Pengelompokan Tingkat Kesejahteraan dan Tekanan Psikologis Mahasiswa Menggunakan Mental Health Inventory-38 dengan Algoritma K-Means
- Yadullah Asy-syakiri Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Elvia Budianita
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Iwan Iskandar Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Fadhilah Syafria Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Yuli Widiningsih Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Student mental health is an urgent issue, particularly in STEM environments with high academic demands. This study aims to categorize the mental health patterns of students in the Faculty of Science and Technology at UIN Sultan Syarif Kasim Riau from the 2022–2025 cohorts using the K-Means Clustering algorithm. Data were collected via the MHI-38 questionnaire, which was adapted into Indonesian and validated by a clinical psychologist. Of the 559 data points, 522 valid data points were used after the data cleaning stage. Clustering evaluation utilized the Davies-Bouldin Index (DBI) and the Silhouette Coefficient with two distance calculation methods Euclidean and Manhattan across the range of k=2 to 10. The best configuration was obtained at k=2; Euclidean Distance yielded a DBI of 2.03 and a Silhouette of 0.18, while Manhattan Distance yielded a DBI of 2.19 and a Silhouette of 0.20. The clustering formed two clusters: Cluster 0 consisted of 334 students (64%) categorized as having psychological well-being with an average score of 3.82, and Cluster 1 consisted of 188 students (36%) categorized as experiencing psychological stress with an average score of 2.98. Cross-cohort analysis showed that the 2022 cohort was the most stable (72.4% in Cluster 0), while the 2023 and 2024 cohorts had a proportion of Cluster 1 of around 40%. By major, Computer Science was dominated by Cluster 1 (61.8%), while Mathematics and Electrical Engineering were dominated by Cluster 0 (83.3% and 82%, respectively). These results are expected to provide information to program administrators so they can evaluate their courses.
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Copyright (c) 2026 Elvia Budianita, Yadullah Asy-syakiri, Iwan Iskandar, Fadhilah Syafria, Yuli Widiningsih
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