Clustering Student Competencies Using the K-Means Algorithm
This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas...
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| Veröffentlicht in: | Ultimatics : Jurnal Teknik Informatika Jg. 17; H. 1; S. 99 - 106 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
01.07.2025
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| ISSN: | 2085-4552, 2581-186X |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas of competence as features. The K-Means algorithm is used to group student data into several clusters based on academic grade patterns. The results show that the K-Means algorithm is quite effective in identifying the initial pattern of student competence, with a Silhouette Score of 0.3489, which falls into the medium category. This study concludes that the use of the K-Means algorithm alone is sufficient to support the analysis of student areas of competence, with potential applications as a recommendation system for students in choosing elective courses and as an evaluation tool for study programs to identify areas of competence that need improvement. |
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| ISSN: | 2085-4552 2581-186X |
| DOI: | 10.31937/ti.v17i1.4071 |