Optimization of MSMEs Clustering in Sampang District Using K-Medoids Method and Silhouette Coefficient Method
Micro, Small, and Medium Enterprises (MSMEs) are an important sector in the economy, playing a significant role in creating jobs and driving local economic growth. This study aims to identify the business development patterns of MSMEs in Sampang District using the K-Medoids method. The background i...
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Center for Research and Community Service, Institut Informatika Indonesia Surabaya
2025-03-01
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| Online Access: | https://ejournal.ikado.ac.id/index.php/teknika/article/view/1116 |
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| author | Muhammad Iqbal Firmansyah Yeni Kustiyahningsih Eza Rahmanita Mochammad Syahrul Abidin Budi Dwi Satoto |
| author_facet | Muhammad Iqbal Firmansyah Yeni Kustiyahningsih Eza Rahmanita Mochammad Syahrul Abidin Budi Dwi Satoto |
| author_sort | Muhammad Iqbal Firmansyah |
| collection | DOAJ |
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Micro, Small, and Medium Enterprises (MSMEs) are an important sector in the economy, playing a significant role in creating jobs and driving local economic growth. This study aims to identify the business development patterns of MSMEs in Sampang District using the K-Medoids method. The background issue raised is the lack of appropriate segmentation for MSMEs, which complicates the efforts of the government and business actors in designing suitable development strategies. The dataset used consists of 1,276 MSME data points with six variables: Type of Business, Number of Workers, Production Capacity, Revenue, Assets, and Business License. The data processing steps include data conversion, one-hot encoding, and normalization to ensure uniformity. Clustering is performed using the Elbow method to determine the optimal number of clusters, with K=4 chosen as the optimal cluster number based on the highest Silhouette Coefficient value of 0.5662 compared to other K values. The Silhouette Coefficient values for K=2 are 0.3711, K=5 is 0.5389, K=7 is 0.5201, and K=9 is 0.4737. The clustering results show that this cluster encompasses various types of services, trade, to food and beverages sectors. This segmentation can support data-driven decision-making at the village level. Although this research shows promising results, it is recommended to expand the quantity and variety of data and consider external factors affecting MSME performance. Thus, this study makes a valuable contribution to understanding the business characteristics of MSMEs in Sampang District.
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| format | Article |
| id | doaj-art-50e68dca0e4544faa58cdb7a8fd3d567 |
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| language | English |
| publishDate | 2025-03-01 |
| publisher | Center for Research and Community Service, Institut Informatika Indonesia Surabaya |
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| series | Teknika |
| spelling | doaj-art-50e68dca0e4544faa58cdb7a8fd3d5672025-08-20T02:00:47ZengCenter for Research and Community Service, Institut Informatika Indonesia SurabayaTeknika2549-80372549-80452025-03-0114110.34148/teknika.v14i1.1116Optimization of MSMEs Clustering in Sampang District Using K-Medoids Method and Silhouette Coefficient MethodMuhammad Iqbal Firmansyah0Yeni Kustiyahningsih1Eza Rahmanita2Mochammad Syahrul Abidin3Budi Dwi Satoto4Department of Information Systems, Universitas Trunojoyo Madura, Bangkalan, East Java, IndonesiaDepartment of Information Systems, Universitas Trunojoyo Madura, Bangkalan, East Java, IndonesiaDepartment of Information Systems, Universitas Trunojoyo Madura, Bangkalan, East Java, IndonesiaDepartment of Information Systems, Universitas Trunojoyo Madura, Bangkalan, East Java, IndonesiaDepartment of Information Systems, Universitas Trunojoyo Madura, Bangkalan, East Java, Indonesia Micro, Small, and Medium Enterprises (MSMEs) are an important sector in the economy, playing a significant role in creating jobs and driving local economic growth. This study aims to identify the business development patterns of MSMEs in Sampang District using the K-Medoids method. The background issue raised is the lack of appropriate segmentation for MSMEs, which complicates the efforts of the government and business actors in designing suitable development strategies. The dataset used consists of 1,276 MSME data points with six variables: Type of Business, Number of Workers, Production Capacity, Revenue, Assets, and Business License. The data processing steps include data conversion, one-hot encoding, and normalization to ensure uniformity. Clustering is performed using the Elbow method to determine the optimal number of clusters, with K=4 chosen as the optimal cluster number based on the highest Silhouette Coefficient value of 0.5662 compared to other K values. The Silhouette Coefficient values for K=2 are 0.3711, K=5 is 0.5389, K=7 is 0.5201, and K=9 is 0.4737. The clustering results show that this cluster encompasses various types of services, trade, to food and beverages sectors. This segmentation can support data-driven decision-making at the village level. Although this research shows promising results, it is recommended to expand the quantity and variety of data and consider external factors affecting MSME performance. Thus, this study makes a valuable contribution to understanding the business characteristics of MSMEs in Sampang District. https://ejournal.ikado.ac.id/index.php/teknika/article/view/1116K-MedoidsElbowSilhoutte CoefficientClusteringSimilarity |
| spellingShingle | Muhammad Iqbal Firmansyah Yeni Kustiyahningsih Eza Rahmanita Mochammad Syahrul Abidin Budi Dwi Satoto Optimization of MSMEs Clustering in Sampang District Using K-Medoids Method and Silhouette Coefficient Method Teknika K-Medoids Elbow Silhoutte Coefficient Clustering Similarity |
| title | Optimization of MSMEs Clustering in Sampang District Using K-Medoids Method and Silhouette Coefficient Method |
| title_full | Optimization of MSMEs Clustering in Sampang District Using K-Medoids Method and Silhouette Coefficient Method |
| title_fullStr | Optimization of MSMEs Clustering in Sampang District Using K-Medoids Method and Silhouette Coefficient Method |
| title_full_unstemmed | Optimization of MSMEs Clustering in Sampang District Using K-Medoids Method and Silhouette Coefficient Method |
| title_short | Optimization of MSMEs Clustering in Sampang District Using K-Medoids Method and Silhouette Coefficient Method |
| title_sort | optimization of msmes clustering in sampang district using k medoids method and silhouette coefficient method |
| topic | K-Medoids Elbow Silhoutte Coefficient Clustering Similarity |
| url | https://ejournal.ikado.ac.id/index.php/teknika/article/view/1116 |
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