Implementasi Naïve Bayes untuk Rekomendasi Pembelian Produk pada Aplikasi E-commerce

Abstrak

Electronic commerce (e-commerce) is a platform that influences buying and selling habits in Indonesia, with data from the Central Statistics Agency 2023 showing 31,753 e-commerce businesses using consumer review data as a determinant of product and service quality. This research aims to develop a sentiment-based product recommendation system using the Naïve Bayes algorithm. The research methodology includes collecting 1,287 data samples obtained from customer reviews using Web Scraper technology on the official MSI Official Store e-commerce platforms in the Tokopedia, Shopee, and Blibli applications. The results of data preprocessing yielded 921 clean data, and the Naïve Bayes Algorithm was applied as a classification model and system implementation in a website application. The data was then divided into 80% for training and 20% for testing. Model evaluation showed an accuracy of 82% for training data and 71% for testing data. These results indicate the effectiveness of the Naïve Bayes algorithm in forming a sentiment-based product recommendation system. This recommendation system helps users make more informed purchasing decisions based on consumer sentiment analysis. This research contributes to the development of intelligent recommendation systems that can improve user decision-making in the digital market