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Computer Science (CO-SCIENCE)
Published by Universitas Bina Sarana Informatika
ISSN : -     EISSN : 27749711     DOI : https://doi.org/10.31294/coscience
Core Subject : Science,
Computer Science & IT
Computer Science (CO-SCIENCE) pertama kali publikasi tahun 2021 dengan nomor ISSN (Elektonik): 2774-9711 yang diterbitkan oleh Lembaga Ilmu Pengetahuan Indonesia (LIPI). Computer Science (CO-SCIENCE) adalah jurnal yang diterbitkan oleh Program Studi Ilmu Komputer Universitas Bina Sarana Informatika. Computer Science (CO-SCIENCE) terbit 2 kali setahun (Januari dan Juli) dalam bentuk elektronik. Redaksi menerima naskah berupa artikel ilmiah dan penelitian pada bidang: Networking, Aplication Mobile, Software Engineering, Web Programming, Mobile Computing, Cloud Computing, Data Mining, dan Aplikasi Sains.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 141 Documents
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Comparative Analysis of Machine Learning Models for Burnout Prediction in Generation-Z Anis Masruriyah; Sanggi Bayu Ardika; Ade Hikma Tiana; Budi Arif Dermawan
Computer Science (CO-SCIENCE) Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i2.13081

Abstract

Burnout has become an increasingly prevalent mental health issue among Generation Z due to the interaction of psychological and behavioral factors in a highly digitalized environment. This study aims to predict burnout risk levels using a multi-class machine learning classification approach. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM), encompassing data understanding, preprocessing, modeling, and evaluation. A synthetic dataset containing 10,000 records and 22 psychological, behavioral, and lifestyle attributes was used to classify burnout risk into three categories: low, medium, and high. To address class imbalance and ensure reliable performance estimation, Stratified K-Fold cross-validation was employed. Logistic Regression was implemented as a baseline linear model, while Random Forest represented a non-linear approach. Experimental results demonstrate that Random Forest achieved the best performance, obtaining a macro F1-score of 0.988 and outperforming Logistic Regression in multi-class burnout prediction. Feature importance analysis further revealed that psychological variables, particularly the wellbeing index and anxiety score, contributed more substantially to burnout prediction than behavioral variables such as screen time. These findings indicate that internal psychological conditions are stronger predictors of burnout risk than external digital behaviors. This study provides a comparative evaluation of linear and non-linear machine learning models in an imbalanced multi-class setting and offers an interpretable framework to support data-driven strategies for early burnout detection and mental health intervention.

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