Penerapan Data Mining Menggunakan Algoritma K-Means Untuk Menentukan Stok Smartphone Berdasarkan Pola Harga Penjualanan - Juni Lapita Hasugian; Andri Wijaya (Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980, 2026)
Intensifying competition in the smartphone retail sector encourages businesses to utilize sales data more effectively to support strategic decision-making, especially in inventory planning. In many cases,sales records are primarily used for routine administrative purposes and are not thoroughly analayzed to uncover sales trends that can guide stock prioritization.This research focuses on the application of data mining techniques to determine smartphone stock priorities by analyzing sales patterns. The dataset used in this study consists of smartphone sales records obtained from a retail store, incorporating attributes such as product pricing and sales volume. The research process includes data preprocessing stages, namely data cleaning and normalization, followed by the implementation of the K-Means clustering algorithm. Through the clustering process, smartphone products are categorized into several groups that reflect high, moderate, and low sales performance. The findings indicate that the K-Means-based data mining approach is effective in identifying sales patterns and classifying products according to their sales levels. The resulting clusters serve as a valuable reference for establishing stock priorities, improving inventory management efficiency, and supporting strategic decision-making in smartphone retail operations. Consequently, the application of data mining techniques offers an effective approach to enhancing inventory control in smartphone retail businesses.