Penerapan Algoritma K-Means Clustering dalam Pengelompokan Kelas Berdasarkan Nilai Akademik

The K-Means algorithm can be used to group data into one or more clusters.Data instances with similar representations are grouped into the same cluster, while those with dissimilar representations are assigned to different clusters.The clustering of student data, which consists of superior, average, and low-performing student groups or classes, can be utilized to assist schools in supporting students based on their diverse learning capacities and levels of participation in the learning process.K-means cluster computation is employed to present reports on student learning outcomes based on centralized assessment data.This assessment determines the characteristics that the computational process uses to identify students within superior, average, and low-performing classes.The objective of this project is to implement the K-Means algorithm using the Python programming language to evaluate its effectiveness in processing centralized assessment data for generating student performance reports and classifying students into appropriate class groups.This approach enables efficient and effective management and clustering of student data.