Application of the K-Means Clustering Algorithm for Sales Data Clustering to Identify Best-Selling Products at PT Cahaya Surga Teknik Indonesia - Amran Sitohang; R. Mahdalena Simanjorang; Fittra Ferdiansyah; Angel Nurfadilah (STMIK Methodist Binjai, 2026)
Business competition demands that companies utilize sales data as a basis for strategic decision-making. PT Cahaya Surga Teknik Indonesia has sales data that continues to grow each period, but has not been optimally utilized to identify best-selling products. This study aims to apply the K-Means Clustering algorithm to group sales data so that it can identify the categories of best-selling products, medium-selling products, and less-selling products. The research method uses data mining with KDD (Knowledge Discovery in Database) stages including data selection, preprocessing, transformation, clustering process using the K-Means algorithm, and evaluation of cluster results. The research dataset consists of annual sales data (content year) with attributes of number of transactions, number of sales, and total revenue. The results show that the K-Means algorithm is able to divide the data into 3 main clusters: the very best-selling product cluster, the moderately best-selling product cluster, and the less-selling product cluster. The clustering results can help companies in determining stock strategies, promotions, and sales planning.