Implementation of K-Means Clustering for Student Achievement Classification Using Academic Performance Indicators
DOI:
https://doi.org/10.30865/ijics.v10i2.9795Keywords:
K-Means Clustering, Student Achievement, Data Mining, Academic Performance, Educational Decision-MakingAbstract
Student achievement assessment in educational institutions is often conducted manually by relying on average scores or class rankings, making the results less objective and unable to represent students’ overall academic conditions. This study aims to implement the K-Means Clustering algorithm to classify student achievement using academic performance indicators. The dataset consists of 10 student records with four variables: average score, examination score, attendance percentage, and assignment score. The research stages include data collection, data validation, Min-Max normalization, initial centroid selection, Euclidean Distance calculation, cluster formation, centroid updating, and interpretation of clustering results. The number of clusters was set to K=3, representing high, medium, and low achievement categories. The results show that Cluster C1 consists of 4 students with high achievement, Cluster C2 consists of 3 students with medium achievement, and Cluster C3 consists of 3 students with low achievement. The final centroid values indicate that Cluster C1 has the strongest academic performance, while Cluster C3 requires more intensive academic support. These findings demonstrate that K-Means Clustering can classify student achievement objectively and support data-driven educational decision-making, academic guidance, and targeted learning strategy development.
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