<![CDATA[This study uses the K-Means clustering algorithm to examine furniture sales patterns at Eka Perabot Store in Tangerang City. Understanding sales patterns can help the store develop better inventory management and marketing strategies. This study analyzed sales data to identify clustering patterns based on product price and quantity. Sales data collection, preprocessing, and the application of the K-Means algorithm using RapidMiner software were all part of the research process. The cleaned data was then grouped into clusters based on similar characteristics, resulting in product groups with specific characteristics, such as low-priced and high-selling products. The results showed that organizing with K-Means successfully divided products into categories appropriate to the store. The K-Means clustering method proved effective in helping Eka Perabot Store understand customer preferences and develop better sales strategies because each cluster has unique characteristics that can be used as a basis for business decision-making.]]>
Copyrights © 2025
