K-Means Algorithm Method for Clustering Best-Selling Product Data at XYZ Grocery Stores

Ridzki, Mohamad Maulana
Hadijah, Ijah
Mukidin, Mukidin
Azzahra, Adelia
Nurjanah, Aisyah

International Journal of Social Service and Research
Vol. 3 No. 12 (2023): International Journal of Social Service and Research (IJSSR)

This study aims to utilize the K-means clustering algorithm in data mining to categorize sales data at XYZ Grocery store. The research is essential for understanding sales patterns and enhancing inventory management strategies. The research methodology involves implementing the K-means clustering algorithm to generate centroid values for each cluster, thereby creating groups of products based on their sales performance. The findings of this study are expected to provide insights into sales trends at the store. While the abstract provides a general overview, specific results and contributions of this research are not detailed. Further studies could offer a more in-depth understanding of the practical applications of these findings in improving store management and inventory control.