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All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal INFOTEL
Ibnu Rivansyah Subagyo
Universitas Mercu Buana Yogyakarta, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Integrating Gamification in Expert Systems: A Novel Approach for Stress Disorder Diagnosis in Digital Mental Health putri taqwa prasetyaningrum; Norshahila Ibrahim; Reny Yuniasanti; Ibnu Rivansyah Subagyo
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1324

Abstract

The increasing prevalence of stress disorders highlights the need for innovative, accessible, and engaging diagnostic tools in mental health services. This study presents the design and implementation of a gamified expert system for diagnosing stress disorders, integrating gamification elements to enhance user engagement and reduce stigma. The system employs the forward chaining method to deliver high-accuracy, rule-based diagnoses while incorporating features such as points, rewards, and leaderboards to motivate user interaction.The system's development followed a user-centered design approach to ensure an intuitive interface aligned with user needs. Evaluation results demonstrated a diagnostic accuracy rate of 92%, validated by mental health professionals, alongside significant improvements in user engagement metrics, including session frequency and duration. Qualitative feedback indicated that gamification effectively reduced stigma and increased motivation for mental health assessments.These findings suggest that gamified expert systems can bridge gaps in accessibility and engagement in mental health services. This research contributes to the advancement of digital health technologies by providing practical insights into integrating gamification into expert systems to foster proactive mental health management.
Application of Gray Level Co-Occurrence Matrix (GLCM) for Abdominal Wave Image Classification: A Comparative Study of LVQ, KNN, and SVM Putri Taqwa Prasetyaningrum; Ibnu Rivansyah Subagyo
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.126

Abstract

Medical image classification is a crucial research area in medical imaging analysis to support clinical diagnosis. In this study, we implemented the Gray Level Co-Occurrence Matrix (GLCM) method to extract texture features from abdominal wave images and enhance classification accuracy. Three machine learning classification methods—Learning Vector Quantization (LVQ), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—were employed and compared based on their classification performance. The experimental results show that the KNN method achieved the highest accuracy of 96.83%, followed by SVM with 95.24%, and LVQ with 84.13%. These findings indicate that KNN is the most effective classification method for abdominal wave images among those tested. This study highlights the significance of texture feature extraction using GLCM in improving medical image classification accuracy. The results of this study can contribute to the advancement of digital healthcare technologies, particularly in gastrointestinal disorder detection and digestive health monitoring. Future research should explore hybrid deep learning approaches and larger datasets to further enhance classification accuracy and model robustness.
Co-Authors Norshahila Ibrahim Prasetyaningrum, Putri Taqwa putri taqwa prasetyaningrum Reny Yuniasanti