Application of K-Means Clustering in Mapping of Central Java Crime Area - Retno Tri Vulandari; Wawan Laksito Yuly Saptomo; Danar Wijaya Aditama (Indonesian Journal of Applied Statistics, 2020)
Crimes occur in many places and cause complex problems that have widespread impacts on all levels of society. Crime is related to several factors including crime index, the ratio of the number of police to the population, population density and poverty rates. In this study trying to develop an information system that is able to display and map crime-prone areas in Central Java. Based on these factors, it is used to classify regions in Central Java, namely the category of safe, quite vulnerable, vulnerable and very vulnerable. K-Means clustering method, is very suitable to be used in predicting and grouping which areas are included in the 4 categories. The formulation of the problem is to find out areas prone to crime in Central Java. Based on the results, there are 11 regions with safe categories, 4 areas with quite vulnerable categories, 13 regions with vulnerable categories and 6 regions with very vulnerable categories.Keywords : K-Means clustering, mapping, Central Java,  criminality, crime area.