<![CDATA[Sarcasm detection in Indonesian social media faces challenges in natural language processing due to implicit meanings and limited labeled datasets. YouTube, with 143 million users in Indonesia, represents a largely unexplored source of sarcastic expressions. This study aims to develop an automatic sarcasm detection system for Indonesian YouTube comments using fine-tuned IndoBERT and evaluate the performance of two IndoBERT variants. A dataset of 5,291 YouTube comments was collected and automatically labeled using GPT-4o with structured prompts based on linguistic indicators of sarcasm. Two IndoBERT variants (IndoNLU and IndoLEM) were fine-tuned with three class imbalance mitigation strategies: imbalanced, under-sampling, and class weighting. Zero-shot evaluation was conducted as a baseline to measure fine-tuning effectiveness. Models were evaluated using accuracy, precision, recall, and F1-score metrics. Pre-trained models without fine-tuning showed very limited sarcasm detection capability with F1-scores of 0.1613 for IndoNLU and 0.3519 for IndoLEM. Fine-tuning with under-sampling dramatically improved F1-scores to 0.6499 for IndoNLU and 0.6568 for IndoLEM, showing improvements up to 303%. IndoBERT-IndoNLU provided more balanced performance with 0.6424 accuracy, while IndoLEM showed higher sarcasm recall of 0.7639. Fine-tuning IndoBERT is effective for detecting sarcasm in Indonesian YouTube comments. This study contributes by providing a new labeled dataset, demonstrating the effectiveness of automatic labeling using large language models, and providing empirical evidence of the significant value of fine-tuning for Indonesian sarcasm detection.]]>
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