<![CDATA[The rapid growth of e-commerce has generated a large volume of transactional data; however, this data has not been fully utilized to support strategic decision-making, particularly in product segmentation. The main problem addressed in this study is the absence of a systematic product grouping approach based on key attributes such as price, discount, and revenue, which leads to less effective pricing and promotional strategies. Therefore, this study aims to analyze product sales patterns and cluster e-commerce products based on the characteristics of price, discount_percent, and total_revenue. The dataset used is an Amazon-style e-commerce dataset consisting of 50,000 transaction records and 13 attributes, with the analysis focusing on the three main attributes as the basis for clustering. The method applied in this research is K-Means Clustering, which involves data preprocessing, normalization using Min-Max Scaling, and determining the optimal number of clusters using the Elbow Method and Silhouette Score. The results indicate that the optimal number of clusters is three clusters, supported by the highest Silhouette Score of 0.354 and a clear elbow pattern in the Elbow graph. Additional evaluation using the Davies-Bouldin Index of 0.9335 indicates that the clustering quality is fairly good, although not yet optimal. The clustering results produce three main groups: premium product cluster (high price, low discount, high revenue), discount product cluster (moderate price, high discount, moderate revenue), and low-performance product cluster (low price, low discount, low revenue). In conclusion, the K-Means algorithm is capable of effectively clustering e-commerce products based on relevant numerical attributes and generating insights that can support business strategies such as pricing and promotional decisions.]]>
