Comparison of Machine Learning for Mental Health Identification (The DASS-21 Questionnaire) - Wardhani, Retno; Nafi'iyah, Nur (Universitas Negeri Padang, 2025)
Stress appears at almost any age. Stress can disrupt mental and physical balance, and even students can experience it. Early detection of an individual's emotions is crucial. Researchers hope that by taking such actions, an individual can maintain self-control and prevent the stress they are experiencing from worsening. Bodily characteristics such as speech, body language, eye contact, and facial expressions indicate stress, depression, and anxiety. Psychological activities and human life are associated with physiological emotions. The three categories of negative thoughts or sad emotions are stress, anxiety, and depression. This research assesses or finds students who experience anxiety, depression, and stress. This study compares methods for determining mental health through the distribution of DASS-21 scale questionnaires. The researcher classified the research results using Naive Bayes, Decision Tree, k-NN, SVM, and Logistic Regression methods. According to experiments, SVM is effective for identifying mental health anxiety, depression, and stress with accuracy, recall, and precision of 0.86, 0.90, and 0.80. At Universitas Islam Lamongan, 138 engineering faculty students answered the DASS-21 questionnaire.