Comparing SVM, Random Forest, Logistic Regression, and Decision Tree for TikTok Sentiment Analysis of Koperasi Desa Merah Putih
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Abstract
The Koperasi Desa Merah Putih program is a strategic Indonesian government initiative designed to strengthen village economies through integrated cooperative services. This study analyzes sentiment expressed in Indonesian-language TikTok comments and compares Support Vector Machine, Random Forest, Logistic Regression, and Decision Tree under identical experimental conditions. The dataset comprised 49,805 labeled comments obtained from a public Kaggle repository. Data preparation included quality screening, case folding, slang normalization, tokenization, stop-word removal while retaining negation terms, and Indonesian stemming. The processed comments were represented using Term Frequency Inverse Document Frequency features and divided through a stratified 80:20 hold-out scheme, producing 39,844 training instances and 9,961 testing instances. Because Neutral comments represented 72.89% of the test set, evaluation emphasized macro-averaged metrics alongside accuracy. Support Vector Machine achieved the best performance, with 93.35% accuracy, 91.32% macro precision, 84.78% macro recall, and 87.70% macro F1-score, providing the most balanced overall classification performance among the evaluated models.
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