CLUSTERING LARGE APPLICATION USING METAHEURISTICS (CLAM) FOR GROUPING DISTRICTS BASED ON PRIMARY SCHOOL DATA ON THE ISLAND OF SUMATRA

Abstract

. K-medoids is one of the partitioning methods with the medoid as its center cluster, where medoid is the most centrally located object in a cluster, which is robust to outliers. The k-medoids algorithm used in this study is Clustering Large Application Using Metaheuristics (CLAM), where CLAM is a development of the Clustering Large Application based on Randomized Search (CLARANS) algorithm in improving the quality of cluster analysis by using hybrid metaheuristics between Tabu Search (TS) and Variable Neighborhood Search (VNS). In the case study, the best cluster analysis method for classifying sub-districts on the island of Sumatra based on elementary school availability and elementary school process standards is the CLAM method with k=6, num local = 2, max neighbor = 154, tls = 50 and set radius = 100-10:5. It can be seen that based on the overall average silhouette width value, the CLAM method is better than the CLARANS method.