SISTEM INFORMASI CLUSTER DATA BARANG UNTUK MENGETAHUI MINAT BELI KONSUMEN PADA TOKO BROTO MENGUNAKAN ALGORITMA K-MEANS: Indonesia - Habibanis, Fauzani; Ahmad Heru Mujianto (Fakultas Teknologi Informasi, 2025)
An information system for managing sales and inventory data is highly needed by business owners, especially for Broto Store, which operates at a mid-to-upper sales scale. Manual data processing is highly ineffective and can complicate record-keeping, leading to inaccuracies in reporting and ultimately affecting business decisions, particularly in inventory management. This study aims to develop a computerized sales and inventory information system equipped with a clustering feature using the K-Means algorithm to identify consumer buying interest in the products sold. The K-Means algorithm is utilized in this system by using total stock and total sales data as variables to determine the cluster results: less popular, popular, and very popular. The system is designed as a web-based application to simplify operations, transaction recording, inventory management, and consumer buying interest analysis. The implementation results of the clustering process performed by the system showed an accuracy of 94.44% when calculated using the clustering match ratio compared to manual calculations.