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.

Método de clustering e inteligencia artificial para clasificar y proyectar delitos violentos en Colombia

“…En cuanto al método de clustering, la mayoría de investigaciones aplican una cantidad de dos clústeres (Azis et al, 2021). Si bien esto permite focalizar el análisis en contextos con mayores y menores delitos, deja de lado una jerarquización más desagregada de regiones que podrían clasificarse y desglosarse según las cualidades propias de los delitos, de manera que permita a las autoridades comprender a profundidad el comportamiento de cada uno de los grupos e incluso comprender el posible desarrollo de capacidades de violencia diferentes.…”

Pemetaan Tingkat Kriminalitas di Indonesia: Analisis Spasial dengan Pendekatan SIG pada Tingkat Provinsi

“…Setiap catatan menggambarkan informasi diskrit tentang aktivitas kriminal kuantitatif yang dilaporkan kepada otoritas penegak hukum regional dari tahun 2017 hingga 2019. Studi ini menunjukkan bahwa enam provinsi-Sumatera Utara, Sumatera Selatan, Metro Jaya, Jawa Barat, Jawa Timur, dan Sulawesi Selatan-menunjukkan lokasi geografis dengan tingkat insiden kejahatan yang paling mencolok [16]. RapidMiner digunakan untuk menerapkan proses pengelompokan.…”