<![CDATA[This study aims to develop an interpretive Decision Tree model to identify depression levels in college students using Patient Health Questionnaire-9 (PHQ-9) data. Depression in college students is a growing mental health issue, while most machine learning models are still difficult to interpret. Therefore, this study uses Decision Tree as an explainable artificial intelligence (XAI) approach that is able to produce transparent and easy-to-understand decision rules. The research method uses a supervised learning approach with PHQ-9 data represented in text form and converted into numeric using Label Encoding. The target variable is formed based on the depression level category from the total PHQ-9 score. The model was evaluated using a 5-fold cross-validation technique with accuracy, precision, recall, and F1-score metrics. The results showed that the model achieved an accuracy of 54.4% in cross-validation and 68% in training data, with a macro F1-score of 0.67. Feature importance analysis showed that the PHQ2 variable was the most dominant factor in classifying depression. In addition, the Decision Tree structure was able to provide a clear interpretation of the pattern of depressive symptoms in college students. This study shows that Decision Tree has the potential to support early detection of student depression in a more transparent and interpretable manner.]]>
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