Rancang Bangun Model Name Entity Recognition Menggunakan Metode Backpropagation dalam Klasifikasi Berita Hoaks Seputar Vaksin Covid-19 - Khairunnas, Muhammad Fadil; Arhami, Muhammad; Khadafi, M. (Politeknik Negeri Lhokseumawe, 2023)
The flow of news regarding the development of Covid-19 has dominated various information channels in Indonesia in the last 2 years, either through print or digital media. Various types of news related to Covid-19 continue to circulate, including hoax news. One of the most widely circulated hoax news is the news about the Covid-19 vaccine. The rise of information containing hoax news and untrue rumors about the Covid-19 vaccine in the community can worsen the pandemic situation. Currently, there is no intelligent system capable of classifying hoaxes about the Covid-19 vaccine. To maximize the prevention of the spread of hoax news about the Covid-19 vaccine and overcome the problems faced, the researchers designed a classification system for hoax news about the Covid-19 vaccine with a machine learning approach. The system built can classify news with a combination of the Backpropagation Name Entity Recognition (NER) algorithm. Dataset used is 600 Covid-19 vaccine news data obtained from the sites https://turnbackhoax.id/ and https://www.kompas.com/ with the keyword