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Abstract

A criminal act is an act that is prohibited by a criminal law accompanied by a sanction in the form of a particular crime for whoever violates the prohibition. Criminal action as a social phenomenon is more influenced by various aspects of life in society, including poverty and unemployment factors. Grouping the factors that influence a crime is necessary to find the most recent information that was not previously known. This research uses the K-Means method, a non-hierarchical cluster analysis that seeks to partition data with the same characteristics into one cluster. The results showed that 3 clusters formed, with cluster 1 covering 17 provinces are areas with the characteristics of the lowest percentage of poverty and the highest average unemployment, the cluster group 2 includes 12 provinces which are areas with the characteristics of the percentage of moderate poverty and the lowest average unemployment, the cluster group 3 includes five provinces which are areas with the characteristics of the highest percentage of poverty and moderate unemployment.

Keywords

K-means Poverty Unemployment

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Author Biographies

Zumrotul Wahidah, Universitas Islam Indonesia, Indonesia

 

 

Dina Tri Utari, Universitas Islam Indonesia, Indonesia

 

 

How to Cite
Wahidah, Z., & Utari, D. T. (2022). Implementation K-Means Algorithm to Group Provinces By Factors Influenced Criminal Act in Indonesia in 2019. Enthusiastic : International Journal of Applied Statistics and Data Science, 2(1), 37–46. https://doi.org/10.20885/enthusiastic.vol2.iss1.art5
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References

  1. A. Chazawi, Pelajaran Hukum Pidana I, Jakarta: PT Raja Girafindo, 2005.
  2. R. Pasiza, S. Nugroho and F. Faisal, "Analisis Jalur Faktor-Faktor Penyebab Kriminalitas di Indonesia," 2008. [Online]. Available: http://sigitnugroho.id/e-Skripsi/2015/08/Analisis%20Jalur%20Faktor-faktor%20Penyebab%20Kriminalitas%20di%20Indonesia.pdf.
  3. K. C. A. Handayani, R. N. Isfahani and E. Widodo, "Faktor-Faktor yang Mempengaruhi Kriminalitas di Indonesia Tahun 2011-2016 dengan Regresi Data Panel," Indonesian Journal of Applied Statistics, vol. 2, 2019.
  4. U. T. Suryadi and Y. Supriatna, "Sistem Clustering Tindak Kejahatan Pencurian di Wilayah Jawa Barat Menggunakan Algoritma K-Means," Jurnal Teknologi Informasi dan Komunikasi, 2019.
  5. W. Astuti and D. A. Widodo, "Pemetaan Tindak Kejahatan Jalanan di Kota Semarang Menggunakan Algoritma K-Means Clustering," Jurnal Teknik Elektro, Universitas Negeri Semarang, vol. 8, 2016.
  6. J. J. Purnama, R. Nurfalah, S. Rahayu and H. B. Novitasari, "Analisa Algoritma K-Means Clustering Pemetaan Jumlah Tindak Pidana," Kumpulan Jurnal Ilmu Komputer (KLIK), pp. 128-142, 2019.
  7. Badan Pusat Statistik, 2019. [Online]. Available: https://www.bps.go.id/indicator/34/101/1/jumlah-tindak-pidana-menurut-kepolisian-daerah.html. [Accessed 24 April 2021].
  8. E. Prasetyo, Data Mining : Konsep dan Aplikasi Menggunakan METLAB, Yogyakarta: Andi, 2012.
  9. A. widarjono, Analisis Statiistika Multivariat Terapan, Yogyakarta: UPP STIM YKPN, 2010.
  10. Suparto, "Analisis Korelasi Variabel-Variabel yang Mempengaruhi Siswa dalam Memilih Perguruan Tinggi," Jurnal IPTEK, vol. 18, 2016.
  11. M. Syakur, B. Khotimah, S. Rochman and B. Satoto, "Integration K-means Clustering Method and Elbow Method for Identification of the Best Customer Profile Cluser," IOP Conf. Ser. Mater. Sci. Eng., vol. 336, 2018.
  12. M. P. Frushicheva, 11 August 2016. [Online]. Available: https://rstudio-pubs-static.s3.amazonaws.com/201598_e96ae3be88b64ba8baffb2923bfdf5c6.html.
  13. S. Nugroho, Statistika Multivariat Terapan, Pertama ed., UNIB Press, 2008.