<![CDATA[Academic stress is a psychological condition commonly experienced by students due to increasing academic, social, and emotional demands. Without early detection, this issue can negatively impact mental health and academic performance. This study aims to develop and evaluate a machine learning–based model using the Random Forest algorithm to predict students’ stress levels based on the Indonesian version of the Depression Anxiety Stress Scale-21 (DASS-21). Data were collected from 143 university students in Yogyakarta who completed the DASS-21 questionnaire, and stress subscale scores derived from seven items were multiplied by two and categorized into five levels: Normal, Mild, Moderate, Severe, and Extremely Severe. The dataset was then cleaned, labeled, normalized, and split into training and testing subsets (60:40) using stratified sampling. Model performance was evaluated using accuracy, macro-F1, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The Random Forest model achieved an accuracy of 87.93%, macro-F1 of 0.7047, MAE of 0.121, and RMSE of 0.347, with the best performance observed in the Severe (F1 = 0.9387) and Normal (F1 = 0.9230) categories. To enhance practical usability, the model was deployed in a web-based system named StressPredict, which provides real-time predictions, class probabilities, and an analytical dashboard for monitoring student populations. The findings confirm that the Random Forest algorithm is effective for multi-level stress classification and demonstrates strong potential as a digital mental health monitoring tool in higher education environments, supporting early screening and informed interventions for student well-being.]]>
