September 5, 2025Indonesian Journal of Computer ScienceOpen Access

Machine Learning and Transformer-based Model for Sentiment Analysis of Indonesian E-Commerce Reviews

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IndoBERT achieved a classification accuracy of 94.1%, outperforming SVM's 89.5% and Multinomial Naïve Bayes's 84.2%.
The study utilized a public dataset comprised of Indonesian e-commerce reviews for evaluation.
Machine learning methods involved text pre-processing and feature extraction using TF-IDF to prepare the data.
IndoBERT showed higher effectiveness despite greater computational demands for sentiment analysis.

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Implication

Analysis reveals the IndoBERT model significantly enhances sentiment classification accuracy in e-commerce reviews, outperforming SVM and Naïve Bayes.

Cite This Study

Widyananda et al. (2025) studied this question.

synapsesocial.com/papers/68c23966b210217d6477bae8 https://doi.org/10.33022/ijcs.v14i4.4980

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Implication

Analysis reveals the IndoBERT model significantly enhances sentiment classification accuracy in e-commerce reviews, outperforming SVM and Naïve Bayes.

Machine Learning and Transformer-based Model for Sentiment Analysis of Indonesian E-Commerce Reviews

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Implication

Cite This Study

Machine Learning and Transformer-based Model for Sentiment Analysis of Indonesian E-Commerce Reviews

Key Points

Discussion

Implication

Cite This Study

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