Prediction of Criminal Theft Locations at the Binjai Police Station using Historical Data and the KNN Algorithm

Rizky Pambudi Rizky Pambudi

doi:10.59934/jaiea.v5i1.1658

Authors

Rizky Pambudi Rizky Pambudi STMIK Kaputama

DOI:

https://doi.org/10.59934/jaiea.v5i1.1658

Keywords:

Historical Data, Prediction, Theft, K-Nearest Neighbor, RapidMiner

Abstract

This research aims to predict the locations of theft crimes in the Binjai Police Department by utilizing historical data obtained from Binjai Police. The implementation of the K-Nearest Neighbor (KNN) algorithm was carried out to analyze crime patterns based on variables such as district location, time of occurrence, day of occurrence, and type of crime. The historical data were processed through normalization, splitting into training and testing datasets, and model evaluation using RapidMiner software. The results show that the KNN algorithm is able to classify with a fairly good level of accuracy, making it a useful basis for providing predictive information on theft-prone locations. These findings are expected to assist the police and the Binjai city government in formulating crime prevention strategies and increasing public awareness.

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References

S. P. Dewi, N. Nurwati, and E. Rahayu, “Penerapan Data Mining Untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode K-Nearest Neighbor,” Build. Informatics, Technol. Sci., vol. 3, no. 4, pp. 639–648, 2022, doi: 10.47065/bits.v3i4.1408.

F. Hermawan and J. Prianggono, “Crime of theft prediction using Machine Learning K-Nearest Neighbour Algorithm at Polresta Bandar Lampung,” Sinkron, vol. 8, no. 3, pp. 1515–1527, 2023, doi: 10.33395/sinkron.v8i3.12422.

S. S. M. K. Muhammad Arhami and S. T. M. T. Muhammad Nasir, Data Mining - Algoritma dan Implementasi. Andi Offset, 2020. [Online]. Available: https://books.google.co.id/books?id=AtcCEAAAQBAJ

Y. Ardilla et al., DATA MINING DAN APLIKASINYA. Penerbit Widina, 2021. [Online]. Available: https://books.google.co.id/books?id=53FXEAAAQBAJ

S. Syam et al., Data Mining : Teori dan Penerapannya dalam Berbagai Bidang. PT. Sonpedia Publishing Indonesia, 2024. [Online]. Available: https://books.google.co.id/books?id=hTAxEQAAQBAJ

B. A. S. Fairuz, Panduan Praktis Machine Learning Klasifikasi Menggunakan Python: Diandra Kreatif. Diandra Kreatif, 2024. [Online]. Available: https://books.google.co.id/books?id=Wr5AEQAAQBAJ

L. Suryadi, N. E. Pratiwi, F. Ardhy, and P. Riswanto, “Penerapan Data Mining Prediksi Penjualan Mebel Terlaris Menggunakan Metode K-Nearest Neighbor (K-Nn)(Studi Kasus: Toko Zerita Meubel),” JUSIM (Jurnal Sist. Inf. Musirawas), vol. 7, no. 2, pp. 174–184, 2022.

H. Hamdiyah, “Analisis Unsur-Unsur Tindak Pidana Pencurian: Tinjauan Hukum,” J. Tahqiqa J. Ilm. Pemikir. Huk. Islam, vol. 18, no. 1, pp. 98–108, 2024, doi: 10.61393/tahqiqa.v18i1.216.

A. D. Cahya and T. Anggraini, “Analisis Problematika Bank Syariah Indonesia Setelah Merger Studi Kasus Bank Syariah Indonesia (BSI),” Syntax Lit. J. Ilm. Indonesia., vol. 7, no. 12, pp. 17230–17241, 2022.