BENCHMARKING MACHINE LEARNING MODELS FOR INDONESIAN E-COMMERCE SENTIMENT CLASSIFICATION

Abstract

Indonesia's expanding e-commerce sector generates a growing volume of customer-written product reviews that can reveal both satisfaction and dissatisfaction. Automatically determining sentiment in these reviews is nevertheless difficult because marketplace language commonly includes informal wording, inconsistent spelling, brief statements, and domain-specific terms. This research benchmarks conventional machine learning methods for classifying the sentiment of Indonesian e-commerce reviews in the PRDECT-ID dataset. The data were obtained from Tokopedia and contain sentiment and emotion annotations. Following preprocessing, the experiment used 5,305 reviews, comprising 2,752 negative and 2,553 positive instances. The processing pipeline included case folding, text cleaning, normalization, tokenization, selective removal of stopwords, and Term Frequency-Inverse Document Frequency (TF-IDF) feature construction. Multinomial Naive Bayes, Support Vector Machine, and Random Forest were then evaluated under the same experimental configuration. The TF-IDF and Support Vector Machine combination produced the strongest results, reaching 0.9595 accuracy, 0.9594 macro-F1, and 0.9595 weighted-F1. These findings establish a reproducible reference point for sentiment classification in Indonesian e-commerce reviews.