Penerapan Algoritma K-Means dalam Pengelompokan Capaian Belajar Siswa dalam Matematika di MAN 1 Cirebon - Hani Tarkasih (Perspectives in Mathematics and Applications, 2025)
This study aims to apply the K-means Algorithm in grouping students' mathematics learning outcomes at MAN 1 Cirebon and measure its accuracy using the Davies-Bouldin Index. This type of research employs an experimental design with a quantitative approach. The research population consists of all students at MAN 1 Cirebon, with a sample of class X students majoring in Mathematics and Natural Sciences. The data used came from daily test scores and odd semester end-of-semester assessments. The results showed that the K-Means Algorithm successfully grouped student learning outcomes into three categories: Cluster 1 (high category), comprising 83 students; Cluster 2 (medium category), containing 64 students; and Cluster 3 (low category), comprising 27 students. The Davies-Bouldin Index value of 0.30 indicates that the grouping results are good quality. This research contributes to the application of data mining methods in supporting the objective and systematic evaluation of student learning outcomes.