Clustering of High School Students Academic Scores Using K-Means Algorithm

• Published in: Journal of information systems and informatics (Palembang.Online) Vol. 7; no. 1; pp. 572 - 586
• Main Authors: Azzahra, Chairunisa, Sriani, Sriani
• Format: Journal Article
• Language: English
• Published: Informatics Department, Faculty of Computer Science Bina Darma University 22.03.2025
• Subjects: clustering, data mining, scores, k-means
• ISSN: 2656-5935; 2656-4882
• DOI: 10.51519/journalisi.v7i1.1029

The clustering of student subject scores in senior high school is conducted using the K-Means Clustering algorithm. The issue addressed in this study is how to optimally group students based on their academic scores to help schools understand the distribution of student abilities. This clustering is essential as a foundation for evaluating and improving the learning system. The research methodology includes data collection and preprocessing, determining the optimal number of clusters using the Davies-Bouldin Index (DBI), and applying the K-Means Clustering algorithm. The analysis results indicate that the optimal number of clusters is three, with an average DBI value of 1.226. Cluster 0 is categorized as "very good" (46 students), Cluster 1 as "good" (70 students), and Cluster 2 as "less good" (51 students).The clustering results can be utilized for more targeted learning interventions and curriculum adjustments. Schools can implement remedial programs or additional classes for students in the "less good" cluster to improve their academic performance. Meanwhile, students in the "very good" cluster can be provided with advanced learning materials or opportunities to participate in academic competitions. Additionally, clustering outcomes provide valuable insights for refining teaching strategies, allocating resources more effectively, and personalizing learning approaches to suit each student's needs. Furthermore, these clustering results support academic decision-making by enabling educators and administrators to identify student performance trends and address potential learning gaps. This data-driven approach helps schools enhance overall educational quality by adapting teaching methods and policies based on empirical findings.