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All Journal Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation
Rywalman Rante Pasang
Universitas Widya Gama Mahakam Samarinda

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Klasifikasi tingkat depresi mahasiswa menggunakan algoritma decision tree Ahmad Fadilla; Rywalman Rante Pasang; Cecillia Listia Anggraini; Sherly Virgoila Pidang; Aldi Bastiatul Fawait
Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation Vol. 1 No. 2 (2026)
Publisher : Universitas Widya Gama Mahakam Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/minabis.v1i2.28

Abstract

Depression is a mental health disorder commonly experienced by college students due to academic, social, and financial pressures. The high rate of depression among college students can impact academic achievement, social relationships, and overall quality of life. Therefore, a method is needed to quickly and accurately identify levels of depression. This study aims to classify college students' depression levels using the Decision Tree algorithm. The method used includes utilizing the Student Depression Dataset obtained from the Kaggle platform, data preprocessing, building a classification model using the Decision Tree algorithm, and evaluating model performance. The Decision Tree algorithm was chosen because it produces decision rules that are easy to understand and interpret. The test results obtained an accuracy value of 81.08%. This value indicates that the model is capable of classifying college students' depression levels quite well. Furthermore, the classification report results shows that the model has a precision value of 0.81 for both classes. The recall value for the depression class reached 0.88, indicating that the model successfully recognized most students experiencing depression. The research findings can assist educational institutions in early detection of students at risk of depression, allowing for more effective treatment and support.
Co-Authors Ahmad Fadilla Aldi Bastiatul Fawait Fawait Cecillia Listia Anggraini Sherly Virgoila Pidang