Rancang Bangun Model Name Entity Recognition Menggunakan Metode Backpropagation dalam Klasifikasi Berita Hoaks Seputar Vaksin Covid-19 - Khairunnas, Muhammad Fadil; Arhami, Muhammad; Khadafi, M. (Jurnal Teknologi Rekayasa Informasi dan Komputer, 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 "vaksin covid". Dataset divided into two, training data and test data. The training data is preprocessed and then used in model design. Test data is used to evaluate the results of model design. This process produces a machine learning model with accuracy rate of 97,62%. From these results, the system is able to classify news texts about Covid-19 vaccine. From these results, the system is able to classify news texts about Covid-19 vaccine. Keywords — Backpropagation, Hoax news, NER