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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Stella Crystal
STMIK TIME
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Student Mental Health Monitoring System Based on Daily Activities with the SVM Method Stella Crystal; Robby Huang; Devi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2299

Abstract

Student mental health is a crucial issue that requires effective and responsive self-monitoring systems. This study aims to develop "LacakJiwa," an Android-based mobile application designed to monitor student mental health through the analysis of daily activity patterns. The method employed is the Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel to classify mental health risks into low and high categories. Input data includes sleep duration, daily step count, gadget usage, and social interaction duration collected from 146 student data entries. The SVM model is integrated into the application using TensorFlow Lite to enable on-device classification, ensuring user privacy through SQLite local database storage. Testing results on 44 test samples showed an accuracy rate of 52.27%, precision of 36.36%, and recall of 22.22%. While the system was successfully implemented technically, the low recall value indicates significant challenges in detecting complex non-linear behavioral patterns in students. This research provides a foundation for developing digital self-control instruments that are adaptive to Indonesian local culture.
Co-Authors Devi Robby Huang