SALES PREDICTION AT PT. WORLD INFINITE NETWORK USING NAÏVE BAYES AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM METHODS
DOI:
https://doi.org/10.31937/ti.v17i2.4472Abstract
In the process of analyzing sales transaction data at PT. World Infinite Network, existing information has not yet optimized the sales of offered products. The purpose of this optimization is to obtain purchasing patterns of frequently bought items by customers. Every year, IT products are increasingly needed, even showing growing demand. One data processing technique that can help is data mining. Based on this informational relationship, decisions can be made through processes such as description, estimation, prediction, classification, clustering, and association. Previous studies indicate that the Apriori method is more intuitive and interpretable, while the Naïve Bayes method provides fast, simple, and precise computation, making it one of the most widely used techniques in classification tasks. This study employs both Adaptive neuro fuzzy inference system and Naïve Bayes algorithms to analyze sales data and predict trends. The results show that the Naïve Bayes Algorithm achieved an accuracy of 19.05%, demonstrating its potential application in supporting strategic sales predictions for PT. World Infinite Network.
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