<![CDATA[Cyberbullying on social media is difficult to detect automatically because comments may contain informal language, sarcasm, implicit body shaming, and meanings that depend on context. This study evaluates the ability of IndoBERT to detect cyberbullying in Indonesian-language Instagram comments and analyzes the model’s classification errors. The dataset consists of 1,050 comments collected from public Instagram posts and manually labeled into two classes: cyberbullying and non-cyberbullying. The IndoBERT-base-uncased model was fine-tuned and evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The experimental results show an accuracy of 84.76% and an F1-score of 0.8462. For the cyberbullying class, precision reached 0.9294, while recall was 0.7524, indicating that a portion of bullying comments were still missed as false negatives. Error analysis shows that difficult cases were mainly associated with sarcasm, implicit body shaming, informal language, and ambiguous comments. The contributions of this study are an empirical evaluation of IndoBERT on Indonesian Instagram comments, a clear identification of the research gap between conventional machine-learning approaches and the need for contextual Indonesian-language modeling, and an error analysis that provides practical evidence for improving AI-based content moderation. The findings indicate that IndoBERT has promising performance, but larger datasets, conversational context, and pragmatic language modeling are still required.]]>
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