Analisis Perbandingan Metode DBSCAN dan Mean Shift Dalam Mengelompokkan Data IPM Kabupaten/Kota se-Indonesia

Authors

  • Muhamad Rizki Fasihullisan Damopolii Universitas Ahmad Dahlan
  • Lisna Zahrotun Universitas Ahmad Dahlan
  • Anna Hendri Soleliza Jones Universitas Ahmad Dahlan

DOI:

https://doi.org/10.37859/jf.v15i3.10797
Keywords: clustering, DBSCAN, human development index, mean shift, SW/SB ratio

Abstract

This study extends previous research that clustered the 2019 Human Development Index (HDI) data of regencies and cities in Indonesia using K-Means, K-Medoids, and Agglomerative Hierarchical Clustering (AHC). HDI is an important indicator for describing the level of regional development; therefore, clustering analysis of HDI data is needed to support more targeted development policy formulation. However, these conventional clustering methods have limitations, including the requirement to predefine the number of clusters and their limited ability to handle noise. Therefore, this study applies and compares two density-based clustering algorithms, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and Mean Shift, which are capable of forming clusters automatically without specifying the number of clusters in advance and can effectively handle noise. The determination of optimal parameters for each method is conducted using the Sw/Sb Ratio metric, which measures the ratio between within-cluster and between-cluster standard deviations. The results show that Mean Shift with an optimal bandwidth parameter of 1 achieves an Sw/Sb Ratio value of 0.3609, which is better than DBSCAN with a value of 0.3739, and also outperforms the clustering methods used in previous studies, which produced a value of 0.51. These findings indicate that density-based clustering algorithms, particularly Mean Shift, provide more representative clustering results for HDI data and may serve as a more effective alternative method for analyzing human development data in Indonesia.

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References

H. T. L. Emilia Khristina Kiha, Sirilius Seran, “PENGARUH JUMLAH PENDUDUK, PENGANGGURAN, DAN KEMISKINAN TERHADAP INDEKS PEMBANGUNAN MANUSIA (IPM) DI KABUPATEN BELU,” Intelektiva J. Ekon. Sos. Hum., vol. 2, no. 07, pp. 60–84, 2021.

E. Luthfi and A. W. Wijayanto, “Analisis perbandingan metode hirearchical, k-means, dan k-medoids clustering dalam pengelompokkan indeks pembangunan manusia Indonesia,” Inovasi, vol. 17, no. 4, pp. 761–773, 2021, doi: 10.30872/jinv.v17i4.10106.

I. GEORGESCU, J. KINNUNEN, A. ANDRONICEANU, and A.-M. ANDRONICEANU, “A Computational Approach to Economic Inequality, Happiness and Human Development,” Inform. Econ., vol. 24, no. 4/2020, pp. 16–28, 2020, doi: 10.24818/issn14531305/24.4.2020.02.

kartal Nazan, “Evaluation of Oecd Countries in Terms of Human Development Index , Mortality Rates and Health OecÜlkeleri̇ni̇İnsani̇Geli̇şmeEndeksi̇ , Ölüm,” Hacettepe Sağlık İdaresi Dergisi, 2025; 28(1), 65-80, vol. 28, no. 1, p. 16, 2025.

J. Salinas and J. Zhang, “Unveiling structural change determinants: A machine learning approach to long-term dynamics,” Socioecon. Plann. Sci., vol. 101, pp. 1–17, 2025, doi: 10.1016/j.seps.2025.102290.

B. Yang and X. Xie, “Analyzing and predicting global happiness index via integrated multilayer clustering and machine learning models,” PLoS One, vol. 20, no. 4 April, pp. 1–20, 2025, doi: 10.1371/journal.pone.0322287.

I. Fahmiyah and R. A. Ningrum, “Human Development Clustering in Indonesia,” J. Adv. Technol. Multidiscip., vol. 02, no. 01, pp. 27–33, 2023.

M. F. J. Muttaqin, “Cluster Analysis Using K-Means Method to Classify Sumatera Regency and City Based on Human Development Index Indicator,” Semin. Nas. Off. Stat., vol. 2022, no. 1, pp. 967–976, 2022, doi: 10.34123/semnasoffstat.v2022i1.1299.

A. D. Pratama and U. Ciptawaty, “Economic Spatial Patterns and Human Development Index Districts and Cities in Five Southern Sumatera Provinces,” J. Pembang. Wil. dan Kota, vol. 18, no. 2, pp. 192–208, 2022, doi: 10.14710/pwk.v18i2.36430.

A. Rahmat, H. Hardi, F. A. Syam, Z. Zamzami, B. Febriadi, and A. P. Windarto, “Utilization of the field of data mining in mapping the area of the Human Development Index (HDI) in Indonesia,” J. Phys. Conf. Ser., vol. 1783, no. 1, 2021, doi: 10.1088/1742-6596/1783/1/012035.

T. A. Cinderatama, R. Z. Alhamri, and Y. Yunhasnawa, “Implementasi Metode K-Means, Dbscan, Dan Meanshift Untuk Analisis Jenis Ancaman Jaringan Pada Intrusion Detection System,” INOVTEK Polbeng - Seri Inform., vol. 7, no. 1, p. 169, 2022, doi: 10.35314/isi.v7i1.2336.

L. Qadrini, “Metode K-Means dan DBSCAN pada Pengelompokan Data Dasar Kompetensi Laboratorium ITS Tahun 2017,” J Stat. J. Ilm. Teor. dan Apl. Stat., vol. 13, no. 2, pp. 5–11, 2020, doi: 10.36456/jstat.vol13.no2.a2886.

R. Reliovani, N. N. Syafitri Husein, K. Z. Abdurrafi, C. Rafqi, A. Husni, and M. A. Khowarizmi, “Algoritma Mean Shift untuk Menentukan Segmentasi Pelanggan pada Penjualan Toko Online,” Gunung Djati Conf. Ser., vol. 3, pp. 93–98, 2021.

R. Rizuan, E. Haerani, J. Jasril, and L. Oktavia, “Penerapan Algoritma Mean-Shift Pada Clustering Penerimaan Bantuan Pangan Non Tunai,” J. Comput. Syst. Informatics, vol. 4, no. 4, pp. 1019–1027, 2023, doi: 10.47065/josyc.v4i4.3876.

H. V. Singh, A. Girdhar, and S. Dahiya, “A Literature survey based on DBSCAN algorithms,” Proc. - 2022 6th Int. Conf. Intell. Comput. Control Syst. ICICCS 2022, no. Iciccs, pp. 751–758, 2022, doi: 10.1109/ICICCS53718.2022.9788440.

M. Nigar Shumaila, “A Comparison of K-Means and Mean Shift Algorithms,” Int. J. Theor. Appl. Math., vol. 7, no. 5, p. 76, 2021, doi: 10.11648/j.ijtam.20210705.12.

P. Bhattacharjee and P. Mitra, “A survey of density based clustering algorithms,” Front. Comput. Sci., vol. 15, no. 1, 2021, doi: 10.1007/s11704-019-9059-3.

A. Rifqi and R. T. Aldisa, “Penerapan Data Mining Untuk Clustering Kualitas Udara,” J. Sist. Komput. dan Inform., vol. 5, no. 2, pp. 289–297, 2023, doi: 10.30865/json.v5i2.7145.

D. E. P. Lature and A. N. Sebayang, “Identifikasi Perilaku Pelanggan Berdasarkan Penilaian Tingkat Kepuasan Menggunakan Mean Shift Clustering,” J. Inform. Press, vol. 1, no. 2, pp. 9–14, 2024.

A. N. Azizah, T. Widiharih, and A. R. Hakim, “Kernel K-Means Clustering untuk Pengelompokan Sungai di Kota Semarang Berdasarkan Faktor Pencemaran Air,” J. Gaussian, vol. 11, no. 2, pp. 228–236, 2022, doi: 10.14710/j.gauss.v11i2.35470.

R. F. Utari, F. Insani, S. Agustian, and L. Afriyanti, “Pengelompokan Data Pendistribusian Listrik Menggunakan Algoritma Mean Shift,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. July, pp. 1015–1023, 2024.

U. Rahardja, Q. Aini, and M. Iqbal, “Analisis Cluster dalam Pengelompokan Provinsi di Indonesia Berdasarkan Variabel Penyakit Menular Menggunakan Metode Complete Linkage, Average Linkage dan Ward,” InfoTekJar J. Nas. Inform. dan Teknol. Jar., vol. 5, no. 1, pp. 40–43, 2020, [Online]. Available: https://doi.org/10.30743/infotekjar.v5i1.2464%0Ahttps://doi.org/10.30743/infotekjar.v5i1.1997

H. Simamora and Bagus Sumargo, “Pengklasifikasian Pengguna Internet Lingkungan Pedesaan Menurut Jenjang Pendidikan di Indonesia menggunakan Metode Cluster Average Linkage,” J. Stat. dan Apl., vol. 3, no. 1, pp. 22–29, 2019, doi: 10.21009/jsa.03103.

H. Sofyan, M. Iqbal, M. Marzuki, and M. Muhammad, “The comparison of k-modes clustering and ROCK clustering to the poverty indicator in Samadua Subdistrict, South Aceh,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1087, no. 1, p. 012085, 2021, doi: 10.1088/1757-899x/1087/1/012085.

J. L. M. Rocha, M. A. C. Zela, N. I. V. Torres, and G. S. Medina, “Analogy of the Application of Clustering and K-Means Techniques for the Approximation of Values of Human Development Indicators,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 9, pp. 526–532, 2021, doi: 10.14569/IJACSA.2021.0120959.

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Published

2026-01-06

Issue

Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)

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