Analisis Gaya Hidup Mahasiswa dalam Memprediksi Tingkat Stres Menggunakan Algoritma Decision Tree dan Random Forest - Muchammad Thoha; Muhammad Ary Sanjaya Putra; Ary Kania Sya'diah; Aulia Nurhaliza; Diana Laily Fithri (JSI: Jurnal Sistem Informasi (E-Journal), 2025)
HIgh Stress levels in students are an important issue that can affect their mental health and academic performance. This study aims to analyze the influence of students' lifestyles on stress levels using Machine Learning approaches, specifically Decision Tree and Random Forest algorithms. Data was collected through surveys on sleep habits, diet, physical activity, and social media usage. The analysis results show that lifestyle has a significant correlation with stress levels, and the Random Forest model provides higher prediction accuracy than Decision Tree. The findings are expected to provide a basis for preventive decision-making to manage stress among students.