Geographic Information Systems for Crime Prone Areas Clustering

Heti Mulyani; Jajang Nurjaman; Muhammad Nugraha

doi:10.15575/join.v5i2.599

Geographic Information Systems for Crime Prone Areas Clustering

Heti Mulyani, Jajang Nurjaman, Muhammad Nugraha

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.

Keywords

Crime; Geographic Information Systems; Clustering; K-Means

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References

BPS, “Kabupaten Purwakarta dalam Angka 2019,” Badan Pus. Stat. Kabupaten Purwakarta, 2019.

F. D. Yuliansyah, “Sistem Informasi Geografis Untuk Klasifikasi Daerah Rawan Kriminalitas Menggunakan Metode K-Means,” Univ. Islam Indones., p. Yogyakarta, 2016.

V. Jain, Y. Sharma, A. Bhatia, and V. Arora, “Crime Prediction using K-means Algorithm,” GRD Journals - Glob. Res. Dev. J. Eng., vol. 2, no. 5, pp. 206–209, 2017, [Online]. Available: www.grdjournals.com.

Chetan G. Wadhai, Tiksha P. Kakade, Dnyaneshwari S. Tumsare, Khushabu A. Bokde, and Prof. Deepa Bhattacharya, “Crime Detection Technique Using Data Mining and K-Means,” Int. J. Eng. Res., vol. V7, no. 02, pp. 223–226, 2018, doi: 10.17577/ijertv7is020110.

S. M. Dewi, A. P. Windarto, I. S. Damanik, and H. Satria, “Analisa Metode K-Means pada Pengelompokan Kriminalitas Menurut Wilayah,” Semin. Nas. Sains Teknol. Inf., pp. 620–625, 2019.

L. Suriani, “Pengelompokan Data Kriminal Pada Poldasu Menentukan Pola Daerah Rawan Tindak Kriminal Menggunakan Data Mining Algoritma K-Means Clustering,” J. Sist. Komput. dan Inform., vol. 1, no. 2, p. 151, 2020, doi: 10.30865/json.v1i2.1955.

D. Gultom et al., “Penerapan Algoritma K-Means Untuk Mengetahui Tingkat Tindak Kejahatan Daerah Pematangsiantar,” J. Teknol. Inf., vol. 4, no. 1, pp. 146–151, 2020.

H. Rohul, M. Tambun, and I. Pendahuluan, “Rancang Bangun Aplikasi Data Mining Untuk,” vol. 1, no. 1, pp. 31–38, 2015.

M. S. Davis, “Crime Rate,” Concise Dict. Crime Justice, pp. 252–258, 2012, doi: 10.4135/9781452229300.n522.

D. Kartini, “Rancang Bangun Aplikasi K-Means untuk Klasifikasi Kelulusan Siswa Sekolah Kepolisian Negara Daerah Kalimantan Selatan,” ProTekInfo, vol. 3, no. 1, pp. 14–21, 2016.

Suendri, “Implementasi Diagram UML (Unified Modelling Language) Pada Perancangan Sistem Informasi Remunerasi Dosen Dengan Database Oracle (Studi Kasus: UIN Sumatera Utara Medan),” J. Ilmu Komput. dan Inform., vol. 3, no. 1, pp. 1–9, 2018, [Online]. Available: http://jurnal.uinsu.ac.id/index.php/algoritma/article/download/3148/1871.

H. Mulyani, T. Djatna, and I. S. Sitanggang, “Agent based modeling on dynamic spreading dengue fever epidemic,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 15, no. 3, pp. 1380–1388, 2017, doi: 10.12928/TELKOMNIKA.v15i3.4511.