Implementasi Data Mining Untuk Analisis Prilaku Konsumen Dalam Meningkatkan Penjualan Di Shopee - Pratama, Asyraf Muntasir; Siswanto, Siswanto; Zulfiandry, Ricky (UNIVED Press, 2025)
The rapid growth of e-commerce requires an in-depth understanding of consumer behavior to increase sales. This research applies a data mining method using the K-Means clustering algorithm to analyze weekly revenue data from the Anjani_Store.id store on the Shopee platform, in the period from January to July 2023. The research uses a waterfall approach and is implemented through the Python programming language with the Flask framework. The clustering results produce three groups of consumer behavior based on income levels: low, medium, and high. Clusters are analyzed to support promotional strategies and business decision making. This system was tested and showed that the application of the K-Means method was effective in grouping consumers appropriately and supporting increased sales based on data analysis