Komparasi Naive Bayes dan SVM untuk Analisis Sentimen Pada E-Commerce Seller Center - Yanuar Laik, Abraham Adrian; Nabilla, Adinda; Diah, Andi; Sumanto; Indra, Ahmad; Arya, Yudi (CV. Information Technology Training Center - Indonesia (ITTC), 2025)
The development of e-commerce drives the need to understand customer opinions through sentiment analysis to improveservice quality. Tokopedia and TikTok Shop as popular e-commerce platforms provide a review feature that can be asource of data to analyze consumer perceptions. This study aims to compare the performance of two text classificationalgorithms, namely Naive Bayes and Support Vector Machine (SVM), in analyzing the sentiment of customer reviews takenfrom the TikTok Tokopedia Seller Center dataset. The research method used is a computational experiment with aquantitative approach. The dataset used is sourced from the Kaggle site and is available in clean and labeled conditions(positive and negative). Model evaluation is done by measuring accuracy, precision, recall and F1-score. The results showthat Naive Bayes is superior with 97.50% accuracy and 84.00% F1-score, compared to SVM which obtained 94.90%accuracy and 76.80% F1-score. Thus, Naive Bayes is considered more effective for sentiment analysis of e-commercecustomer reviews