APPLICATION OF K-MEANS++ WITH DUNN INDEX VALIDATION OF GROUPING WEST KALIMANTAN REGION BASED ON CRIME VULNERABILITY

Abstract

Crime is an unlawful behavior that will be given a punishment or sanctions based on Kitab Undang-Undang Hukum Pidana (KUHP) or other regulations in Indonesia. One of the provinces in Indonesia, namely West Kalimantan reported that criminal cases are increasing in 2021 and 2022. One of the solutions to minimize that case is grouping the district and city in West Kalimantan based on the level of vulnerability so the authority can be more responsive in solving these problems. The grouping can be done by cluster analysis. This analysis aims to group some objects based on the similarity of characteristics. K-Means++ is one of the methods of cluster analysis. K-Means++ is the development of K-Means, in which K-Means++ is smarter than K-Means in selecting the initial centroid because only one initial centroid is chosen randomly, and the initial centroids of the other clusters are done through calculations. This research uses secondary data from BPS of West Kalimantan, consisting of 10 variables. This research aims to form clusters to determine the level of vulnerability of each district and city in West Kalimantan. The selection of the optimal cluster is done by evaluating the cluster. One of these evaluations is the Dunn Index. Based on the analysis results, the optimum number of clusters is with a Dunn Index value of 0.55. The first cluster is categorized as non-vulnerable with ten members, the second cluster as vulnerable with three members, and the third cluster as very vulnerable with one member.