Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations

Yuni Rahmatika; Eko Sediyono; Catur Edi Widodo

doi:10.22219/kinetik.v5i3.1067

Issue

Vol. 5, No. 3, August 2020

Issue Published : Aug 31, 2020

Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations

https://doi.org/10.22219/kinetik.v5i3.1067

Yuni Rahmatika

Universitas Diponegoro

Eko Sediyono

Universitas Kristen Satya Wacana

Catur Edi Widodo

Universitas Diponegoro

Corresponding Author(s) : Yuni Rahmatika

yuni.rahmatika25@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 5, No. 3, August 2020
Article Published : Jul 31, 2020

Abstract

Clustering algorithms can be used to build geographic mapping systems to determine crime-prone locations. This study aims to establish a geographical mapping system to determine crime-prone locations that can help police control certain locations that often occur crime and provide information to people in crime-prone locations. Criminal groups are calculated based on crime data from November 2018 to October 2019 which occurred in 9 districts in Kudus Regency. The crime grouping process uses the k-means method used to classify based on regional vulnerability and uses a weighted product method that functions as a vulnerability ranking that is vulnerable to crime selection. The grouping results obtained from this study are that there are 1 very vulnerable area, 5 areas in the vulnerable category, and 3 safe areas. While the weighted product method produces Melatilor area as a vulnerable area to be defeated by a score of 0.182093. This research provides benefits for the public to see crime-prone areas so that they can be more vigilant, while for the police to analyze crime so as to speed up the process of resolving crime and increase and improve crime prevention measures.

Keywords

Decision Support System Data Mining K-Means Clustering Weighted Product Method Crime

Rahmatika, Y., Sediyono, E., & Widodo, C. E. (2020). Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(3), 195-202. https://doi.org/10.22219/kinetik.v5i3.1067

Download Citation

References

Bangoria, B., Mankad, N., & Pambhar, V. (2013). A survey on Efficient Enhanced K-Means Clustering Algorithm. IJSRD -International Journal for Scientific Research & Development|, 1(9online), 2321–2613.
Ben Salem, S., Naouali, S., & Chtourou, Z. (2018). A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2018.04.023
Curtis-Ham, S., & Walton, D. (2017). Mapping crime harm and priority locations in New Zealand: A comparison of spatial analysis methods. Applied Geography. https://doi.org/10.1016/j.apgeog.2017.06.008
Dhalmahapatra, K., Shingade, R., Mahajan, H., Verma, A., & Maiti, J. (2019). Decision support system for safety improvement: An approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering. Computers and Industrial Engineering. https://doi.org/10.1016/j.cie.2018.12.044
Fenimore, D. M. (2019). Mapping harmspots: An exploration of the spatial distribution of crime harm. Applied Geography. https://doi.org/10.1016/j.apgeog.2019.06.002
Ismanto, H., & Azhari, A. (2015). Multi Attribute Decision Making Method for Determination of Regional Development Plan Priority in Papua Province based on the Indicator of Gross Regional Domestic Product (GRDP). International Journal of Computer Applications. https://doi.org/10.5120/20567-2957
Jain, V., Sharma, Y., Bhatia, A., & Arora, V. (2017). Crime Prediction using K-means Algorithm. GRD Journals - Global Research and Development Journal for Engineering.
Joshi, A., Sabitha, A. S., & Choudhury, T. (2018). Crime Analysis Using K-Means Clustering. Proceedings - 2017 International Conference on Computational Intelligence and Networks, CINE 2017, 7(04), 33–39. https://doi.org/10.1109/CINE.2017.23
Khairina, D. M., Asrian, M. R., & Hatta, H. R. (2017). Decision support system for new employee recruitment using weighted product method. Proceedings - 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering, ICITACEE 2016. https://doi.org/10.1109/ICITACEE.2016.7892459
Kouser, K. (2013). A comparative study of K Means Algorithm by Different Distance Measures. International Journal of Innovative Research in Computer and Communication Engineering.
Kusumadewi, S. H. (2006). Fuzzy Multi-Attribute Decision Making (Fuzzy MADM). Graha Ilmu Yogyakarta.
Lan, X., Li, Q., & Zheng, Y. (2015). Density K-means: A new algorithm for centers initialization for K-means. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS. https://doi.org/10.1109/ICSESS.2015.7339213
Piroozfar, P., Farr, E. R. P., Aboagye-Nimo, E., & Osei-Berchie, J. (2019). Crime prevention in urban spaces through environmental design: A critical UK perspective. Cities. https://doi.org/10.1016/j.cities.2019.102411
Prasetyo, E. (2014). DATA MINING Mengolah Data Menjadi Informasi Menggunakan Matlab. In penerbit andi. https://doi.org/10.1017/CBO9781107415324.004
Raval, U. R., & Jani, C. (2016). Implementing & Improvisation of K-means Clustering Algorithm. International Journal of Computer Science and Mobile Computing.
Sadeek, S. N., Minhuz Uddin Ahmed, A. J. M., Hossain, M., & Hanaoka, S. (2019). Effect of land use on crime considering exposure and accessibility. Habitat International. https://doi.org/10.1016/j.habitatint.2019.102003
Savitha, K., & Chandrasekar, D. C. (2011). Vertical Handover decision schemes using SAW and WPM for Network selection in Heterogeneous Wireless Networks. Global Journal of Computer Science and Technology. https://doi.org/10.5120/3125-4300
Taufik, I., Saleh, A., Slamet, C., Maylawati, D. S., Ramdhani, M. A., & Muhammad, B. A. (2019). Decision support system design for determining brown sugar quality with weighted product method. Journal of Physics: Conference Series, 1280(2). https://doi.org/10.1088/1742-6596/1280/2/022019
Wu, X., Wu, B., Sun, J., Qiu, S., & Li, X. (2015). A hybrid fuzzy K-harmonic means clustering algorithm. Applied Mathematical Modelling. https://doi.org/10.1016/j.apm.2014.11.041
Zhu, Q., Zhang, F., Liu, S., & Li, Y. (2019). An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago. Information and Management. https://doi.org/10.1016/j.im.2019.103247

References

Bangoria, B., Mankad, N., & Pambhar, V. (2013). A survey on Efficient Enhanced K-Means Clustering Algorithm. IJSRD -International Journal for Scientific Research & Development|, 1(9online), 2321–2613.

Ben Salem, S., Naouali, S., & Chtourou, Z. (2018). A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2018.04.023

Curtis-Ham, S., & Walton, D. (2017). Mapping crime harm and priority locations in New Zealand: A comparison of spatial analysis methods. Applied Geography. https://doi.org/10.1016/j.apgeog.2017.06.008

Dhalmahapatra, K., Shingade, R., Mahajan, H., Verma, A., & Maiti, J. (2019). Decision support system for safety improvement: An approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering. Computers and Industrial Engineering. https://doi.org/10.1016/j.cie.2018.12.044

Fenimore, D. M. (2019). Mapping harmspots: An exploration of the spatial distribution of crime harm. Applied Geography. https://doi.org/10.1016/j.apgeog.2019.06.002

Ismanto, H., & Azhari, A. (2015). Multi Attribute Decision Making Method for Determination of Regional Development Plan Priority in Papua Province based on the Indicator of Gross Regional Domestic Product (GRDP). International Journal of Computer Applications. https://doi.org/10.5120/20567-2957

Jain, V., Sharma, Y., Bhatia, A., & Arora, V. (2017). Crime Prediction using K-means Algorithm. GRD Journals - Global Research and Development Journal for Engineering.

Joshi, A., Sabitha, A. S., & Choudhury, T. (2018). Crime Analysis Using K-Means Clustering. Proceedings - 2017 International Conference on Computational Intelligence and Networks, CINE 2017, 7(04), 33–39. https://doi.org/10.1109/CINE.2017.23

Khairina, D. M., Asrian, M. R., & Hatta, H. R. (2017). Decision support system for new employee recruitment using weighted product method. Proceedings - 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering, ICITACEE 2016. https://doi.org/10.1109/ICITACEE.2016.7892459

Kouser, K. (2013). A comparative study of K Means Algorithm by Different Distance Measures. International Journal of Innovative Research in Computer and Communication Engineering.

Kusumadewi, S. H. (2006). Fuzzy Multi-Attribute Decision Making (Fuzzy MADM). Graha Ilmu Yogyakarta.

Lan, X., Li, Q., & Zheng, Y. (2015). Density K-means: A new algorithm for centers initialization for K-means. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS. https://doi.org/10.1109/ICSESS.2015.7339213

Piroozfar, P., Farr, E. R. P., Aboagye-Nimo, E., & Osei-Berchie, J. (2019). Crime prevention in urban spaces through environmental design: A critical UK perspective. Cities. https://doi.org/10.1016/j.cities.2019.102411

Prasetyo, E. (2014). DATA MINING Mengolah Data Menjadi Informasi Menggunakan Matlab. In penerbit andi. https://doi.org/10.1017/CBO9781107415324.004

Raval, U. R., & Jani, C. (2016). Implementing & Improvisation of K-means Clustering Algorithm. International Journal of Computer Science and Mobile Computing.

Sadeek, S. N., Minhuz Uddin Ahmed, A. J. M., Hossain, M., & Hanaoka, S. (2019). Effect of land use on crime considering exposure and accessibility. Habitat International. https://doi.org/10.1016/j.habitatint.2019.102003

Savitha, K., & Chandrasekar, D. C. (2011). Vertical Handover decision schemes using SAW and WPM for Network selection in Heterogeneous Wireless Networks. Global Journal of Computer Science and Technology. https://doi.org/10.5120/3125-4300

Taufik, I., Saleh, A., Slamet, C., Maylawati, D. S., Ramdhani, M. A., & Muhammad, B. A. (2019). Decision support system design for determining brown sugar quality with weighted product method. Journal of Physics: Conference Series, 1280(2). https://doi.org/10.1088/1742-6596/1280/2/022019

Wu, X., Wu, B., Sun, J., Qiu, S., & Li, X. (2015). A hybrid fuzzy K-harmonic means clustering algorithm. Applied Mathematical Modelling. https://doi.org/10.1016/j.apm.2014.11.041

Zhu, Q., Zhang, F., Liu, S., & Li, Y. (2019). An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago. Information and Management. https://doi.org/10.1016/j.im.2019.103247

Issue

Vol. 5, No. 3, August 2020

Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations

Corresponding Author(s) : Yuni Rahmatika

Abstract

Keywords

Download Citation

References

Downloads