Klasifikasi dalam Pembuatan Portal Berita Online dengan Menggunakan Metode BERT - Jehezkiel Hardwin Tandijaya; Liliana Liliana; Indar Sugiarto (Jurnal Infra, 2021)
Internet helps human by making various information from many online news platform accessible. But nowadays, there are a lot of news that can be accessed in different online news platform and needs to be categorized. The news that can be accessed in some of the sources don’t have high credibility about an event, because the publishers use false and misleading information to push their agendas. So in order to check the credibility of an event, it is needed to also read from other sources and not only from 1 source. However, this is not effective because the reader has to look for another news source with different URL address. In this research scraping will be done to retrieve the news that are available in a news platform. After the scraping process is done, the news will be classified to determine the category of the news. The method that will be used is Bidirectional Encoder Representations from Transformers. From the testing of this research, the news can be retrieved and classified. The testing with a pre-trained model indobenchmark /indobert-base-p1 get a very good result where the accuracy reaches 87.548%.