Implementation of Data Mining using the Clustering Method (Case: Region of the Actors of Theft Crime by Province)

Frinto Tambunan(1*),

(1) Universitas Potensi Utama
(*) Corresponding Author

Abstract


Theft is a behavior that causes harm to victims who are targeted and cause casualties. This study aims to classify areas of theft crimes based on provision by using data mining techniques. Data was obtained from the Indonesian statistical center (Badan Pusat Statistik) consisting of 34 provinces. The grouping technique used is K-Means. Clusters are divided into 3 namely: C1: areas with high crime rates of theft, C2: areas with crime rates of ordinary theft and C3: areas with low theft crime rates. Data processing is done using the help of RapidMiner software. The results of the k-means analysis obtained 17 provinces in Indonesia have the highest theft crime rate (C1), namely: Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Lampung, DKI Jakarta, West Java, Central Java, East Java, Banten, West Nusa Tenggara, East Nusa Tenggara, South Kalimantan, South Sulawesi and Papua. The results of the study concluded that more than 50% of regions in Indonesia still had high rates of crime of theft.


Full Text:

PDF

References


J. C. Rubio-romero et al., “Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Promosi,” Ind. Mark. Manag., vol. 1, no. 1, pp. 1–9, 2014.

A. P. Windarto, “Penerapan Data Mining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering,” Techno.COM, vol. 16, no. 4, pp. 348–357, 2017.

M. ko. Dicky Nofriansyah, S.Kom., Konsep Data Mining Vs Sistem Pendukung Keputusan.pdf, Ed.1, Cet. Yogyakarta: Deepublish, 2014.

E. Elisa, “Analisa dan Penerapan Algoritma C4.5 Dalam Data Mining Untuk Mengidentifikasi Faktor-Faktor Penyebab Kecelakaan Kerja Kontruksi PT.Arupadhatu Adisesanti,” J. Online Inform., vol. 2, no. 1, p. 36, 2017.

K. Singh, D. Malik, and N. Sharma, “Evolving limitations in K-means algorithm in data mining and their removal,” IJCEM Int. J. Comput. Eng. Manag. ISSN, vol. 12, no. April, pp. 2230–7893, 2011.

A. P. Windarto, “Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method,” Int. J. Artif. Intell. Res., vol. 1, no. 2, pp. 26–33, 2017.

S. Sudirman, A. P. Windarto, and A. Wanto, “Data Mining Tools | RapidMiner : K-Means Method on Clustering of Rice Crops by Province as Efforts to Stabilize Food Crops In Indonesia,” IOP Conf. Ser. Mater. Sci. Eng., vol. 420, no. 12089, pp. 1–8, 2018.

N. Kaur, J. K. Sahiwal, N. Kaur, and P.- Punjab, “Efficient K-Means Clustering Algorithm Using Ranking Method,” Int. J. Adv. Res. Comput. Eng. Technol., vol. 1, no. 3, pp. 85–91, 2012.

K. Fatmawati and A. P. Windarto, “Data Mining: Penerapan Rapidminer Dengan K-Means Cluster Pada Daerah Terjangkit Demam Berdarah Dengue (Dbd) Berdasarkan Provinsi,” Comput. Eng. Sci. Syst. J., vol. 3, no. 2, p. 173, 2018.

B. Supriyadi, A. P. Windarto, T. Soemartono, and Mungad, “Classification of natural disaster prone areas in Indonesia using K-means,” Int. J. Grid Distrib. Comput., vol. 11, no. 8, pp. 87–98, 2018.




DOI: https://doi.org/10.30645/ijistech.v2i2.25

Refbacks

  • There are currently no refbacks.







Jumlah Kunjungan:

View My Stats

Published Papers Indexed/Abstracted By: