Implementasi K-Means Clustering Untuk Rekomendasi Kelas Unggulan di SMK 1 Teknologi dan Rekayasa Mimika - Desy Maryani; Nur Fadillah; Andi Alviadi Nur Risal; Andi Akram Nur Risal (Program Studi Teknik Komputer, 2024)
New student admission is a strategic step for schools to ensure the quality of education and optimal resource management. This research aims to analyse new student candidate data at SMK Negeri 1 Teknologi dan Rekayasa Mimika using the K-Means clustering method. Student data is grouped into three clusters based on academic grades to detect prospective students who have the potential to enter superior classes. The analysis results show that cluster 3 has the highest average academic score, making it possible for students in cluster 3 to enter the general superior class. cluster 2 showed excellence in certain aspects, particularly E grades, making it suitable for specific programmes oriented towards talent development. Finally, cluster 1 has the lowest average score and requires further assistance to improve academic performance. Evaluating the cluster results using Silhouette Score, all three clusters are in the