Klasifikasi Burnout Mahasiswa menggunakan Algoritma Random Forest dengan Analisa Feature Importance

Abstract

Prolonged stress in higher education environments can lead to emotional, physical, and mental exhaustion among students, negatively affecting concentration, academic performance, and mental health. This study aims to classify student burnout levels using the Random Forest algorithm and to analyze model performance as well as dominant contributing factors through a feature importance approach. The dataset was obtained from the Kaggle platform and consisted of 1,100 samples covering psychological, physiological, environmental, academic, and social variables. The research methodology included data preprocessing, data balancing using the Synthetic Minority Over-sampling Technique (SMOTE), and data splitting with six scenarios: 70:30, 75:25, 78:22, 80:20, 83:17, and 87:13. Experimental results showed that all splitting scenarios produced relatively stable performance, with the 80:20 split achieving the highest accuracy of 89%, while other scenarios ranged from 85% to 87%. Feature importance analysis indicated that physiological factors, particularly blood_pressure, had the most significant contribution to student burnout classification.