Abstract
Bread product sales have become an important aspect of the bakery business, influenced by fluctuations in demand that are not easily predictable. Efficient sales management requires a deep understanding of sales patterns. This study aims to optimize bread product sales by using the K-Means Clustering algorithm to analyze daily sales performance at Toko Roti Amin. The data used includes sales volume and transaction frequency for bread products, consisting of 356 data points. The results show that the bread products can be grouped into three clusters: 129 data in the “Good Sales” cluster, 28 data in the “Moderate Sales” cluster, and 199 data in the “Low Sales” cluster. These findings assist bakery owners in managing stock, production planning, and more targeted marketing strategies. Although there are limitations in using K-Means Clustering, such as dependence on the initial centroid selection, this study proves that applying this technique can enhance inventory management and maximize profit in the bakery business.
