Clustering of High School Students Academic Scores Using K-Means Algorithm - Azzahra, Chairunisa; Sriani, Sriani (Universitas Bina Darma, 2025)
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