Prediksi Tingkat Stres Mahasiswa Selama Pembelajaran Daring Menggunakan Algoritma Machine Learning - Rocky, Rocky Khalifah Akbar; dede; Aldo, Aldo Septian Raharjo; Celvin, Celvin Immanuel Suhendar (Journal of Data Science Methods and Applications, 2025)
The sudden transition to online learning during the COVID-19 pandemic has had a significant psychological impacton students, particularly in the form of increased stress levels. This study aims toidentify and analyze the factors that influence student stress during online learningusing a quantitative approach and predictive modeling. Data were obtained from 100 students aged 18–25 years, covering variables such as screen time, sleep duration, physical activity, pre-exam anxiety, and changes inacademic performance. Statistical analysis showed that high screen time, less than 6 hours of sleep, andacademic anxiety were significantly associated with increased stress levels (p < 0.01). The Random Forest modelsuccessfully predicted stress categories with 82% accuracy and identified sleep duration as the mostdominant factor. These findings indicate the need for more adaptive academic policy reforms regarding mental health,including digital load management, healthy sleep education, and the integration of psychological support. This studyprovides an empirical basis for educational institutions to design data-driven preventive interventions toreduce the prevalence of stress among students.