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All Journal Journal of Information Systems and Informatics
Anisah
Institut Sains dan Bisnis Atma Luhur
Computer Science & IT

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Comparative Analysis of Machine Learning Algorithms for Predicting Difficulty Disengaging from Social Media Among Students Supardi; Djoko Soetarno; Rahmat Sulaiman; Chandra Kirana; Ari Amir Alkodri; Anisah
Journal of Information System and Informatics Vol 8 No 5 (2026): October
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i5.1735

Abstract

Social media use can adversely affect students’ academic functioning and psychological well-being when disengagement becomes difficult. This study compared Random Forest (RF), XGBoost, and Support Vector Machine (SVM) for predicting self-reported difficulty disengaging from social media among university students. A cross-sectional survey dataset comprising 118 responses was analyzed, including 75 positive (63.6%) and 43 negative (36.4%) cases. The binary target represented a behavioral proxy rather than a clinically validated diagnosis of social media addiction. Using balanced class weighting and 10-fold stratified cross-validation, SVM achieved the highest mean accuracy (77.2%) and ROC-AUC (0.804), followed by RF (73.0%) and XGBoost (66.8%). However, the performance difference between SVM and RF was not statistically significant (Nadeau–Bengio corrected *p* = 0.102), while RF demonstrated greater resampling stability. The findings provide preliminary evidence for algorithm selection in behavioral proxy classification and support future digital well-being research. Interpretation is limited by the small, single-institution sample and single-item outcome measure; therefore, the results should not be used for clinical screening or institutional deployment without external validation using established psychometric instruments.
Co-Authors Ari Amir Alkodri Chandra Kirana Djoko Soetarno Rahmat Sulaiman Supardi