience of scientific community, to encourage the progress and innovation of the technology for human life and also to be a best platform for proliferation of ideas and thought for all scientists, regardless of their locations or nationalities. The journal covers all areas of Informatics and Communication Technology (ICT) focuses on integrating hardware and software solutions for the storage, retrieval, sharing and manipulation management, analysis, visualization, interpretation and it applications for human services programs and practices, publishing refereed original research articles and technical notes. It is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in ICT.
Arjuna Subject : -
Articles 650 Documents
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An explainable AI-based intrusion detection system for cyber threats in stock markets using the SECURE framework Anandhi Ramani; Malathy S.; Ramya K.
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1726-1735

Abstract

As electronic trading technology accelerates the way trades occur in financial markets, the ability to manipulate the markets in many different ways has be come stronger through this same technology. Two ways this happens are via spoofing and layering, which pervert the way prices are determined, confuse in vestors, and reduce the level of trust in the integrity of financial markets. Exist ing surveillance mechanisms were predominantly rule-based or relied on opaque machine learning models, limiting their adaptability and interpretability. This study proposed SECURE, an explainable artificial intelligence (AI)-based in trusion detection system designed to detect and interpret spoofing and layering activities in real time. The framework integrated ensemble machine learning models, cybersecurity-inspired monitoring logic, and shapley additive expla nations (SHAP)-based explainability to achieve accurate detection with trans parent decision-making. Using Level-II order book data, the system analysed behavioural and microstructural trading features to classify manipulative pat terns while generating feature-level explanations for each alert. Experimental evaluation demonstrated that the proposed framework achieved high detection accuracy, low false-positive rates, and dependable real-time performance. The results indicated that combining intrusion detection principles with explainable AI improved regulatory interpretability and strengthened the effectiveness of au tomated market surveillance systems.
Interactive tax learning for secondary students using SmartTax (CukaiPintar) Fatma Alifah Mohd Jaaffar; Nor Hafizah Adnan
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1546-1560

Abstract

Tax literacy remains an essential yet underrepresented component of financial education in secondary schools. However, traditional teaching approaches and the lack of engaging digital tools have made it challenging for students to understand abstract taxation concepts. While mobile learning is increasingly used in education, few applications explicitly combine expert validation and usability evaluation to ensure instructional quality and user acceptance, particularly in tax education. This study developed and evaluated SmartTax (CukaiPintar), a mobile learning application designed to enhance tax literacy among Malaysian secondary students. A dual-method approach was employed: the fuzzy Delphi method (FDM) was used to obtain expert consensus on content and usability, while the technology acceptance model (TAM) guided the evaluation of student acceptance and behavioral intention. Ten mathematics educators participated in the expert validation phase, while 30 students were involved in the usability testing. Findings revealed strong correlations between perceived usefulness and attitude (r = 0.600) and between attitude and behavioral intention (r = 0.653), with other variables such as mobility, enjoyment, and self-efficacy also showing significant effects. These results underscore the value of integrating expert and user input in educational application development. SmartTax (CukaiPintar) offers evidence-based guidance for creating effective and student-centered mobile learning tools in tax education.
Design framework for higher education certificates using blockchain technology Salem S. M. Khalifa; Ali Mohamed E. Ejmaa; Abdulmawla Najih; Ismail Aboulqasim Omar Abumilyana
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1800-1815

Abstract

Digital academic credentials face persistent threats from credential fraud, while traditional centralized verification systems suffer from single points of failure and administrative delays. Although blockchain technology offers immutability and trust, existing solutions present significant research gaps: (1) direct on-chain storage violates data protection regulations; (2) a lack of decentralized identity governance leaves networks vulnerable to unauthorized; and (3) existing literature lacks formal operational algorithms and empirical benchmarking under scalable workloads. To resolve these gaps, this paper proposes a multi-tiered design framework for higher education certificates anchored in design science research (DSR) methodology. The proposed solution bridges these limitations by implementing a privacy-preserving hybrid architecture that combines off-chain encrypted File System storage with on-chain SHA-256 hash anchoring. To address governance vulnerabilities, W3C Decentralized Identifiers are integrated with a smart-contract Issuer Registry and a global revocation list to enforce strict issuer access control. Furthermore, formal algorithmic protocols are established for system enrollment, issuance, and off-chain encryption. Empirical evaluations conducted using Hyperledger Caliper demonstrate a 100% transaction success rate, a stable throughput of 17.7 TPS, and sub-second verification latencies (10.53 ms), validating the framework’s privacy compliance, security, and performance scalability for global academic verification.
Classification of cardiac ECG signals using quadratic discriminant analysis for atrial fibrillation Wameedh Raad Fathel; Marwah Nabeel Ibrahim Al-Siraj; Maysaloon Abed Qasim; Marwa Mawfaq Mohamedsheet Al-Hatab
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1851-1860

Abstract

Accurate and timely detection of atrial fibrillation (AF) is a crucial issue in avoiding the risk of serious cardiovascular complications such as stroke and heart failure. This study proposed a machine learning-based system used for automated electrocardiogram (ECG) signal classification using features derived from spectral and temporal analyses. The study used a dataset obtained from the PhysioNet 2017 challenge database with 5000 samples labeled as normal rhythm (N), AF, and noisy rhythm (NR). Five primary features: average spectral energy, variation in spectral power, root mean square fluctuation, overall signal energy, and power variance were extracted, expressing both amplitude and frequency-domain characteristics of cardiac dynamics. 10-fold stratified cross-validation approach was used to evaluate the performance of the model using multiple classifiers, which are: quadratic discriminant analysis (QDA), support vector machine (SVM), random forest (RF), K-nearest neighbors (KNN), and multi-layer perceptron (MLP). Additionally, to improve model stability and accuracy, a soft voting-based ensemble model was developed. Although RF achieved the highest accuracy (94.15%), the ensemble model (93.21%) outperformed QDA and SVM. The ensemble approach achieved sensitivities ranging from 0.89 to 0.96 and specificities between 0.95 and 0.97 across all rhythm categories, demonstrating strong generalization.
Text clustering on computing publication using natural logarithm-based TF-IDF in K-means Jiel Vayad Ramadhan; Widodo Widodo; Ressy Dwitias Sari
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1635-1647

Abstract

Text clustering is essential for organizing large textual datasets, yet the impact of term frequency inverse document frequency (TF-IDF) logarithmic base selection remains underexplored. This study compares natural logarithm and base-10 logarithm in TF-IDF weighting applied to K-Means and hierarchical agglomerative clustering (HAC) of scientific publications from Universitas Negeri Jakarta across Information Systems, Computer Science, and Informatics Education departments. Clustering performance was evaluated using the Davies-Bouldin index (DBI), silhouette coefficient, and Calinski-Harabasz Index across k=2-5. Results show that both bases produce structurally equivalent clustering outcomes, with k=2 consistently optimal across all departments based on DBI and Silhouette, and consistent agreement between K-Means and HAC on this configuration. Stability analysis across five independent random seeds confirms zero variance at k=2 for two departments and the highest Silhouette score for the third. However, natural logarithm yields higher term weight magnitudes with wider contrast between dominant terms, enhancing qualitative interpretability of cluster themes. Thus, logarithm base selection does not affect clustering structure but meaningfully influences term weight representation, with natural logarithm preferred for thematic analysis. This provides clear, empirically grounded guidance for TF-IDF-based text clustering pipelines. This approach can be further extended to hybrid weighting techniques and additional clustering algorithms.
Machine learning analysis of telecom customer churn in California Tianyu Lan; Hanan Aldowah; Sohail Ahmed Shahani
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp2000-2010

Abstract

Customer churn is a significant concern in the telecommunications industry, particularly in highly competitive and price-sensitive markets such as California. Although machine learning methods are widely applied for churn prediction, their practical adoption is often limited by insufficient interpretability. This study develops an interpretable and business-oriented churn analysis framework by comparing multiple machine learning models, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), combined with shapley additive explanations (SHAP)-based explanation techniques. Using a real-world California telecom customer dataset, the framework identifies key churn determinants related to contract characteristics, pricing sensitivity, and customer service behaviors. Experimental results demonstrate that XGBoost achieves the best predictive performance among the evaluated models, while SHAP provides transparent explanations of feature contributions, highlighting the importance of contractual and pricing factors for customer retention strategies.
Adversarial-resilient iot-enabled financial intelligence system using explainable deep learning for sentiment-driven stock trading decisions Suresh Talamala; Sukhada Aloni; J. Vijayalakshmi; Subham Pankaj Samantaray; K. Swanthana; Nisarg Gandhewar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1944-1953

Abstract

Financial stock market trend decision systems support the investment decisions by predicting future price movements and analyzing large-scale market data. Despite, IoT enabled trading systems continuously collects and exchange sentiment and market information are exposed to adversarial attacks, which misleads automated trading algorithms and results in inaccurate investment decision systems. To face this challenge, this study proposes an adversarial-resilient explainable artificial intelligence for sentiment-driven stock trading (ARXAI-SST) architecture. The proposed framework combines graph neural networks (GNN) for adversarial attack detection, FinBERT based sentiment representation, BiLSTM with attention (BiLSTM-Att) for the sentiment analysis, and SHAP-based explainable artificial intelligence for enhancing model interpretability. The experimentation outcomes show that the proposed framework accomplishes higher accuracy and robustness compared with the existing approaches while providing transparent insights into the trading decisions. The proposed model offers an efficient and reliable solution for secure sentiment driven stock trading in IoT enabled financial environments.
A technology-driven architecture for sustainable Barangay digital governance in Nueva Vizcaya Vilchor G. Perdido; Michael John R. Robles; Rodelyn M. Aguinaldo
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1493-1509

Abstract

The Philippines has prioritized the digitalization of government agencies, including local government units (LGUs), to improve service delivery and enhance transparency through e-government initiatives. Despite these national efforts, many Barangays in Bayombong continue to rely on manual processes, resulting in challenges related to data management, recordkeeping, and client transaction efficiency. This study developed a technology-driven framework to strengthen Barangay-level digital governance through the design and implementation of the integrated computer-based Barangay information system (iCBISys). Deployed in Barangay Busilac, the system features four core modules: digital logbook, resident profiling, certificate issuance with payment collection, and file complaint (e-Blotter). iCBISys was developed using the DevOps SDLC model to support iterative development and long-term sustainability, utilizing laravel framework for backend development and Bootstrap 5 for the user interface. A quantitative research design was employed, supported by survey questionnaires for evaluation. System performance based on the ISO/IEC 25010software product quality model yielded a mean score of 4.45 (strongly agree) from 20 evaluators. End-user training assessed through the ADDIE model obtained a score of 3.99 (Agree), while the impact of the system achieved a mean score of 4.37 (strongly agree). Findings indicate that iCBISys significantly streamlined Barangay operations and demonstrates strong potential to advance grassroots e-governance.
Detection of seizures from EEG using KNN and Opti-KNN Mohebbanaaz Mohebbanaaz; Madhusudhana Reddy Barusu; S. Bhargav; D. Lalitha Kumari; Vishnu Vardhan Rao G.; M. Jyothirmai
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1841-1850

Abstract

Epileptic seizures are caused by sudden and abnormal electrical disturbances in the brain, which can be identified through electroencephalogram (EEG) signals. Detecting seizures accurately at an early stage is important for providing timely treatment and improving patient care. In this study, two machine learning (ML) methods, namely k-nearest neighbour (KNN) and an optimized version of KNN (Opti-KNN), are proposed for automated seizure detection using EEG data. The research was carried using the BONN EEG dataset. The standard KNN model produced an accuracy of 94%, whereas the optimized KNN model improved the performance and achieved 97.33% accuracy. The findings indicate that proper hyperparameter tuning can greatly enhance the effectiveness of seizure classification. Overall, the proposed models provide a consistent and effective solution for automated seizure detection.
Evaluating browser-based immersive technology for cultural heritage Muhammad Dedi Irawan; Yusuf Ramadhan Nasution; Yustria Handika Siregar; Muhammad Eka; Nurmaliana Pohan
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1664-1672

Abstract

Digital cultural heritage requires accessible information and communication technology (ICT) channels that can present cultural content without requiring dedicated software installation. This study evaluates a browser based WebAR–WebVR platform for the interactive exploration of Malay hand-drawn batik. The evaluation comprised expert readiness review, functional verification, task-based interaction involving 60 participants aged 15–30 years, a five-point user-perception questionnaire, and qualitative technical observation. All eight predefined WebAR and WebVR functions produced the expected outcomes on Google Chrome using a Vivo Y36 5G smartphone and a VR Box. Mean scores were 4.60 for ease of interaction, 4.40 for increased interest in batik, 4.20 for attractiveness, and 4.60 for cultural-promotion usefulness. Triangulation of functional, perceptual, and operational evidence showed that successful task completion corresponded with positive ease ratings, while complex multimedia assets increased loading and rendering burden. Lighting, marker visibility, camera permission, device capability, and multimedia complexity were the main operating constraints. The findings provide practical evidence for browser based cultural ICT applications in museums, tourism, and digital education.

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