Optimasi Model XGBoost dengan Genetic Algorithm untuk Prediksi Kesehatan Mental Siswa Sekolah Menengah Berbasis Machine Learning

Nor Riduan (Universitas Amikom Yogyakarta)
Alva Hendi Muhammad (Universitas Amikom Yogyakarta)

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Vol 9 No 4 (2025): OCTOBER-DECEMBER 2025

Mental health is a vital aspect of human well-being, yet often neglected. Recent studies report a rise in depression, anxiety, and stress among adolescents, especially post-COVID-19. Machine learning has emerged as a powerful tool for predicting mental health conditions. This study employs the XGBoost Regressor using a regression-based ML approach to predict mental health high school students. To enhance accuracy, hyperparameter optimization is conducted using a Genetic Algorithm (GA) to identify the optimal parameter set. The baseline model achieved an MSE of 0.3698, RMSE of 0.6081, and MAPE of 14.09%. After GA optimization, performance improved to an MSE of 0.3092 (16.4% reduction), RMSE of 0.5560 (8.6% reduction), and MAPE of 12.88% (8.6% reduction). These results demonstrate the model's effectiveness for early mental health screening in educational settings, enabling timely interventions by school counselors and healthcare providers.