  With increasing usage of clickbaits in Indonesian Online News, newsworthy
articles sometimes get buried among clickbaity news. A reliable and lightweight
tool is needed to detect such clickbaits on-the-go. Leveraging state-of-the-art
natural language processing model BERT, a RESTful API based application is
developed. This study offloaded the computing resources needed to train the
model on the cloud server, while the client-side application only needs to send
a request to the API and the cloud server will handle the rest. This study
proposed the design and developed a web-based application to detect clickbait
in Indonesian using IndoBERT as a language model. The application usage is
discussed and available for public use with a performance of mean ROC-AUC of
89%.
