REKOMENDASI PRODUK E-COMMERCE BERBASIS KLASIFIKASI MENGGUNAKAN ALGORITMA MACHINE LEARNING
DOI:
https://doi.org/10.33884/comasiejournal.v13i4.10562Keywords:
naive bayes, product reviews, tokopedia, classificationAbstract
The growth of e-commerce is driving an increase in the number of product reviews, but not
all reviews are informative and relevant. This study aims to build a Tokopedia review
classification system using the Naive Bayes algorithm to filter relevant reviews. The dataset
used totaled 40,607 product review lines. The preprocessing process includes cleaning,
tokenization, stopword removal, and feature transformation using TF-IDF with 5,000 feature
words. Labels are determined based on ratings, where a rating of 4-5 is considered relevant.
The Multinomial Naive Bayes model yielded an accuracy of 93.71%, a precision of 0.94, a
recall of 0.99 for the relevant class, and an F1-score of 0.96. Although performance in
irrelevant classes is still low, this model is effective in supporting product recommendations
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