Penerapan Algoritma K-Means Clustering Dalam Pola Penjualan Beras

This study uses K-Means Clustering to analyze rice sales patterns with the aim of identifying market segments based on purchasing characteristics. The data analyzed include sales volume, purchase frequency and price. The clustering results show several consumer groups with similar purchasing patterns, allowing producers and retailers to better adjust their marketing strategies. These findings provide useful insights to improve the effectiveness of promotional campaigns and meet market needs more efficiently. K-Means Clustering is one of the data analysis techniques that is often used to group objects based on similar attributes. Identify different market segmentation and purchasing patterns that may not be directly visible. This study aims to cluster rice sales data to reveal hidden patterns in sales transactions. By applying K-Means Clustering, this study identifies several consumer groups that have similar characteristics in terms of rice purchases. The results of this study provide insight into market segments that can be used for more effective marketing strategies and product personalization. These findings are expected to help rice producers and retailers design more targeted promotional campaigns and increase efficiency in meeting market needs.