Performance Analysis of IndoBERT for Detection of Online Gambling Promotion in YouTube Comments
Kamdan Kamdan, Malik Pajar Anugrah, Moh Jeli Almutaali, Restu Ramdani, Ivana Lucia Kharisma · 2025
The proliferation of online gambling promotions on social media platforms, particularly YouTube, poses a significant challenge in digital security and regulation. This study evaluates the performance of IndoBERT in detecting online gambling-related spam in YouTube comments. The research utilizes YouTube Data API to collect comments, preprocess the text through cleaning and tokenization, and fine-tune IndoBERT for classification. The model’s performance is assessed using accuracy, precision, recall, and F1-score metrics. IndoBERT achieves outstanding results with an accuracy of 98.26%, proving its effectiveness in detecting online gambling promotion. The confusion matrix analysis highlights a low error rate, with minimal false positives and false negatives. IndoBERT is a promising tool for combating online gambling spam, offering high reliability for automated content moderation. Future improvements should focus on handling implicit promotional language, enhancing dataset diversity, and integrating rule-based filtering. This study contributes to NLP advancements in Indonesian text classification, supporting efforts to maintain a safer digital environment.