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All Journal Jurnal Informatika dan Rekayasa Perangkat Lunak
Noor Azizah
Universitas Diponegoro
Computer Science & IT

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Comparative Approaches to Clustering for Profiling Students in Educational Data Mining Noor Azizah; Kusworo Adi; Catur Edi Widodo
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

This study aims to compare the performance of five clustering algorithms, a K-Means, K-Medoids, Fuzzy C-Means (FCM), DBSCAN, and Gaussian Mixture Model (GMM) in profiling 239 students using quantitative data. The methodology includes data collection, refinement, transformation, application of clustering algorithms, and evaluation using the Silhouette Score, Davies–Bouldin Index, and execution time. The results indicate that K-Means provides the most balanced performance, achieving the highest Silhouette score with well-defined cluster separation. K-Medoids and GMM demonstrate competitive performance, while DBSCAN excels in detecting outliers but produces an excessive number of clusters, limiting its interpretability for profiling. FCM performs the weakest due to poor cluster separability. Overall, K-Means is recommended as the primary approach for student profiling, while other algorithms may complement specific analytical needs.
Co-Authors Catur Edi Widodo Kusworo Adi