Penerapan K-Means Clustering Untuk Aplikasi Penentuan Prestasi Siswa (Studi kasus : SMP Tunas Karya Batang Kuis)

Abstract

Student achievement assessment is an essential aspect of education to evaluate the quality of the learning process. However, manually grouping student achievement data often requires considerable time and is prone to errors in decision-making. This study aims to apply the K-Means Clustering algorithm to group student achievement data at SMP Tunas Karya Batang Kuis. The data used consist of students’ academic scores, which are analyzed and grouped into three achievement categories: high, moderate, and low. The data processing stages include data collection, preprocessing, determining the number of clusters (k), and applying the K-Means algorithm to form student groups. The results show that the K-Means Clustering algorithm can effectively classify students based on their level of achievement. The implementation of this method is expected to assist the school in conducting evaluations, coaching, and making more accurate decisions to improve the quality of student learning.