Perbandingan IndoBERT dan Bi-LSTM Dalam Mendeteksi Pelanggaran Undang-Undang ITE
Keywords:
Bi-LSTM, IndoBERT, UU ITEAbstract
Social media has become a widely used platform in Indonesia, facilitating daily information exchange. However, it also serves as a medium for negative content, including hate speech, cyberbullying, and the promotion of illegal activities such as online gambling. This study aims to develop an automatic classification system to detect ITE Law violations using deep learning approaches. Two models compared are IndoBERT and Bi-LSTM. The dataset used consists of labeled Indonesian-language comments collected from social media and public sources such as Kaggle. The types of ITE violations classified include cyberbullying, hate speech, and online gambling. Experimental results show that both IndoBERT and Bi-LSTM achieved an accuracy of 97%, with IndoBERT performing slightly better in detecting cyberbullying and hate speech. This research is expected to contribute to efforts in automatically preventing ITE Law violations through natural language processing technology.
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