<![CDATA[The rapid expansion of the Indonesian animation industry has sparked vibrant public discourse on social media, yet existing research primarily focuses on binary or overall sentiment rather than fine-grained aspect-level evaluations. This study presents an aspect-based sentiment analysis (ABSA) comparing a traditional machine learning baseline (Random Forest with TF-IDF and SMOTE) against a deep learning model (fine-tuned IndoBERT) on 2,759 tweets discussing domestic animated films Jumbo and Merah Putih: One For All (MPOA). An 11-class joint aspect-sentiment classification scheme was established using a semi-automated annotation pipeline, achieving almost perfect inter-annotator agreement (Cohen’s Kappa κ = 0.8616). Experimental results demonstrate that fine-tuned IndoBERT significantly outperformed the Random Forest baseline, increasing accuracy from 40.16% to 51.64% (+11.48 percentage points) and weighted F1-score from 36.60% to 45.69% (+24.8% relative gain). Corpus-wide aspect distribution revealed that general film evaluation dominates social media discussion (95.9%), followed by animation quality (1.9%) and storyline (1.6%). Furthermore, sentiment analysis uncovered contrasting reception patterns: Jumbo attained overwhelmingly positive net sentiment (+63.8%), whereas MPOA faced severe negative backlash (−46.2%) driven by technical animation critiques and public controversies. Despite overall performance constraints caused by severe class imbalance and limited training data, these findings confirm the superiority of pre-trained Transformer architectures for complex multi-class ABSA tasks in low-resource Indonesian social media text.]]>
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