Implementasi Deteksi Judul Berita Clickbait Berbahasa Indonesia dengan pre-trained model Multilingual BERT Pada Aplikasi Berbasis Chrome Extension - Girinoto Girinoto; Dhana Arvina Alwan; Gusti Agung Ngurah Gde K.T. D; Olga Geby Nabila; Arizal arizal; Dimas Febriyan Priambodo (Jurnal Ilmiah SINUS, 2022)
Clickbait news title is often used by online news portal. The purpose of clickbait is to attract reader to open and read the news. Furthermore, news containing clickbait title can give negative impact by reducing the essence of important news. Therefore, clickbait detection tool is needed to avoid the clickbait news title. Chrome extension was chosen in this study because it supports all Chrome based browsers, such as Google Chrome, Chromium, Microsoft Edge, and Opera so that many users apply this program. In this study, Chrome extension-based application was designed and integrated by using artificial intelligence model. This application also utilized the availability of pre-trained multilingual BERT model as Natural Language Processing (NLP) which will be used to predict a clickbait news title. This study used Multilingual BERT model as NLP because this model has been trained into 104 languages, including Bahasa Indonesia and it has significant performance. The result of this study can detect clickbait news along with 92% of AUC-ROC value.