Clustering Student Competencies Using the K-Means Algorithm
DOI:
https://doi.org/10.31937/ti.v17i1.4071Abstract
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.Downloads
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