Application of the K-Means Clustering Algorithm for Sales Analysis in a Padang Restaurant Business

Abstract

This study aims to apply the K-Means Clustering algorithm to analyze sales data and categorize products based on their sales performance, namely best-selling, moderately-selling, and least-selling products. The data used in this research comes from a Padang restaurant in Deltamas in 2022 to 2023. The research process began with data collection, followed by data cleaning and normalization using the Min-Max method, and then the application of the K-Means algorithm for clustering. The results show that the K-Means algorithm successfully grouped the products into three categories: Cluster 0 (best-selling products), Cluster 1 (moderately-selling products), and Cluster 2 (least-selling products). Thus, this study demonstrates that the K-Means algorithm can be used to cluster sales data and assist business owners in managing product inventory and making more informed decisions.