Abstract
Everbright has become one of the distributing companies selling various products, one of which is the Dettol product. In optimizing the stock and marketing of Dettol products, the company faces difficulties in identifying the best-selling products and those less favored by customers. This research data originates from the sales transaction data of Dettol soap in November, comprising 77 sales data. Through the use of data mining, particularly the K-means Clustering method, it becomes a relevant approach to solving this issue. The objective of this research is to avoid excess inventory that remains unsold while meeting the diverse needs of customers. The results of this study show the visualization of the distribution of Dettol soap product clusters and grouping based on sales levels. There were 21 best-selling item data, 31 well-selling item data, and 25 less popular item data, enabling the company to manage inventory based on the best-selling items.
