Mapping study using the unsupervised learning clustering approach

Abstract

The aim of this research is to analyze the number of criminal cases in Indonesia by utilizing unsupervised learning techniques. The unsupervised learning technique used is data mining by mapping clusters of regions in Indonesia. Sources of data were obtained from the Operations Control Bureau, National Police Headquarters of the Republic of Indonesia through processed data from the Central Statistics Agency (abbreviated as BPS) with data url: https://www.bps.go.id. The data mining method used to map the form of calcter is k-medoid. The data used is data on the number of crimes according to the regional police (2017-2019) which consists of 34 records. The attribute used is the number of crimes in the past three years based on the regional police for each province. The mapping label used is the high cluster (D1) and the low cluster (D2) on the number of criminal acts in Indonesia. The mapping analysis process uses the help of Rapid Miner software. In determining the amount of calcter (k = 2) is done using the Davies Bouldin Index (DBI) parameter with a value of 0.876 (the smaller the better). The results showed that six provinces were in the high cluster (D1) and twenty-eight provinces were in the low cluster (D2). The final centroid in each cluster is 16,008; 21,498; 21,616 (cluster_0 / D1) and 6,994; 7,311; 6,785 (cluster_1 / D2). The six provinces in the high cluster of criminal cases are North Sumatra, South Sumatra, Metro Jaya, West Java, East Java and South Sulawesi. The results of the research are expected to provide information for the government to reduce the number of criminal acts in Indonesia based on the number of clusters that exist.