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

• Published in: Journal of Embedded Systems Security and Intelligent Systems pp. 60 - 67
• Main Authors: Andi Akram Nur Risal, Dyah Darma Andayani, Muh Ilham Suherman, Andi Baso Kaswar
• Format: Journal Article
• Language: English
• Published: 23.03.2024
• ISSN: 2745-925X; 2722-273X; 2722-273X
• DOI: 10.59562/jessi.v5i1.2178

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.