Implementasi Algoritma K-means Clustering Data Penjualan Pada Warung Sembako Isan Menggunakan Rapidminer - Muhammad Azriel; Daviqia Fadel; Fajri Maulana Azzam Harahap; Irsad Fauzan; Muhammad Fadlan Jabbar; Maulana Fansyuri (Journal of Information Technology and Informatics Engineering, 2025)
This study aims to apply the K-Means Clustering algorithm with the help of RapidMiner software on sales data at Warung Sembako Isan. In managing small businesses such as grocery stores, processing sales data manually often faces various challenges, such as errors in recording and difficulties in identifying sales trends. Therefore, data mining techniques, especially clustering methods, are used to categorize products based on their sales capabilities. This process is carried out using RapidMiner, which allows analysis without the need for programming through a visual interface. The data were analyzed using the K-Means algorithm with parameter k = 3, which produces three categories: products with high potential, medium potential, and low potential. The results of this clustering make it easier for shop owners to understand product performance, develop storage strategies, and plan more efficient promotions. This study shows that the use of simple technology can improve operational efficiency and assist MSMEs in data-based decision making.