Application of the DBSCAN Algorithm in MSME Clustering using the Silhouette Coefficient Method
Abstract
MSMEs participate in the very important contribution of developing Indonesia's economy, where this industry contributes to GDP and also to the absorption of labor. Most MSMEs in Sidoarjo Regency are still constrained by financial management and the utilization of technology. This research will apply the DBSCAN method to clustering MSMEs in Sidoarjo for the purpose of finding patterns in characteristics related to capital, turnover, and workforce. The analysis will involve 1,479 MSMEs, while the research methodology applies the CRISP-DM method to guide the process from business understanding up to the implementation phase. Normalization using Simple Feature Scaling was applied before clustering. The results of this analysis provide insight that the best possible combination of the parameters in DBSCAN is epsilon (ε) 0.10 and MinPts 16, which gives the optimal value of Silhouette Score as 0.4304. It creates seven clusters, in which the third has the highest Silhouette value of 0.9326, indicating that there are high similarities recorded within that cluster. These results provide essential lessons to develop more targeted policy strategies and interventions for MSMEs in Sidoarjo and explore the capabilities of DBSCAN as an effective analytical tool in determining the characteristics of businesses in the region.
Downloads
References
S. R. R. S. R. P. Djoko Wahyudi, “Empowerment Of Micro Small And Medium Enterprises A Presto Milkfish In The Era Of Pandemics Kanigaran Sub District Probolinggo City,” INTEGRITAS : Jurnal Pengabdian, vol. 5, pp. 26 - 32, 2021.
A. R. L. S. Maria Mu’ti Wulandari, “Efforts To Development Of Entrepreneurship Koperasi And Micro, Small And Medium Business Post Covid-19,” Soedirman Law Review, vol. 5, pp. 120 - 136, 2023.
F. G. S. G. M. K. Fanniar Aurelia, “Peranan Sistem Digital Accounting Terhadap Perkembangan Bisnis,” Jurnal Manajemen dan Ekonomi Bisnis, vol. 3, pp. 26 - 40, 2023.
K. M. B. S. A. F. Indah Permata Sari, “Analisa Kebijakan Publik Terhadap UMKM,” Jurnal Ilmiah Mahasiswa Pendidikan Sejarah, vol. 8, pp. 552 - 559, 2023.
L. E. Z. O. H. H. Dwi Utari Iswavigra, “Marketing Strategy UMKM dengan CRISP-DM Clustering dan Promotion Mix Menggunakan Metode K-Medoids,” Jurnal Informasi dan Teknologi, vol. 5, pp. 45 - 54, 2023.
A. R. I. A. F. Dwi Astuti, “Penentuan Strategi Promosi Usaha Mikro Kecil Dan Menengah (UMKM) Menggunakan Metode CRISP-DM dengan Algoritma K-Means Clustering,” Journal of Informatics, Information System, Software Engineering and Applications, vol. 1, pp. 60 - 72, 2019.
D. A. W. W. Zicola Vladimir, “Klasterisasi Topik Skripsi Informatika Dengan Metode DBSCAN,” Jurnal Algoritme , vol. 3, pp. 82 - 90, 2022.
M. A. H. R. W. Aulia Fadhli Boer, “Pengelompokan Daerah Rawan Bencana di Pulau Sumatera dengan,” Prosiding Seminar Nasional, vol. 6, pp. 389 - 400, 2023.
M. Nana Nurhaliza, “Clustering of Data Covid-19 Cases in the World Using DBSCAN Algorithms,” Indonesian Journal of Informatic Research and Software Engineering, vol. 1, pp. 1 - 8, 2021.
A. Z. A. H. P. L. I. Rahmatina Hidayati, “Analisis Silhouette Coefficient pada 6 Perhitungan Jarak K-Means Clustering,” Techno.Com, vol. 20, pp. 186 - 197, 2021.
Y. L. W. L. J. H. Zhichao Li, “Crowdsourcing Logistics Pricing Optimization Model Based on DBSCAN Clustering Algorithm,” IEEE Access, vol. 8, pp. 92615 - 92626, 2020.
K. J. S. R. M. Z. Shapol M. Mohammed, “Glove Word Embedding and DBSCAN algorithms for Semantic Document Clustering,” IEEE Access, pp. 211 - 216, 2021.
D. Deng, “Research on Anomaly Detection Method Based on DBSCAN Clustering Algorithm,” International Conference on Information Science, Computer Technology and Transportation, pp. 439 - 442, 2021.
S.-S. Li, “An Improved DBSCAN Algorithm Based on the Neighbor Similarity and Fast Nearest Neighbor Query,” IEEE Access, vol. 8, pp. 47468 - 47476, 2020.
G. Y. Adil Abdu Bushra, “Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based Clustering Algorithms,” IEEE Access, vol. 9, pp. 87918 - 87935, 2021.
D. Deng, “DBSCAN Clustering Algorithm Based on Density,” International Forum on Electrical Engineering and Automation, pp. 949 - 953, 2021.
W. H. X. X. J. Y. Yong Huang, “An empirical study of personalized advertising recommendation based on DBSCAN clustering of sina weibo user-generated content,” 10th International Conference of Information and Communication Technology, pp. 303 - 310, 2021.
J. S.-P. Juan Carlos PERAFA´ N-LO´ PEZ, “An unsupervised pattern recognition methodology based on factor analysis and a genetic-DBSCAN algorithm to infer operational conditions from strain measurements in structural applications,” Chinese Journal of Aeronautics, pp. 165 - 181, 2021.
N. H. R. C. d. A. Stiphen Chowdhurya, “Feature weighting in DBSCAN using reverse nearest neighbours,” Elsevier, pp. 1 - 15, 2023.
A. A. S. G. F. L. G. Kevin Surya Pranata, “Development clustering system IDX company with k-means algorithm and DBSCAN based on fundamental indicator and ESG,” 7th International Conference on Computer Science and Computational Intelligence, pp. 319 - 327, 2023.
C. Y. A. L. Y. C. L. C. Bing Ma, “A Faster DBSCAN Algorithm Based on Self-Adaptive Determination of Parameters,” 10th International Conference on Information Technology and Quantitative Management, pp. 113 - 120, 2023.
