A Comparison of Classical Machine Learning and IndoBERT on Sentiment Analysis of Danantara Program in X
TL;DR
Findings demonstrate that the deep transformer approach is far more robust in extracting semantics from informal Indonesian social media text, with practical implications for public policy decision-making.
Abstract
The rapid growth of social media has made it a primary channel for the public to express opinions on national strategic economic policies, including the establishment of the Danantara entity. This study aims to map public sentiment on Platform X and compare the performance of classical frequency-based architectures with transformer-based models. A common research gap in previous studies is the reliance on Bag-of-Words models, which fail to capture local context and sarcasm in informal text. A total of 9,525 tweets from the period January–May 2025 were collected via crawling and labeled using a hybrid approach combining InSet Lexicon and manual validation by experts (Cohen’s Kappa = 0.81). To address significant class imbalance (66.5% negative), SMOTE was applied to classical models. Experimental results reveal a significant performance gap: the classical TF-IDF + SVM model achieved a positive-class F1-score of only 59% due to feature distortion caused by SMOTE in the TF-IDF space, while the fine-tuned IndoBERT model substantially outperformed it with a global accuracy of 95.80% and a positive-class F1-score of 81%. These findings demonstrate that the deep transformer approach is far more robust in extracting semantics from informal Indonesian social media text, with practical implications for public policy decision-making.