Penerapan Algoritma K-Means pada Pengelompokan Kabupaten/Kota Berdasarkan Indikator Penerima Bantuan Sosial di Provinsi Jawa Tengah

Abstract

Poverty is defined as a state in which a person is unable to meet their basic needs such as food, clothing, shelter, and education. The purpose of this study is to group all districts and cities in Central Java Province into several clusters based on the similarity of the characteristics of social assistance recipients. The method used in this study is a non-hierarchical method, namely the k-means clustering method. The variables used in this study are the elderly, mental disabilities, poor families, victims of natural disasters. Based on the results of the research, four clusters were obtained, namely: Cluster 1 consists of 4 regencies/cities with high social assistance recipients, namely: Blora Regency, Kendal Regency, Batang Regency, Pemalang Regency. Cluster 2 consists of 8 regencies/cities with moderate social assistance recipients, namely: Cilacap, Banyumas, Purbalingga, Magelang, Klaten, Rembang, Demak, Temanggung Regencies.Cluster 3 consists of 3 regencies/cities with low social assistance recipients, namely: Kebumen, Wonogiri, Tegal Regencies.Cluster 4 consists of 16 regencies/cities with very low social assistance recipients, namely: Banjarnegara, Purwokerto, Wonosobo, Boyolali, Karanganyar, Sragen, Grobongan, Pati, Kudus, Semarang, Pekalongan, Brebes, Surakarta, Salatiga, Semarang City, Tegal. Then a significance test was carried out with the ANOVA test, it was known that the X3 variable had a significance value of 0.000 < 0.05, then the X3 variable had the greatest difference between the X1, X2, and X4 variables.