Perbandingan Naïve Bayes dan Logistic Regression untuk Analisis Sentimen Ulasan Produk Skincare di Tokopedia

Indra Dikusuma; Yunus Widjaja

doi:10.55606/jitek.v5i3.8420

Authors

Indra Dikusuma Universitas Pembangunan Jaya
Yunus Widjaja Universitas Pembangunan Jaya

DOI:

https://doi.org/10.55606/jitek.v5i3.8420

Keywords:

Naive Bayes, Logistic Regression, Analisis Sentimen, Tokopedia, E-Commerce

Abstract

This study aims to compare the performance of Naïve Bayes (NB) and Logistic Regression (LR) in classifying the sentiment of skincare product reviews on Tokopedia. A total of 973 reviews were collected and processed through text preprocessing and feature extraction using TF-IDF. Evaluation using accuracy, precision, recall, and F1-score showed that NB achieved an accuracy of 75.9% but struggled to detect negative sentiment , while LR reached an accuracy of 92.3% with a more balanced classification. It is concluded that Logistic Regression is superior and more suitable for complex e-commerce review datasets, offering practical guidance for effective model selection

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