PENENTUAN ALGORITMA TERBAIK DALAM ANALISIS KLASTER TINDAK PIDANA DI INDONESIA MENGGUNAKAN K-MEANS DAN FUZZY C-MEANS

Article Sidebar

PDF
Published: Jul 30, 2025
Keywords:
K-Means, Fuzzy C-Means, Machine Learning, Crime
Dimensions
Altmetrics
Statistics
Read Counter : 1
Download : 2
Crossmark/ Data Version

Main Article Content

Derren Fusta

Abstract

Crime is an issue that has a significant impact on the security and stability of society. Many factors influence the occurrence of criminal acts, such as social, economic and environmental conditions. To identify criminal acts that have occurred, machine learning technology can be used to identify patterns in criminal act data. The K-Means and Fuzzy C-Means algorithms are machine learning algorithms used in this research to identify patterns in criminal act data using a cluster analysis method based on the number of criminal acts in an area, so that later it can make it easier for the local government and police in the process of making policies or further action. The dataset used in this research comes from the Indonesian regional police from 2000 to 2022 which was found on the website of the Indonesian Central Bureau of Statistics. This research aims to determine the best algorithm for the performance results of the K-Means and Fuzzy C-Means algorithms in cluster analysis of criminal acts in Indonesia. Test results using the Silhouette Coefficient and Davies-Bouldin Index evaluation methods show that the K-Means algorithm and the Fuzzy C-Means algorithm obtained similar results, namely 0.7078 for the Silhouette Coefficient and 0.5457 for the Davies-Bouldin Index.

Article Details

Issue
Vol. 19 No. 2 (2024): Jurnal Komputer dan Informatika
Section
Articles
Creative Commons License

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

Menawarkan akses terbuka

References

[1] A. Hanan, A. F. N. Masruriyah, and T. A. Mudzakir, “Implementasi Model Prediksi Data Kriminalitas Menggunakan Algoritma Single Moving Average,” Scientific Student Journal for Information, Technology and Science, vol. 5, no. 1, pp. 150–158, 2024.

[2] A. D. Sulistyono, A. N. Hanifah, I. Slamet, and A. Adnan, “Cluster Analysis using K-Means Algorithm and Centroid Linkage for Indonesia Crime Data 2021: Analisis Cluster Menggunakan Algoritma K-Means dan Centroid Linkage pada Data Kriminalitas Indonesia Tahun 2021,” Al-Musthalah: Jurnal Riset dan Penelitian Multidisiplin, vol. 1, no. 1, pp. 54–67, 2021.

[3] E. Sarastuti, D. Mahdiana, and N. Kusumawardhany, “Klasterisasi Tindak Kriminalitas di Provinsi Jawa Barat dengan Menggunakan Algoritma K-Medoids,” Bit (Fakultas Teknologi Informasi Universitas Budi Luhur), vol. 21, no. 1, pp. 84–84, Apr. 2024, doi: https://doi.org/10.36080/bit.v21i1.2976.

[4] M. Julham, S. Sumarno, F. Anggraini, A. Wanto, and S. Solikhun, “Penerapan Jaringan Syaraf Tiruan dalam Memprediksi Tingkat Kriminal di Kabupaten Simalungun Menggunakan Algoritma Backpropagation,” Brahmana Jurnal Penerapan Kecerdasan Buatan, vol. 1, no. 1, pp. 64–73, 2019, doi: https://doi.org/10.30645/brahmana.v1i1.9.

[5] F. Rahmadayanti and R. Rahayu, “PENERAPAN METODE DATA MINING PADA KASUS KRIMINALITAS INDONESIA,” Jurnal Teknologi Informasi MURA, vol. 15, no. 1, pp. 52–61, Jun. 2023, doi: https://doi.org/10.32767/jti.v15i1.2054.

[6] R. Risdianti, A. K. Nasution, R. Oktaviandi, and E. Bu’ulolo, “Penerapan Algoritma Apriori Untuk Mengetahui Pola Jenis Kejahatan Yang Sering Terjadi (Studi Kasus: Polsek Percut Sei Tuan),” Seminar Nasional Sains dan Teknologi Informasi (SENSASI), vol. 3, no. 1, pp. 117–120, 2021.

[7] R. N. Fahmi, M. Jajuli, and N. Sulistiyowati, “Analisis Pemetaan Tingkat Kriminalitas di Kabupaten Karawang menggunakan Algoritma K-Means,” INTECOMS: Journal of Information Technology and Computer Science, vol. 4, no. 1, pp. 67–79, Jun. 2021, doi: https://doi.org/10.31539/intecoms.v4i1.2413.

[8] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” Project Euclid, vol. 5.1, pp. 281–298, 1967.

[9] “Steinhaus, H. (1957) Sur la division des corps matériels en parties. Bulletin L’Académie Polonaise des Science, 4, 801-804. (In French) - References - Scientific Research Publishing,” Scirp.org, 2018.

[10] “Lloyd, S.P. (1957) Least Square Quantization in PCM. IEEE Transactions on Information Theory, 28, 129-137. - References - Scientific Research Publishing,” Scirp.org, 2023.

[11] H. D. Tampubolon, S. Suhada, M. Safii, S. Solikhun, and D. Suhendro, “Penerapan Algoritma K-Means dan K-Medoids Clustering untuk Mengelompokkan Tindak Kriminalitas Berdasarkan Provinsi,” Jurnal Ilmu Komputer dan Teknologi, vol. 2, no. 2, pp. 6–12, Nov. 2021, doi: https://doi.org/10.35960/ikomti.v2i2.703.

[12] N. Dinanti, S. Lestanti, and S. N. Budiman, “PENERAPAN ALGORITMA K – MEANS UNTUK PENGELOMPOKKAN TINDAK KEJAHATAN DI WILAYAH HUKUM POLRES BLITAR KOTA,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 5, pp. 3770–3777, Jan. 2024, doi: https://doi.org/10.36040/jati.v7i5.7815.

[13] D. A. Azhari, Y. Maulita, and S. Ramadani, “Pengelompokan Data Kriminal untuk Menentukan Pola Rawan Tindak Kriminal Menggunakan Algoritma K-Means,” vol. 2, no. 5, pp. 122–138, Sep. 2024, doi: https://doi.org/10.62383/polygon.v2i5.238.

[14] W. W. Pribadi, A. Yunus, and A. S. Wiguna, “PERBANDINGAN METODE K-MEANS EUCLIDEAN DISTANCE DAN MANHATTAN DISTANCE PADA PENENTUAN ZONASI COVID-19 DI KABUPATEN MALANG,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 6, no. 2, pp. 493–500, Aug. 2022, doi: https://doi.org/10.36040/jati.v6i2.4808.

[15] D. Arthur and S. Vassilvitskii, “k-means++: the advantages of careful seeding,” Symposium on Discrete Algorithms, pp. 1027–1035, Jan. 2007, doi: https://doi.org/10.5555/1283383.1283494.

[16] S. Vassilvitskii and D. Arthur, “K-means++: The Advantages of Careful Seeding.” Accessed: Dec. 03, 2024. [Online]. Available: http://theory.stanford.edu/~sergei/slides/BATS-Means.pdf

[17] S. Lloyd, “Least squares quantization in PCM,” IEEE Transactions on Information Theory, vol. 28, no. 2, pp. 129–137, Mar. 1982, doi: https://doi.org/10.1109/tit.1982.1056489.

[18] Q. Du, V. Faber, and M. Gunzburger, “Centroidal Voronoi Tessellations: Applications and Algorithms,” SIAM Review, vol. 41, no. 4, pp. 637–676, Jan. 1999, doi: https://doi.org/10.1137/s0036144599352836.

[19] “Elkan, C. (2003) Using the Triangle Inequality to Accelerate k-Means, In Proceedings of the Twentieth International Conference on International Conference on Machine Learning (ICML’03). AAAI Press, Washington DC, 147-153. - References - Scientific Research Publishing,” Scirp.org, 2024.

[20] J. C. Dunn, “A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters,” Journal of Cybernetics, vol. 3, no. 3, pp. 32–57, Jan. 1973, doi: https://doi.org/10.1080/01969727308546046.

[21] A. Rohmatullah, D. Rahmalia, and M. S. Pradana, “KLASTERISASI DATA PERTANIAN DI KABUPATEN LAMONGAN MENGGUNAKAN ALGORITMA K-MEANS DAN FUZZY C MEANS,” Jurnal Ilmiah Teknosains, vol. 5, no. 2, pp. 86–93, Feb. 2020, doi: https://doi.org/10.26877/jitek.v5i2.4254.

[22] G. N. S. Putri, D. Ispriyanti, and T. Widiharih, “IMPLEMENTASI ALGORITMA FUZZY C-MEANS DAN FUZZY POSSIBILISTICS C-MEANS UNTUK KLASTERISASI DATA TWEETS PADA AKUN TWITTER TOKOPEDIA,” Jurnal Gaussian, vol. 11, no. 1, pp. 86–98, May 2022, doi: https://doi.org/10.14710/j.gauss.v11i1.33996.

[23] A. E. Pramitasari and Y. Nataliani, “PERBANDINGAN CLUSTERING KARYAWAN BERDASARKAN NILAI KINERJA DENGAN ALGORITMA K-MEANS DAN FUZZY C-MEANS,” JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 8, no. 3, pp. 1119–1132, Sep. 2021, doi: https://doi.org/10.35957/jatisi.v8i3.957.

[24] A. Muhammad and E. Budianita, “Pengelompokan Tingkat Kecanduan Game Online Menggunakan Algoritma Fuzzy C-Means,” Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), vol. 5, no. 4, pp. 601–610, Aug. 2022, doi: https://doi.org/10.32672/jnkti.v5i4.4511.

[25] N. Nurdiana, A. Nilogiri, and M. Rahman, “Penerapan Algoritma Fuzzy C-Means dan Metode Elbow untuk Mengelompokkan Provinsi di Indonesia Berdasarkan Indeks Demokrasi Indonesia,” Jurnal Smart Teknologi, vol. 3, no. 5, pp. 544–551, 2022.

[26] M. D. Simatupang and A. W. Wijayanto, “ANALISIS KLASTER BERDASARKAN TINDAKAN KRIMINALITAS DI INDONESIA TAHUN 2019,” Jurnal Statistika Industri dan Komputasi, vol. 6, no. 01, pp. 10–19, Jan. 2021.

[27] N. Azmi, H. HS, Y. Yuyun, and H. Hazriani, “Penerapan Metode K-Means Clustering Dalam Mengelompokkan Data Penjualan Obat pada Apotek M23,” Prosiding SISFOTEK, vol. 7, no. 01, pp. 244–248, Oct. 2023.

[28] I. T. Umagapi, B. Umaternate, H. Hazriani, and Y. Yuyun, “Uji Kinerja K-Means Clustering Menggunakan Davies-Bouldin Index Pada Pengelompokan Data Prestasi Siswa,” Prosiding SISFOTEK, vol. 7, no. 1, pp. 303–308, Oct. 2023.