Implementasi Deteksi Judul Berita Clickbait Berbahasa Indonesia dengan pre-trained model Multilingual BERT Pada Aplikasi Berbasis Chrome Extension

Girinoto Girinoto (Politeknik Siber dan Sandi Negara)
Dhana Arvina Alwan (Politeknik Siber dan andi Negara)
Gusti Agung Ngurah Gde K.T. D (Politeknik Siber dan Sandi Negara)
Olga Geby Nabila
Arizal arizal (Politeknik Siber dan Sandi Negara)
Dimas Febriyan Priambodo (Politeknik Siber dan Sandi Negara)

Jurnal Ilmiah Sinus
Vol 20, No 2 (2022): Vol. 20 No. 2 Juli 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.