APLIKASI K-MEANS DAN FUZY CLUSTERING DALAM PENGELOMPOKAN KECAMATAN DI KABUPATEN BANYUMAS
Clustering N objects into c clusters can be used to get information about data observation. Among the clustering methods are K-Means (KMC) and Fuzzy C-means (FCM) methods. In the K-means method, objects are members or not members of the cluster, while in the FCM method, objects are included in the cluster based on the degree of membership. This study discusses the implementation of KMC and FCM in the custering of sub-districts in Banyumas Regency based on total of population, the number of health workers and the number of health facilities and infrastructure. The results showed that the KMC and FCM methods produced the same cluster membership. Furthermore, the analysis of clustering based on the number of population, the number of health workers and the number of health facilities and infrastructure (scenario 1) and based on the number of health workers and the number of health facilities and infrastructure which have been corrected by population (scenario 2). The percentage of the variance ratio between clusters to the total variance in scenario 1 is 69% while in scenario 2 it is 85%. Clustering based on scenario 2 is better than scenario 1.