Comparative Study of Agglomerative Hierarchical Clustering and K-Means for Student Academic Stress Grouping
- Irfan Arifin Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Iwan Iskandar
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Elvia Budianita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Novi Yanti Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Fitri Insani Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Academic stress is a common problem experienced by college students due to high academic demands, parental expectations, and social pressures during their college years. The high levels of academic stress experienced by students underscore the need for a data-driven approach to more accurately identify and map students’ stress levels. This research aims to compare the performance of the Agglomerative Hierarchical Clustering (AHC) and K-Means methods in clustering students’ academic stress levels and to determine which method produces the best clustering quality. Data were obtained from the distribution of the Perception of Academic Stress Scale (PAS) questionnaire, consisting of 18 statement items, with 361 valid respondents from the Informatics Engineering Program at UIN SUSKA Riau, class of 2022–2025. The selection of the best linkage method in AHC was performed using the Cophentic Correlation Coefficient (CCC), where Ward Linkage was selected with the highest CCC value of 0.8180. Comparative evaluation was conducted using the Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index for variations in the number of clusters from K=2 to K=7. The test results showed that AHC Ward Linkage with K=2 was the best configuration with a Silhouette Coefficient of 0.4407 and a Davies-Bouldin Index of 0.8373, outperforming K-Means, which only excelled in the Calinski-Harabasz Index with a value of 419.7405 The clustering resulted in two clusters: High Stress with 244 students (67.6%) and Low Stress with 117 students (32.4%). The 2023 and 2024 cohorts had the highest proportions of high stress at 90.4% and 90.6%, respectively. This research contributes empirical evidence comparing hierarchy-based and partition-based clustering methods for academic stress data, while also demonstrating the use of the Cophenetic Correlation Coefficient as an objective basis for linkage method selection in AHC. It is hoped that the results of this study can serve as a basis for the institution in designing targeted mental health intervention programs for students.
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