Implementasi Metode Clusterisasi K-Means Pada Pemetaan Daerah Rawan Kriminal Kota Dili Berbasis WebGIS
DOI:
https://doi.org/10.29080/systemic.v7i1.1274Keywords:
Crime, Data Mining, Clustering, K-Measn, GIS, WebGISAbstract
This study aims to map sub-districts that are prone to criminal acts with data mining to group criminal data using k-means clustering, as well as visualize into a webgis-based criminal information system by displaying a map of the distribution of criminal data and the percentage of each sub-district. -district of the city of Dili. After carrying out a series of processes, it was found that the sub-district of dom aleixo which shows a very vulnerable status area with the type of crime of assault has a high percentage of total victims with a value of 49.6%, theft 19.6%, harassment 12%, prostitution 9.6%, murder 6.8% and finally drugs with a percentage of 2.4%.
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