Penerapan Teknik Clustering untuk Analisis Pemerataan Angkutan Umum di Kabupaten Bogor

Emir Muhammad Al-Fariq; Safrizal Safrizal

doi:10.55606/jitek.v5i3.8417

Authors

Emir Muhammad Al-Fariq Universitas Pembangunan Jaya
Safrizal Safrizal Universitas Pembangunan Jaya

DOI:

https://doi.org/10.55606/jitek.v5i3.8417

Keywords:

Bogor Regency, Clustering, Data Mining, K-Means, Public Transportation, Service Equity

Abstract

This research analyzes the distribution of public transport services from AKDP and AKDK units in 40 sub-districts in Bogor Regency, an area with rapid population growth that faces congestion due to dependence on private vehicles. The main problem is that service gaps have not been mapped quantitatively, so the aim of this research is to apply clustering techniques to identify underserved areas and provide an objective basis for data-based policy recommendations. The proposed method is a quantitative approach using the K-Means Clustering algorithm on secondary datasets from the Department of Transportation with three main features: number of licensed fleets, number of unlicensed fleets, and total fleet. The main findings succeeded in classifying sub-districts into three clusters, Cluster 0 (established services), Cluster 1 (static growth), and Cluster 2 (significant service gaps dominated by informal fleets). In conclusion, this research proves the existence of significant service disparities, and shows that the clustering method is effective in mapping priority zones for more equitable transportation policy interventions.

References

[1] P. Kawasan et al., “T A T A L O K A The Development of Settlement in the Transit Areas of Bogor Regency,” vol. 24, pp. 349–365, 2022.

[2] N. P. Azwanda, A. Kota Bogor, and J. Barat, “EFEKTIVITAS BUS RAPID TRANSIT BISKITA TRANS PAKUAN DALAM MENGURANGI KEMACETAN DI KOTA BOGOR,” 2024.

[3] V. Carolin and E. Kurniati, “TANTANGAN PEMBANGUNAN PERKOTAAN TERHADAP URBANISASI, KEMACETAN DI JAKARTA: ANALIS PERMASALAHAN DAN SOLUSI,” Jurnal Ilmu Ekonomi, vol. 4, no. 1, pp. 252–273, Apr. 2025, doi: 10.59827/jie.v4i1.222.

[4] J. A. Ariesandi, R. Resita, and Z. Salsabila, “KEBIJAKAN TRANSPORTASI UMUM (ANGKOT),” vol. 11, pp. 55–112, Oct. 2020.

[5] A. wulandari, Irmayansyah, and L. T. Ningrum, “PENERAPAN METODE K-MEANS CLUSTERING UNTUK PEMETAAN,” vol. 7, pp. 107–116, Feb. 2025.

[6] M. Annas and S. N. Wahab, “Data Mining Methods: K-Means Clustering Algorithms,” International Journal of Cyber and IT Service Management, vol. 3, no. 1, pp. 40–47, Mar. 2023, doi: 10.34306/ijcitsm.v3i1.122.

[7] N. Selvia, E. W. Ambarsari, and N. Dwitiyanti, “Shortest Path Clustering Dalam Menyaring Tingkat Kepadatan Arus Lalu Lintas,” JURIKOM (Jurnal Riset Komputer), vol. 10, no. 2, p. 396, Apr. 2023, doi: 10.30865/jurikom.v10i2.5979.

[8] J. Manajemen et al., “Pengaruh Data Warehouse Terhadap Pengambilan Keputusan Article History,” vol. 3, no. 1, 2024.

[9] F. Dwi Ratnaningtyas and D. Widi Astuti, “KURANG OPTIMALNYA FUNGSI DARI TERMINAL CEPU TYPE A,” 2022. [Online]. Available: http://siar.ums.ac.id/

[10] I. hiqmah Turmuzi, D. Mellenia, and U. Ikhwana Fadhlih, “IMPLEMENTASI TEKNOLOGI BIG DATA DI PEMERINTAHAN INDONESIA,” 2023.

[11] D. Rahmiyati and E. Britantono Siswantoro, “Penerapan Metode K-Means untuk Pemetaan Objek Wisata Sebagai Rekomendasi Prioritas Pengembangan Pariwisata,” 2025. [Online]. Available: http://creativecommons.org/licences/by/4.0/

[12] A. R. Yudistira, H. A. Yasmin, and M. K. Pasya, “Upaya Mengatasi Kemacetan di Jawa Barat Melalui Pengelompokkan Kendaraan Pribadi,” Gunung Djati Conference Series, vol. 50, 2025.

[13] H. F. Ramadhan, A. Fauzi, C. N. Rupelu, D. P. Aprillia, N. D. Anjani, and Halimatusadiah, “PENGARUH BUSINESS INTELLIGENCE TERHADAP PERUSAHAAN,” Jun. 2022.

[14] J. Zemnickis, “Data Warehouse Data Model Improvements from Customer Feedback,” Baltic Journal of Modern Computing, vol. 11, no. 3, pp. 475–499, 2023, doi: 10.22364/bjmc.2023.11.3.08.

[15] H. Mulyani, R. A. Setiawan, and H. Fathi, “Optimization of K Value in Clustering Using Silhouette Score (Case Study: Mall Customers Data),” Journal of Information Technology and Its Utilization, vol. 6, no. 2, pp. 45–50, Dec. 2023, doi: 10.56873/jitu.6.2.5243.

[16] D. Kurniadi, Y. Handoko Agustin, H. Ilham Nur Akbar, and I. Farida, “Penerapan Algoritma k-Means Clustering untuk Pengelompokan Pembangunan Jalan pada Dinas Pekerjaan Umum dan Penataan Ruang,” AITI: Jurnal Teknologi Infor-masi, vol. 20, no. 1, pp. 64–77, 2022.

[17] S. Paembonan, H. Abduh, and K. Kunci, “Penerapan Metode Silhouette Coeficient Untuk Evaluasi Clutering Obat Clustering; K-means; Silhouette coeficient,” 2021. [Online]. Available: https://ojs.unanda.ac.id/index.php/jiit/index

[18] Reynaldi, R. J. F. Djarot, M. Wahyudi, Sumanto, and A. S. Budiman, “Analisa Pola Penyebaran Pengguna Layanan Transjakarta dengan Metode K-Means Clustering,” 2025.

[19] “Jumlah Angkutan Umum (Unit) AKDP dan AKDK Menurut Kecamatan Di Kabupaten Bogor,” Nov. 2024. Accessed: Sep. 18, 2025. [Online]. Available: https://opendata.bogorkab.go.id/dataset/jumlah-angkutan-umum--unit-akdp-dan-akdkmenurut-kecamatan-di-kabupaten-bogor

[20] “Jumlah Angkutan Umum Berizin (Unit) AKDP dan AKDK Menurut Kecamatan Di Kabupaten Bogor,” 2024. Ac-cessed: Sep. 18, 2025. [Online]. Available: https://opendata.bogorkab.go.id/dataset/jumlah-angkutan-umum-berizin-unit-akdp-dan-akdkmenurut-kecamatan-di-kabupaten-bogor