Main Article Content
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.
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References
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References
A. Chazawi, Pelajaran Hukum Pidana I, Jakarta: PT Raja Girafindo, 2005.
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.
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.
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.
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.
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.
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].
E. Prasetyo, Data Mining : Konsep dan Aplikasi Menggunakan METLAB, Yogyakarta: Andi, 2012.
A. widarjono, Analisis Statiistika Multivariat Terapan, Yogyakarta: UPP STIM YKPN, 2010.
Suparto, "Analisis Korelasi Variabel-Variabel yang Mempengaruhi Siswa dalam Memilih Perguruan Tinggi," Jurnal IPTEK, vol. 18, 2016.
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.
M. P. Frushicheva, 11 August 2016. [Online]. Available: https://rstudio-pubs-static.s3.amazonaws.com/201598_e96ae3be88b64ba8baffb2923bfdf5c6.html.
S. Nugroho, Statistika Multivariat Terapan, Pertama ed., UNIB Press, 2008.