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Journal : Journal Of Artificial Intelligence And Software Engineering

Classification of Best-Selling Products Based at Noenaasstore Using Naïve Bayes Algorithm Hidayah, Nurul; Christianto, Paminto Agung; Amalia, Nurul
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.7701

Abstract

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.
Product Sales Analysis based on sales level using the K-Means Clustering method Kinasih, Aisha Bethary; Christianto, Paminto Agung; Amalia, Nurul
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.7710

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a highly strategic role in driving Indonesia’s economic growth. Nevertheless, most business actors have not yet utilized digital technology to its full potential. One such example is Toko Nabila Daster, which recorded 475 sales transactions during the period of January–June 2025, but has not conducted an analysis to identify products with high, medium, or low sales levels. This situation may result in stock accumulation and ineffective promotional strategies. The objective of this study is to group products based on their sales levels using the K-Means Clustering method. The optimal number of clusters is determined through the Elbow Method, while the quality of clustering is assessed using the Davies-Bouldin Index (DBI). The results of the analysis indicate the formation of product clusters that distinguish best-selling, moderately selling, and low-selling categories. These findings are expected to serve as a foundation for business decision-making, particularly in designing promotional strategies and managing inventory more efficiently.