Classification of Best-Selling Products Based at Noenaasstore Using Naïve Bayes Algorithm - Hidayah, Nurul; Christianto, Paminto Agung; Amalia, Nurul (Politeknik Negeri Lhokseumawe, 2026)
The advancement of information technology has shifted consumer shopping behavior toward e-commerce platforms, with the fashion category occupying the top position. This situation requires MSMEs to identify their best-selling products in order to design more accurate marketing strategies and business decisions. This study applies the Naïve Bayes algorithm to sales transaction data from Noenaasstore, an MSME engaged in women’s fashion, to classify products based on their sales levels. Model evaluation using RapidMiner achieved an accuracy of 92,62%, a weighted mean precision of 83,81%, and a weighted mean recall of 95,51%. These findings indicate that the Naïve Bayes algorithm can effectively categorize products, thereby enabling business owners to formulate promotional strategies and support data-driven decision-making.