Implementation K-Means Algorithm to Group Provinces By Factors Influenced Criminal Act in Indonesia in 2019

Abstract

A criminal act is an act that is prohibited by a criminal law accompanied by a sanction in the form of a particular crime for whoever violates the prohibition. Criminal action as a social phenomenon is more influenced by various aspects of life in society, including poverty and unemployment factors. Grouping the factors that influence a crime is necessary to find the most recent information that was not previously known. This research uses the K-Means method, a non-hierarchical cluster analysis that seeks to partition data with the same characteristics into one cluster. The results showed that 3 clusters formed, with cluster 1 covering 17 provinces are areas with the characteristics of the lowest percentage of poverty and the highest average unemployment, the cluster group 2 includes 12 provinces which are areas with the characteristics of the percentage of moderate poverty and the lowest average unemployment, the cluster group 3 includes five provinces which are areas with the characteristics of the highest percentage of poverty and moderate unemployment.