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This paper compares PyCaret AutoML with fine-tuned IndoBERT for Indonesian sentiment analysis on IKN Twitter data. Logistic Regression achieved the best performance among classical models (77.57% accuracy), while fine-tuning IndoBERT yielded significantly higher accuracy (89.59%). The study demonstrates the superiority of Transformer-based models for processing informal Indonesian text in social media sentiment analysis.
Fine-tuning IndoBERT on Indonesian Twitter data blows away classical ML baselines in sentiment classification, suggesting transfer learning is a must-try for low-resource NLP.
This paper benchmarks a classical machine learning approach based on PyCaret AutoML against a deep learning approach based on IndoBERT fine-tuning for binary sentiment analysis of Indonesian-language Twitter comments related to Ibu Kota Nusantara (IKN). The dataset contains 1,472 manually labeled samples, consisting of 780 negative and 692 positive comments. In the machine learning setting, Logistic Regression, Naive Bayes, and Support Vector Machine were evaluated using 10-fold cross-validation, with Logistic Regression achieving the best performance among the classical models at 77.57% accuracy and 77.17% F1-score. In the deep learning setting, the indobenchmark/indobert-base-p1 model was fine-tuned for five epochs and achieved 89.59% test accuracy and 89.37% F1-score. The results show that IndoBERT substantially outperforms the machine learning baselines, highlighting the effectiveness of Transformer-based contextual representations for informal Indonesian social media text.
