Utilizing the K-Means Clustering Algorithm for Analyzing Student Achievement Assessment at SMK Negeri 1 Gowa

Abstract

Student achievement assessment is an integral part of the educational process that aims to measure student learning achievement. This study aims to analyze student achievement assessments at SMK Negeri 1 Gowa using the K-Means algorithm. This study uses student data from the 2021–2022 school year, grouped into three clusters: highest, medium, and sufficient. The analysis results show that K-Means successfully clusters students based on academic achievement. The first cluster displays focused students who excel in a few key subjects (PPKN, Physics, Chemistry, and Math); the second cluster shows students with excellence in certain subjects (PAI, Bahasa Indonesia, and History); and the third cluster displays students with the highest academic achievement in all subjects. Evaluation using the silhouette coefficient shows that cluster one has a range of 0.49–0.54, cluster two has a range of 0.49–0.56, and cluster three has a value of 0.50–0.55, indicating that the data density in each cluster is good. SMK Negeri 1 Gowa can use the results of this study as a basis for school evaluation to enhance student achievement.