Clustering Data Disabilitas menggunakan Algoritma K-Means di Kabupaten Cirebon
Fadhil Muhamad Basysyar
Abstract
The Cirebon Regency Government at the end of every year always carries out social assistance counseling activities. One of the targets of counseling is to people with disabilities. Because the data for people with disabilities in Cirebon Regency is quite large, it is necessary to group them by area that has residents with people with disabilities. The K-Means method is the right method for classifying data on persons with disabilities. The grouping results are measured by the Davies Bouldin index value in order to know the optimization results of the K-Means algorithm. The results show that the largest group is based on the number of people with disabilities, so cluster_0 : low group with 50 cases, cluster_2 : medium group with 53 cases, and cluster_1 : large group with 122 cases. Cluster_0 consists of sub-districts Waled, Ciledug, Losari, Karangsembung, Lemahabang, Susukan Lebak, Pangenan, Beber, Palimanan, Plumbon, Kedawung, Klangenan, Panguragan, Gegesik, Kaliwedi, Depok, Pabuaran, Karangwareng, Tengah Tani, Plered, Greged, and Suranenggala Cluster_1 consists of the districts of Pabedilan, Babakan, Sedong, Astanajapuran, Mundu, Talun, Sumber, Dukuntang, Weru, Gunungjati, Kapetakan, Arjawinangun, Ciwaringin, Articleeman, Gempol and Jamblang. And cluster_2 consists of Susukan and Gebang sub-districts.