Main Article Content


In rural areas of Indonesia, micro, small, and medium enterprises (MSMEs) are often isolated; however, they have been proven to play an important role as the economic backbone of millions of communities. In fact, the sluggish development of MSMEs in Indonesia become a severe problem for the community welfare. The government continues to strive for the welfare of the local communities, one of which is by supporting the existing MSMEs. However, the provision of government assistance may not be optimal for the incorrect target of the MSMEs. This study informs the government and other related parties regarding subdistrict groups whose MSMEs are considered to be their target. The k-affinity propagation method was used to find a set of representative examples, called exemplars, that best summarize the data. The result shows that sub-districts clusters based on general welfare in five commodities. K-affinity propagation algorithm clusters vary by commodity. Data fluctuation from each commodity’s three factors causes this. From this research, it can be determined which subdistricts have the most or least prosperous MSMEs in each of the five commodities analyzed.


MSMEs K-affinity propagation Commodity

Article Details

How to Cite
Tarisya Qurrota A’yuni, Baiq Nina Febriati, Lazuardy Ilham Effendie, Muhammad Muhajir, & Yotenka , R. . (2023). MSME Sales Clustering Based on Business Aid Distribution Priority Using K-Affinity Propagation. Enthusiastic : International Journal of Applied Statistics and Data Science, 3(1), 111–124.


  1. M.I. Mahdi, “Berapa Jumlah UMKM di Indonesia?,”, 2022.
  2. I.Y. Niode, “Sektor UMKM di Indonesia: profil, Masalah dan Strategi Pemberdayaan,” Jurnal Kajian Ekonomi dan Bisnis OIKOS-NOMOS, vol. 2, no. 1, pp. 1–10, 2019, [Online]. Available:
  3. A. Syarifudin, “Jumlah UMKM di Sleman Meningkat Hingga 90 Ribu Selama Pandemi Covid-19,”, 2022.
  4. M. Kriesdinar, “Jumlah UMKM di Sleman Meningkat Signifikan di Masa Pandemi,”, 2021.
  5. T. Nenova, C.T. Niang, and A. Ahmad, Bringing Finance to Pakistan’s Poor: Access to Finance for SME and the Unserved. Washington D.C., USA: The World Bank, 2009.
  6. Supriyanto, “Pemberdayaan Usaha Mikro, Kecil dan Menengah (UMKM) di Kota Malang Berbasis Webgis.5,” Jurnal Ekonomi dan Pendidikan, vol. 3 No.1, pp. 1–16, 2012, doi: 10.21831/jep.v3i1.627.
  7. E. Rouza and L. Fimawahib, “Implementasi Fuzzy C-Means Clustering dalam Pengelompokan UKM Di Kabupaten Rokan Hulu,” Techno.Com: Jurnal Teknologi Informasi, vol. 19, no. 4, pp. 481–495, 2020, doi: 10.33633/tc.v19i4.4101.
  8. D. Remawati, D.J.A. Putra, and T. Irawati, “Metode K-Means untuk Pemetaan Persebaran Usaha Mikro Kecil dan Menengah,” Jurnal TIKomSiN, vol. 9, no. 2, p. 39, 2021, doi: 10.30646/tikomsin.v9i2.574.
  9. Y.K. Siregar, “Analisis Perbandingan Algoritma Fuzzy C-Means dan K-Means,” Annual Research Seminar 2016, vol. 2, no. 1, pp. 151–155, 2016.
  10. N.I. Asriny, M. Muhajir, and D. Andrian, “K-Affinity Propagation Clustering Algorithm for the Classification of Part-Time Workers Using the Internet,” Indonesian Journal Electrical Engineering and Computer Science, vol. 24, no. 1, pp. 464–472, 2021, doi: 10.11591/ijeecs.v24.i1.pp464-472.
  11. K. Maheswari, “Finding Best Possible Number of Clusters using K-Means Algorithm,” International Journal of Engineering and Advanced Technology, vol. 9, no. 1S4, pp. 533–538, 2019, doi: 10.35940/ijeat.a1119.1291s419.
  12. M. Charrad, N. Ghazzali, and A.N. Boiteau, “NbClust: an R package for Determining the Relevant Number of Clusters in a Data Set,” Journal of Statistical Software, vol. 61, no. 6, pp. 1–36, 2014, doi: 10.18637/jss.v061.i06.
  13. X. Zhang, W. Wang, K. Nørvåg, and M. Sebag, “K-AP: Generating Specified K Clusters by Efficient Affinity Propagation,” 2010 IEEE International Conference on Data Mining, 2010, pp. 1187–1192, doi: 10.1109/ICDM.2010.107.
  14. B.J. Frey and D. Dueck, “Clustering by Passing Messages between Data Points,” Science, vol. 315, no. 5814, pp. 972–976, 2007, doi: 10.1126/science.1136800.
  15. A.M. Serdah and W.M. Ashour, “Clustering Large-Scale Data Based on Modified Affinity Propagation Algorithm,” Journal of Artificial Intelligence and Soft Computing Research, vol. 6, no. 1, pp. 23–33, 2016, doi: 10.1515/jaiscr-2016-0003.
  16. N.M. Arzeno and H. Vikalo, “Semi-Supervised Affinity Propagation with Soft Instance-Level Constraints,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 5, pp. 1041–1052, 2015, doi: 10.1109/TPAMI.2014.2359454.
  17. H. Jia, L. Wang, H. Song, Q. Mao, and S. Ding, “A k-ap Clustering Algorithm Based on Manifold Similarity Measure,” in Intelligent Information Processing IX, Z. Shi, E. Mercier-Laurent, and J.Z. Li Eds. New York, USA: Springer Cham, 2018, doi: 10.1007/978-3-030-00828-4_3.
  18. A.F. Moiane and Á.M.L. Machado, “Evaluation of the Clustering Performance of Affinity Propagation Algorithm Considering the Influence of Preference Parameter and Damping Factor,” Boletim de Ciências Geodésicas, vol. 24, no. 4, pp. 426–441, 2018, doi: 10.1590/S1982-21702018000400027.
  19. X. Zhang, C. Furtlehner, C. Germain-Renaud, and M. Sebag, “Data Stream Clustering with Affinity Propagation,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 7, pp. 1644–1656, 2014, doi: 10.1109/TKDE.2013.146.
  20. M. Muhajir and N.N. Sari, “K-Affinity Propagation (K-AP) and K-Means Clustering for Classification of Earthquakes in Indonesia,” 2018 International Symposium on Advanced Intelligent Informatics (SAIN), 2019, pp. 6–10, doi: 10.1109/SAIN.2018.8673344.
  21. L. Hubert and J. Schultz, “Quadratic Assignment as a General Data-Analysis Strategy,” British Journal of Mathematical and Statistical Psychology, vol. 29, pp. 190–241, 1976, doi: 10.1111/j.2044-8317.1976.tb00714.x.
  22. D.L. Davies and D.W. Bouldin, “A Cluster Separation Measure,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 4, pp. 224–227, 2000, doi: 10.1109/TPAMI.1979.4766909.
  23. I. Gurrutxaga, J. Muguerza, O. Arbelaitz, J. M. Pérez, and J.I. Martín, “Towards a Standard Methodology to Evaluate Internal Cluster Validity Indices,” Pattern Recognition Letters, vol. 32, no. 3, pp. 505–515, 2011, doi: 10.1016/j.patrec.2010.11.006.
  24. J.O. McClain and V.R. Rao, “CLUSTISZ: A Program to Test For The Quality of Clustering of A Set of Objects,” Journal of Marketing Research, vol. 12, pp. 456–460, 1975.
  25. B. Desgraupes, “Clustering Indices ClusterCrit,” CRAN Packag., no. April, pp. 1–10, 2013, [Online]. Available:
  26. A. Banerjee, I.S. Dhillon, J. Ghosh, and S. Sra, “Clustering on the Unit Hypersphere Using Von Mises-Fisher Distributions,” Journal of Machine Learning Research, vol. 6, pp. 1345–1382, 2005.
  27. M.J. Bunkers , J.R. Miller Jr., and A.T. DeGaetano, “Definition of Climate Regions in the Northern Plains Using an Objective Cluster Modification Technique,” Journal of Climate, vol. 9, no. 1, pp. 130–146, 1996, doi: 10.1175/1520-0442(1996)009<0130:DOCRIT>2.0.CO;2.
  28. A.R. Barakbah and K. Arai, “Identifying Moving Variance to Make Automatic Clustering for Normal Data Set,” in Proceedings of the IECI Japan Workshop, 2004, pp. 26–30.