<![CDATA[The proliferation of online gambling promotions on social media has created serious social and legal problems in Indonesia, yet existing detection approaches are largely post-hoc, rely on general purpose language models, and struggle to identify implicit and domain specific promotional language that is deliberately used to evade moderation. Moreover, most prior studies focus solely on offline model evaluation and are rarely integrated into real time, deployable systems that can directly protect users. To address these limitations, this study proposes an intelligent detection system utilizing IndoBERT enhanced with Domain Adaptive Pre-Training on a corpus of Indonesian online gambling related text, followed by fine-tuning for text classification. The adapted model demonstrates superior performance, achieving an F1-Score of 98.58% and outperforming baseline approaches including TF-IDF+SVM, BiLSTM, multilingual BERT, and IndoBERT without domain adaptation. To bridge the gap between model development and real-world application, the proposed model is further integrated into an adaptive browser extension capable of scanning, classifying, and filtering social media content in real-time. Functional testing on YouTube and X shows that the system effectively detects and masks online gambling promotional content without disrupting neutral or anti-gambling discourse. This research contributes both methodologically, by demonstrating the effectiveness of domain adaptive pre-training for detecting implicit promotional language, and practically, by delivering a deployable system that provides proactive protection for social media users.]]>
Copyrights © 2025
