Pengelompokkan Tindakan Kriminalitas di Indonesia dengan K-Medoids Menggunakan Algoritma Artificial Bee Colony

Authors

  • Adinda Pratiwi Musa Universitas Negeri Gorontalo
  • Novianita Achmad Universitas Negeri Gorontalo
  • Salmun K. Nasib Universitas Negeri Gorontalo

Keywords:

Artificial Bee Colony, Clustering, Criminal Activity, K-Medoids, Regional Police Department, Silhouette Index

Abstract

This research aims to classify regional police forces in Indonesia based on the level and characteristics of criminal activity by applying the K-Medoids method optimized using the Artificial Bee Colony (ABC) algorithm. This grouping is intended to identify the dominant crime patterns in each region and evaluate the effectiveness of the methods used in producing representative clusters. The analysis results show that the K-Medoids-ABC method produces three main clusters, with the distribution of each consisting of 7 regional police departments in cluster 1, 5 regional police departments in cluster 2, and 21 regional police departments in cluster 3. Cluster validation using the Silhouette Index (SI) yielded a value of 0.387, indicating that the clustering results fall into the weak structure category, meaning the cluster structure is formed but with weak separation (Weak Separation). Cluster 1 shows a moderate and relatively even crime rate, Cluster 2 is dominated by crimes against life and crimes of fraud, embezzlement, and corruption, while Cluster 3 shows low values across all variables, with the lowest values for violent property crimes and drug-related crimes. This cluster reflects regions with relatively safe conditions, as evidenced by very low crime rates. These differences in characteristics between clusters reflect the diversity of factors causing crime in each region and have important implications for formulating more contextual and targeted crime prevention strategies.

Downloads

Download data is not yet available.

References

1] R. E. Caraka et al., “Micro, small, and medium enterprises’ business vulnerability cluster in Indonesia: An analysis using optimized fuzzy geodemographic clustering,” Sustainability, vol. 13, no. 14, p. 7807, 2021.

[2] B. Probierz, J. Kozak, and A. Hrabia, “Clustering of scientific articles using natural language processing,” Procedia Comput. Sci., vol. 207, pp. 3449–3458, 2022.

[3] A. T. R. Dani, S. Wahyuningsih, and N. A. Rizki, “Pengelompokkan Data Runtun Waktu menggunakan Analisis Cluster: Studi Kasus: Nilai Ekspor Komoditi Migas dan Nonmigas Provinsi Kalimantan Timur Periode Januari 2000-Desember 2016,” Eksponensial, vol. 11, no. 1, pp. 29–38, 2020.

[4] A. T. R. Dani, S. Wahyuningsih, and N. A. Rizki, “Penerapan Hierarchical Clustering Metode Agglomerative pada Data Runtun Waktu,” Jambura J. Math., vol. 1, no. 2, pp. 64–78, 2019.

[5] I. Ayuningtias and N. Debataraja, Naomi Nessyana Imro’ah, “Analisis cluster non-hirarki dengan metode k-modes,” Bimaster Bul. Ilm. Mat. Stat. dan Ter., vol. 8, no. 4, 2019.

[6] M. A. Nahdliyah, T. Widiharih, and A. Prahutama, “Metode K-Medoids Clustering dengan Validasi Silhouette Index dan C-Index (Studi Kasus Jumlah Kriminalitas Kabupaten/Kota di Jawa Tengah Tahun 2018),” J. Gaussian, vol. 8, no. 2, pp. 161–170, 2019.

[7] P. K. Panigrahi, S. Ghosh, and D. R. Parhi, “Comparison of GSA, SA and PSO based intelligent controllers for path planning of mobile robot in unknown environment,” Int. J. Electr. Comput. Eng., vol. 8, no. 10, pp. 1633–1642, 2014.

[8] C. A. Asrat, Makkulau, and I. Yahya, “Perbandingan Metode Principal Componen Analysis (PCA) dan Partial Least Square (PLS) dalam Penanganan Multikolinearitas pada Kasus Kemiskinan di Provinsi Sulawesi Tenggara Tahun 2023,” Arus J. Sains dan Teknol., vol. 3, no. 1, pp. 68–82, 2025.

[9] A. Islamiyati, S. Sahriman, and S. Oktoni, “Studi Longitudinal Pada Analisis Data Gula Darah Pasien Diabetes melalui Principal Component Analysis,” Jambura J. Math., vol. 4, no. 1, pp. 41–49, 2022.

[10] A. N. Nitasari, F. N. Salsabila, D. T. Ramadhanty, M. R. Anggriawan, D. Amelia, and E. Mardianto, M Fariz Fadillah Ana, “Reduksi Faktor-Faktor yang Mempengaruhi Kualitas Air Hujan dengan Metode Analisis Komponen Utama,” Zeta-Math J., vol. 8, no. 1, pp. 7–15, 2023.

[11] M. N. Van Delsen and S. Wattimena, Abraham Saputri, “Penggunaan metode analisis komponen utama untuk mereduksi faktor-faktor inflasi di Kota Ambon,” Barekeng J. Ilmu Mat. dan Terap., vol. 11, no. 2, pp. 109–118, 2017.

[12] B. T. El Haqq, A. Antika, and S. P. Wulandari, “Analisis Faktor-faktor Volume Ekspor Hasil Perikanan Menurut Provinsi di Indonesia Tahun 2021 menggunakan Analisis Faktoe,” Zool. J. Ilmu Peternakan, Ilmu Perikanan, Ilmu Kedokt. Hewan, vol. 3, no. 1, pp. 01–18, 2025.

[13] V. B. Samosir, A. M. Widodo, N. Anwar, B. A. Sekti, and N. Erzed, “Identifikasi outlier menggunakan teknik data mining clustering untuk analisis data tracer study pada fakultas ilmu komputer universitas esa unggul,” IKRA-ITH Inform. J. Komput. dan Inform., vol. 8, no. 1, pp. 162–174, 2024.

[14] D. Ayu, I. C. Dewi, and K. Pramita, “Analisis Perbandingan Metode Elbow dan Sillhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali,” J. Matrix, vol. 9, no. 3, p. 102, 2019.

[15] M. Furqan, Y. R. Nasution, and K. Khairunnisa, “Application of Artificial Bee Colony Algorithm to Optimize the Shortest Route to Distrubute Clean Water Pipes,” JOMLAI J. Mach. Learn. Artif. Intelegence, vol. 1, no. 2, pp. 125–132, 2022.

[16] I. M. Karo Karo and H. Hendriyana, “Klasifikasi Penderita Diabetes menggunakan Algoritma Machine Learning dan Z-Score,” J. Teknol. Terpadu, vol. 8, no. 2, pp. 94–99, Dec. 2022, doi: 10.54914/jtt.v8i2.564.

Downloads

Published

2026-02-09

How to Cite

Musa, A. P., Achmad, N., & Nasib, S. K. (2026). Pengelompokkan Tindakan Kriminalitas di Indonesia dengan K-Medoids Menggunakan Algoritma Artificial Bee Colony. System Information and Computer Technology (SYNCTECH), 2(1), 15–27. Retrieved from https://librarium.id/index.php/synctech/article/view/41

Issue

Vol. 2 No. 1 (2026): February

Section

Articles

License

Copyright (c) 2026 Adinda Pratiwi Musa, Novianita Achmad, Salmun K. Nasib

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
  3. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.