Comparing SOM, DBSCAN, and K-Affinity Propagation in Labor Economic Patterns

Authors

  • ID Wiwit Pura Nurmayanti Universitas Mulawarman, Samarinda, Indonesia https://orcid.org/0000-0001-5472-2795
  • ID Desi Yuniarti Universitas Mulawarman, Samarinda, Indonesia
  • ID Meiliyani Siringoringo Universitas Mulawarman, Samarinda, Indonesia
  • ID Ika Purnamasari Universitas Mulawarman, Samarinda, Indonesia
  • ID Desi Febriani Putri Universitas Mulawarman, Samarinda, Indonesia
  • ID Siti Hadijah Hasanah Universitas Terbuka, Tangerang Selatan, Indonesia

DOI:

https://doi.org/10.30812/varian.v9i1.5933

Keywords:

Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Economic Growth, K-Affinity Propagation (K-AP), Labor Force, Self-Organizing Maps (SOM)

Abstract

The objective of this research is to identify the most effective clustering method for grouping Indonesian provinces by labor–economic indicators to support more precise, data-driven policy formulation. Regional disparities in Indonesia’s economic growth, driven by unequal labor characteristics, remain a significant obstacle to achieving inclusive development. An analytical approach capable of grouping provinces by labor and economic indicators is therefore essential. This study applies a comparative clustering analysis using three unsupervised algorithms: Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and K-Affinity Propagation (K-AP). The dataset consists of five key indicators, namely economic growth, total population, labor force, employment rate, and average wage level obtained from Statistics Indonesia (BPS) for the year 2024. The clustering performance is evaluated using internal validation criteria based on the ratio of within-cluster variation (Sw) to between-cluster variation (Sb), where a smaller ratio indicates more compact, well-separated clusters. The results show that each method produces different clustering structures. SOM and DBSCAN generate three clusters with varying provincial distributions, whereas K-AP produces five clusters with more balanced, representative groupings. The evaluation results indicate ratios of 3.1906 for SOM, 0.2000 for DBSCAN, and 0.1779 for K-AP, indicating that K-AP provides the most optimal clustering performance. These findings confirm that K-Affinity Propagation is the most effective and stable method for classifying Indonesian provinces by labor and economic characteristics. The outcomes of this study provide empirical insights and analytical references for labor-driven economic policy formulation and data-driven regional development planning in Indonesia.

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References

Agustino, D. P., Budaya, I. G. B. A., Harsemadi, I. G., Dharmendra, I. K., & Pande, I. M. S. A. (2023). Comparison of the DBSCAN Algorithm and Affinity Propagation on Business Incubator Tenant Customer Segmentation. Jurnal Sisfokom (Sistem Informasi dan Komputer), 12(2), 315–321. https://doi.org/10.32736/sisfokom.v12i2.1682

Aleca, O. E., & Mihai, F. (2025). The Role of Digital Infrastructure and Skills in Enhancing Labor Productivity: Insights from Industry 4.0 in the European Union. Systems, 13(2), 113. https://doi.org/10.3390/systems13020113

Beber, B., Lakemann, T., Schnars, R., & Lay, J. (2025). Employment Effects of Skills Trainings in Sub-Saharan Africa: A Systematic Review of Recent Randomized Controlled Trials. De Economist, 173(1), 87–120. https://doi.org/10.1007/s10645-024-09442-6

Canton, H. (2021, July 8). The Europa Directory of International Organizations 2021 (23rd ed.). Routledge. https://doi.org/10.4324/9781003179900

De Vries, G., Timmer, M., & De Vries, K. (2015). Structural Transformation in Africa: Static Gains, Dynamic Losses. The Journal of Development Studies, 51(6), 674–688. https://doi.org/10.1080/00220388.2014.997222

Diao, X., McMillan, M., & Rodrik, D. (2019). The Recent Growth Boom in Developing Economies: A Structural-Change Perspective. In M. Nissanke & J. A. Ocampo (Eds.), The Palgrave Handbook of Development Economics (pp. 281–334). Springer International Publishing. https://doi.org/10.1007/978-3-030-14000-7_9

Dwitiyanti, N., Siti Ayu Kumala, & Shinta Dwi Handayani. (2024). Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 8(6), 768–778. https://doi.org/10.29207/resti.v8i6.5514

Hassan, B. A., Tayfor, N. B., Hassan, A. A., Ahmed, A. M., Rashid, T. A., & Abdalla, N. N. (2024). From A-to-Z review of clustering validation indices. Neurocomputing, 601, 128198. https://doi.org/10.1016/j.neucom.2024.128198

Hastuti, S. H., Nurmayanti, W. P., & Saputri, A. A. (2023). Penerapan Metode Clustering Self Organizing Maps (som) dan K-Affinity Propagation (k-Ap) dalam Mengelompokkan Nilai Tukar Petani di Indonesia 2022. VARIANCE: Journal of Statistics and Its Applications, 5(1), 79–88. https://doi.org/10.30598/variancevol5iss1page79-88

Hosan, S., Karmaker, S. C., Rahman, M. M., Chapman, A. J., & Saha, B. B. (2022). Dynamic links among the demographic dividend, digitalization, energy intensity and sustainable economic growth: Empirical evidence from emerging economies. Journal of Cleaner Production, 330, 129858. https://doi.org/10.1016/j.jclepro.2021.129858

Iammarino, S., Rodriguez-Pose, A., & Storper, M. (2019). Regional inequality in Europe: Evidence, theory and policy implications. Journal of Economic Geography, 19(2), 273–298. https://doi.org/10.1093/jeg/lby021

Ikotun, A. M., Habyarimana, F., & Ezugwu, A. E. (2025). Cluster validity indices for automatic clustering: A comprehensive review. Heliyon, 11(2), e41953. https://doi.org/10.1016/j.heliyon.2025.e41953

Jaeger, A., & Banks, D. (2023). Cluster analysis: A modern statistical review. WIREs Computational Statistics, 15(3), e1597. https://doi.org/10.1002/wics.1597

Lopez, C., Tucker, S., Salameh, T., & Tucker, C. (2018). An unsupervised machine learning method for discovering patient clusters based on genetic signatures. Journal of Biomedical Informatics, 85, 30–39. https://doi.org/10.1016/j.jbi.2018.07.004

Mason, L., Hicks, B., & Almeida, J. S. (2023). EpiVECS: Exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization. Scientific Reports, 13(1), 21193. https://doi.org/10.1038/s41598-023-48484-9

McKenzie, D. (2017). How Effective Are Active Labor Market Policies in Developing Countries? A Critical Review of Recent Evidence. The World Bank Research Observer, 32(2), 127–154. https://doi.org/10.1093/wbro/lkx001

Melin, P., Monica, J. C., Sanchez, D., & Castillo, O. (2020). Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps. Chaos, Solitons & Fractals, 138, 109917. https://doi.org/10.1016/j.chaos.2020.109917

Nisrina, S., Nurmayanti, W. P., Basirun, Kertanah, & Muhammad Gazali. (2022). Penerapan Metode Clustering SOM dan DBSCAN dalam Mengelompokkan Unmet Need Keluarga Berencana di Nusa Tenggara Barat. J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, 15(2), 237–244. https://doi.org/10.36456/jstat.vol15.no2.a5549

Nurmayanti, W. P., Ratnaningsih, D. J., Nisrina, S., Rahim, A., Malthuf, M., & Kusuma, W. (2022). Clustrering of BPJS National Health Insurance Participant Using DBSCAN Algorithm. Jurnal Varian, 6(1), 25–34. https://doi.org/10.30812/varian.v6i1.1886

Oyewole, G. J., & Thopil, G. A. (2023). Data clustering: Application and trends. Artificial Intelligence Review, 56(7), 6439–6475. https://doi.org/10.1007/s10462-022-10325-y

Putra, I. K., Sukoco, B. M., & Wu, W.-Y. (2025). Landscape and opportunity of rural entrepreneurship: Systematic literature review. Journal of Innovation and Entrepreneurship, 14(1), 122. https://doi.org/10.1186/s13731-025-00580-y

Selmi, A. T. E., Zerarka, M. F., & Cheriet, A. (2025). Efficient technique utilizing an embedding hierarchical clustering-based representation into crossed cubes for TSP optimization. Cluster Computing, 28(2), 92. https://doi.org/10.1007/s10586-024-04741-8

Sorkhi, M. A., Rabbani, M., Akbari, E., & Motameni, H. (2024). A density-based clustering method with calculating the Eps parameter. International Journal of Nonlinear Analysis and Applications. https://doi.org/10.22075/ijnaa.2024.33345.4962

Stromquist, N. P. (2019). World Development Report 2019: The changing nature of work: By the World Bank. Washington, DC, World Bank, 2019, 151 pp. ISBN 978-1-4648-1342-9 (hbk). ISBN 978-1-4648-1328-3 (pbk), ISBN 978-1-4648-1356-6 (eBook), DOI:10.1596/978-1-4648-1328-3. International Review of Education, 65(2), 321–329. https://doi.org/10.1007/s11159-019-09762-9

Wati, R. K., Pratiwi, H., & Winita Sulandari. (2024). Perbandingan Algoritma Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) dan Self-Organizing Map (SOM) untuk Clustering Data Gempa Bumi. Statistika, 24(2). https://doi.org/10.29313/statistika.v24i2.3645

Zhang, X., Lin, F., Wang, Y., & Wang, M. (2022). The impact of digital economy on employment polarization: An analysis based on Chinese provincial panel data. Labor History, 63(5), 636–651. https://doi.org/10.1080/0023656X.2022.2133101

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Published

2026-02-28

Issue

Vol. 9 No. 1 (2026)

Section

Articles

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Copyright (c) 2026 Wiwit Pura Nurmayanti, Desi Yuniarti, Meiliyani Siringoringo, Ika Purnamasari, Desi Febriani Putri, Siti Hadijah Hasanah

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

How to Cite

[1]
“Comparing SOM, DBSCAN, and K-Affinity Propagation in Labor Economic Patterns”, JV, vol. 9, no. 1, pp. 39–52, Feb. 2026, doi: 10.30812/varian.v9i1.5933.

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