Geographic Information Systems for Crime Prone Areas Clustering

Authors

  • Heti Mulyani Politeknik Enjinering Indorama, Indonesia
  • Jajang Nurjaman Sekolah Tinggi Teknologi Indonesia Tanjungpinang, Indonesia
  • Muhammad Nugraha Politeknik Enjinering Indorama, Indonesia

DOI:

https://doi.org/10.15575/join.v5i2.599

Keywords:

Crime, Geographic Information Systems, Clustering, K-Means

Abstract

Crime is one of the problems that is quite complicated and very disturbing to the community. Crimes can occur at different times and places, making it difficult to track which areas are prone to such actions. K-means algorithm is used to cluster prone areas and Geographic Information System is used to map crime-prone areas. Web-based application is developed with the PHP programming language. The data used is quantitative data in the form of the number of crimes committed and the coordinates of the cases. The attributes of the crime used consist of five parameters: theft, mistreatment, rape, women and child protection cases and fraud. The results of this study are clustering areas into 3 cluster and mapping prone areas that is safe area, safe enough area and prone area. From the overall crime data for 2019 in Purwakarta district, it was found that 68.75% was safe, 18.75% was quite safe and 12.5% was prone area.

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Published

2020-12-08

Issue

Vol. 5 No 2 (2020)

Section

Article

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