Implementasi Algoritma K-Means Clustering Meggunakan Rapid Miner Untuk Mengelompokan Penjualan Produk Pada Toko Sanjaya Sport - Rilvani, Elkin; Fikr, Muhammad (Jurnal Pelita Teknologi, 2024)
This study examines the implementation of the K-means clustering algorithm using RapidMiner to classify sports product sales data at Toko Sanjaya Sport. The research addresses the lack of a sales analysis system capable of grouping products based on sales performance, which has hindered effective stock and promotional decision-making. The dataset includes 150 sales records from January to December 2024, with attributes such as product name, category, and price. The research follows the Knowledge Discovery in Database (KDD) stages: data selection, preprocessing, transformation, clustering using K-means, and evaluation with the Davies-Bouldin Index (DBI). The results generated three clusters: highly demanded products (15 items), moderately demanded products (41 items), and less demanded products (69 items). The DBI score of 0.667 indicates good clustering quality. Overall, the findings provide valuable insights to support better inventory management and sales strategy planning at Toko Sanjaya Sport.