Analisis Metode K-Means Clustering Dalam Pengelompokan Penjualan Sembako - Fikria, Rahma; Sriani, Sriani (Journal of Information System Research (JOSH), 2024)
This research aims to analyze the clustering of staple food sales at PT. Sinarmas Distribusi Nusantara using the K-Means Clustering method. The main problem faced by the company is the lack of clarity in identifying which products have high sales performance and which products require more attention. This issue negatively impacts the effectiveness of the company's marketing and distribution strategies. The K-Means Clustering method is used to divide the sales data of staple food products into several clusters based on the similarity of their characteristics. Sales data is collected and analyzed to group products based on their sales levels. The research results show that out of all the products studied, 5 products fall into the "Fast-Selling" category, 2 products into the "Slow-Selling" category, and 51 products into the "Non-Selling" category. Evaluation of the clustering results using the Davies-Bouldin index yielded a value of 0.8911, indicating a reasonably good clustering quality. In conclusion, the K-Means Clustering method is effective in identifying sales patterns of staple food products, thus providing a basis for strategic decision-making in sales management.