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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Rani, Putri Meuthia
Department of Computer Science, UIN Sumatera Utara Medan, Medan Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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Predicting Student Stress Levels Based on Lifestyle Factors Using the Catbost Algorithm Rani, Putri Meuthia; Zufria, Ilka
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24537

Abstract

This study developed a machine learning model to classify student stress levels based on lifestyle factors using the CatBoost algorithm. Data were collected from 630 students of the SciTech Faculty at State Islamic University of North Sumatra through a questionnaire comprising 14 Likert-scale items. Instrument validation was confirmed using Pearson’s r (>0.821, p < 0.05) and Cronbach’s Alpha (0.866). Preprocessing included outlier removal with IQR, feature encoding, stratified train-test split (80:20), and 5-fold cross-validation. The training set was imbalanced and addressed using the SMOTE technique. Model evaluation used accuracy (85%), precision, recall, and F1-score per class, with high recall (0.97) for moderate and improved F1-score (0.79) for low stress. Final classification used a 20% test subset (126 samples). Feature importance analysis identified task procrastination, poor sleep quality, and weak time management as key predictors. These findings affirm CatBoost's reliability through consistent results, scalability, and balanced evaluation metrics beyond mere accuracy.
Co-Authors Ilka Zufria