Pengelompokkan Data Nilai Mahasiswa Menggunakan Metode K-Means - Kusuma Putra, Rafli Danu; Palupi, Kinanthi Sekar; Wakhidah, Nur (Algoritme Jurnal Mahasiswa Teknik Informatika, 2025)
Efficient and organized determination of student grades can help improve the quality of academic evaluation. In this research, the K-Means algorithm is used to cluster students based on academic grades. The K-Means algorithm is an unsupervised learning method often used to group data based on certain data The use of K-Means on student grade data aims to identify into several clusters based on similar characteristics so that students who have achievements can be identified. The implementation process involves the stages of data collection, data pre-processing, and data processing with Google Collaboratory platform using Python. The result showed that data grouping resulted in three clusters, namely students with low,medium, and high performance. The elbow method is used to determine the ideal number of clusters, and cluster quality is assessed by the silhouette coefficient. The best result showed a silhouette coefficient value of 0.458, indicating that the clusters formed were accurate. Thus, the K-Means algorithm is reliable for identifying students' academic performance.