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THE ANALYSIS OF SOCIO-ECONOMIC EFFECT ON CRIMINALITY IN INDONESIA USING FUZZY CLUSTERWISE REGRESSION MODEL

Dian Fatimah Azzarah  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
*Moch. Abdul Mukid scopus  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Di Asih I Maruddani scopus  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Masithoh Yessi Rochayani scopus  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
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Abstract
Crime in Indonesia has shown a fluctuating trend and has increased significantly in recent years, with striking variations in crime rates between provinces. This phenomenon raises questions about the role of socio-economic factors such as education, poverty, and unemployment in influencing crime rates. Although there have been many studies examining the relationship between these variables and crime, the approaches used often assume that the relationship between variables is homogeneous across regions. In fact, heterogeneity in characteristics between provinces can cause different relationships. Therefore, an analysis approach is needed that can accommodate this diversity. This study proposes the Fuzzy Clusterwise Regression method which not only improves model accuracy compared to classical linear regression (with an increase in the coefficient of determination from 65.72% to more than 90%), but is also able to identify different patterns of relationships between regional groups (clusters). The results from FCR showed that the effect of socio-economic factors on crime varies between clusters and the optimum number of clusters is 4. In cluster 1, cluster 2, and cluster 3 all the variables had a significant influence on the amount of crime. Meanwhile, in cluster 4, the population poverty variable has no significant effect on the crime rate.
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Keywords: Crime; Socio-economic Factors; Fuzzy Clusterwise Regression
Funding: Universitas Diponegoro

Article Metrics:

  1. Arsono, Y. D., & Atmanti, H. D. (2014). Pengaruh Variabel Pendidikan, Pengangguran, Rasio Gini, Usia, dan Jumlah Polisi Perkapita Terhadap Angka Kejahatan Properti di Provinsi Jawa Tengah Tahun 2010-2012. Undergraduate Thesis, Universitas Diponegoro
  2. Bertsekas, D. P. (1982). Constrained Optimization and Lagrange Multiplier Methods. New York: Academic Press. https://doi.org/10.1016/B978-0-12-093480-5.50007-6
  3. BPS. (2022). Statistik Kriminal 2022. Badan Pusat Statistik, 023, 30–80. https://doi.org/4401002
  4. BPS. (2023). Statistik Kriminal 2023. Badan Pusat Statistik
  5. Breetzke, G. D., & Pearson, A. L. (2015). Socially Disorganized Yet Safe: Understanding Resilience to Crime in Neighborhoods in New Zealand. Journal of Criminal Justice, 43(6), 444–452. https://doi.org/10.1016/j.jcrimjus.2015.09.001
  6. Cox, D. R., & Hinkley, D. V. (1974). Theoretical Statistics. New York: Chapman and Hall. https://doi.org/https://doi.org/10.1201/b14832
  7. Desarbo, W. S., & Cron, W. L. (1988). A Maximum Likelihood Methodology for Clusterwise Linear Regression. In Journal of Classification, 5, 249-282
  8. Goulas, E., & Zervoyianni, A. (2015). Economic Growth and Crime: Is There An Asymmetric Relationship? Economic Modelling, 49(September), 286–295. https://doi.org/10.1016/j.econmod.2015.04.014
  9. Grover, C. (2012). Crime and Inequality. UK: Routledge
  10. Jajuga, K. (1986). Linear Fuzzy Regression. Fuzzy Sets and Systems, 20(3), 343–353. https://doi.org/10.1016/S0165-0114(86)90045-X
  11. Kartono, K. (2009). Patologi Sosial Jilid 1 (2nd ed.). Rajawali Pers
  12. Klawoon, F., & Hoppner, F. (2003). What is Fuzzy About Fuzzy Clustering? Understanding and Improving the Concept of the Fuzzifier. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2810(May), 5. https://doi.org/10.1007/978-3-540-45231-7
  13. Lochner, L. (2020). Education and Crime. The Economics of Education: A Comprehensive Overview, 109–117. https://doi.org/10.1016/B978-0-12-815391-8.00009-4
  14. Lochner, L., & Moretti, E. (2004). The Effect of Education on Crime: Evidence from Prison Inmates, Arrests, and Self-Reports. The American Economic Review, 94(1), 155–189
  15. Melick, M. D. (2003). The Relationship Between Crime and Unemployment. The Park Place Economist, 11(1), 30–36
  16. Mohammed, H., & Mohamed, W. A. W. (2015). Reducing Recidivism Rates through Vocational Education and Training. Procedia - Social and Behavioral Sciences, 204(November 2014), 272–276. https://doi.org/10.1016/j.sbspro.2015.08.151
  17. O’Sullivan, A. (2019). Urban Economics (9th ed.). McGraw-Hill
  18. Pare, P. P., & Felson, R. (2014). Income Inequality, Poverty and Crime Across Nations. British Journal of Sociology, 65(3), 434–458. https://doi.org/10.1111/1468-4446.12083
  19. Setiadi, E. (2000). Reformasi Hukum Pidana, untuk Mengantisipasi Perkembangan Kejahatan di Bidang Ekonomi (Economic Crimes. Mimbar Jurnal Sosial Dan Pembangunan, 16(3), 205–214. https://doi.org/10.1080/10611991.2016.1251223
  20. Soekanto, S., Liklikuwata, H., & Kusumah, M. W. (1986). Kriminologi Suatu Pengantar. Ghalia Indonesia
  21. Spath, H. (1979). Clusterwise Linear Regression. Computing, 22, 367–373
  22. Wahyuni P., D. (2010). Mencermati Perilaku Kekerasan dan Paradigma Sosial. Unisia, 0(61 SE-Articles), 339–349. https://doi.org/10.20885/unisia.vol29.iss61.art9
  23. Wedel, M., & Kistemaker, C. (1989). Consumer Benefit Segmentation using Clusterwise Linear Regression. International Journal of Research in Marketing, 6(1), 45–59. https://doi.org/10.1016/0167-8116(89)90046-3
  24. Wedel, M., & Steenkamp, J. B. E. M. (1989). A Fuzzy Clusterwise Regression Approach to Benefit Segmentation. International Journal of Research in Marketing, 6(4), 241–258. https://doi.org/10.1016/0167-8116(89)90052-9
  25. Wu, K. L. (2012). Analysis of Parameter Selections for Fuzzy C-Means. Pattern Recognition, 45(1), 407–415. https://doi.org/10.1016/j.patcog.2011.07.012

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