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All Journal Journal of Computing Theories and Applications
Tohomdet, Loreta Katok
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Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : Journal of Computing Theories and Applications

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The Llama–ARCS Adaptive Learning framework: AI–VR Integration System for Real-Time Motivational Feedback in Higher Education Evwiekpaefe, Abraham Eseoghene; Chinyio, Darius Tienhus; Tohomdet, Loreta Katok
Journal of Computing Theories and Applications Vol. 3 No. 3 (2026): JCTA 3(3) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15031

Abstract

This study developed and evaluated an AI-integrated Virtual Reality (VR) system designed to enhance personalized learning in higher education. While VR improves engagement, existing systems often lack adaptivity or experience high latency during AI interactions. To address these limitations, this research introduces a novel integration of a cache-optimized Llama 2 Large Language Model (LLM) that delivers real-time, motivationally grounded feedback. The system was implemented using Unity 3D and validated with 50 undergraduate students. Technical validation showed that the cache layer reduced interaction latency from 17.7 ms to 14.2 ms and maintained zero system crashes throughout the pilot. Learner motivation was assessed using Keller’s ARCS model, yielding mean scores ranging from 4.08 to 4.69 across all dimensions. Independent t-tests (p > 0.05) and negligible effect sizes (Cohen’s d < 0.2) revealed no significant difference between technical (ICT) and non-technical (Physics) students. These findings confirm that the proposed system effectively bridges technological and motivational gaps, providing a robust model for adaptive, immersive education.
Android Malware Detection Using Machine Learning with SMOTE-Tomek Data Balancing Masari, Maryam Sufiyanu; Danladi, Maiauduga Abdullahi; Onyinye, Ilori Loretta; Tohomdet, Loreta Katok
Journal of Computing Theories and Applications Vol. 3 No. 3 (2026): JCTA 3(3) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15084

Abstract

This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.
Co-Authors Chinyio, Darius Tienhus Danladi, Maiauduga Abdullahi Evwiekpaefe, Abraham Eseoghene Masari, Maryam Sufiyanu Onyinye, Ilori Loretta