APPLICATION OF NAIVE BAYES ALGORITHM FOR SALES ANALYSIS AT ERIGO STORE
DOI:
https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5404Abstract
The purpose of this study is to research and compare the accuracy of the previous research algorithm, namely the KNN algorithm with the Naive Bayes algorithm, for the evaluation of Erigo Store sales. Given the increasingly fierce market competition, it is very necessary to formulate a marketing strategy to analyze and predict products using data mining processing methods. Data mining is the introduction of patterns, machine learning techniques, statistics, and visualization techniques that aim to provide information to make better decisions and improve prediction accuracy through the process of analyzing data based on the Knowledge Discovery in Database (KDD) procedure. The research dataset was taken from shopee Toko Erigo e-commerce sales data using web scraping techniques, starting from January 2021 to June 2023 consisting of 5 categories of Erigo Store products, namely Shirts, T-Shirt, Outwear, Jacket and Pants. The overall accuracy of the previous research product using the KNN algorithm was 83.62% while the study using the application of the Naive Bayes algorithm for sales analysis in Erigo stores achieved an accuracy of 98.3% by using Matlab to analyze the data. The accuracy of the T-shirt category reached 98.6%, the shirt category reached 98.4%, the pants category reached 98.1%, the outwear category reached 98.7% and the accuracy of the jacket category reached 97.6%.
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Copyright (c) 2024 Maria Natalenta Sitanggang, Rivandu Ambarita, Cantika Marpaung, Delima Sitanggang
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