ANALISIS PENGELOMPOKAN POLA PENJUALAN PRODUK UMKM MENGGUNAKAN ALGORITMA K-MEANS - Putri Diana; Achmad Solichin (LPPM Universitas Abdurrab, 2026)
Developments in machine learning technology offer significant opportunities for Micro, Small, and Medium Enterprises (MSMEs), particularly those operating in the Warung Madura sector, to analyze sales patterns in greater depth through data processing. This article aims to evaluate the sales patterns of MSME Warung Madura products by utilizing the K-Means Clustering algorithm. The data used in this study include several key parameters, namely product price, sales volume, and profit, taken from sales transaction records at one MSME Warung Madura. The analysis was carried out through a process that includes pre-processing, information normalization, and the application of the K-Means algorithm with a value of k = 3 to group products based on similarities in their sales characteristics. The findings of this study indicate the formation of three product categories, namely (1) products with affordable prices and small margins, (2) premium products with high sales levels and large profits, and (3) products with stable sales performance. The results of this clustering provide a clear product map, allowing business owners to allocate resources (stock, promotions) more strategically based on cluster characteristics. Thus, the application of machine learning using the K-Means algorithm can provide valuable insights to support the digitalization process and increase efficiency in managing the Warung Madura MSME business.