Analisis Produk Terlaris Menggunakan Metode K-Means Clustering Pada “Toko Hartati” - Zalwanda Vadissa Arla; Tata Sutabri (Asosiasi Riset Teknik Elektro dan Informatika Indonesia, 2024)
This research aims to analyze the best-selling products at Toko Hartati using the K-Means Clustering method. K-Means Clustering is an unsupervised learning algorithm that is effective in grouping data based on certain similar characteristics. In this context, the data used includes the number of sales, product prices, and product categories. Through this analysis, it is hoped that insight can be gained regarding products that have the best sales performance, as well as sales patterns that can be used as a reference in stock management and marketing strategies. The data used in this research includes sales transactions during a certain period, with the aim of identifying product clusters based on sales patterns. The analysis results show the existence of two main product groups, where the first cluster contains products with high sales numbers, which can be classified as best-selling products, while the second cluster includes products with lower sales. These findings provide valuable information for the management of Toko Hartati in determining more targeted marketing strategies and more efficient stock management. This research suggests using the K-Means Clustering method in data-based decision making to improve sales performance in retail stores.