Implementation of Data Mining using the Clustering Method (Case: Region of the Actors of Theft Crime by Province)

Theft is a behavior that causes harm to victims who are targeted and cause casualties. This study aims to classify areas of theft crimes based on provision by using data mining techniques. Data was obtained from the Indonesian statistical center (Badan Pusat Statistik) consisting of 34 provinces. The grouping technique used is K-Means. Clusters are divided into 3 namely: C1: areas with high crime rates of theft, C2: areas with crime rates of ordinary theft and C3: areas with low theft crime rates. Data processing is done using the help of RapidMiner software. The results of the k-means analysis obtained 17 provinces in Indonesia have the highest theft crime rate (C1), namely: Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Lampung, DKI Jakarta, West Java, Central Java, East Java, Banten, West Nusa Tenggara, East Nusa Tenggara, South Kalimantan, South Sulawesi and Papua. The results of the study concluded that more than 50% of regions in Indonesia still had high rates of crime of theft.