Implementing Fine-Tuned IndoBERT for Detecting Potential Indonesian Hoax News

Abstract

The spread of fake information or hoaxes on Indonesian digital platforms has become a serious threat to social stability and public literacy. This study aims to design and implement a web-based application capable of automatically detecting potential Indonesian hoax news. The proposed method utilizes a Natural Language Processing approach by applying fine-tuning techniques to the pre-trained IndoBERT model (IndoBERT Base P1). The model was specifically trained using an Indonesian news text dataset to classify and recognize the linguistic patterns of disinformation. Key findings demonstrate that the fine-tuned IndoBERT model was successfully integrated into the web system architecture, delivering excellent classification performance with accuracy, precision, and F1-score reaching 98.6%. Furthermore, the system is capable of processing input text and displaying the confidence score percentage in real-time to the user. In conclusion, the implementation of fine-tuned IndoBERT in a web application has proven to be an effective and reliable early verification tool. The presence of this system is expected to assist the public in filtering information and contribute to suppressing the spread of hoaxes within the Indonesian digital space.