Klasifikasi Judul Berita Clickbait menggunakan RNN-LSTM - Afandi, Widi; Saputro, Satria Nur; Kusumaningrum, Andini Mulia; Adriansyah, Hikari; Kafabi, Muhammad Hilmi; Sudianto, Sudianto (Jurnal Informatika: Jurnal Pengembangan IT, 2022)
Amid technological developments, online news of various life topics is shared across various platforms. Many media often take advantage of this opportunity by uploading their news on several online platforms to increase the traffic and rankings they upload to make much profit. However, many online media attract readers' attention by exaggerating the headlines or news headlines they upload. That way, the news title is often not by the content of the news. This phenomenon is commonly known as "clickbait" among the public. The media usually do this to increase traffic, rankings, and finances. Therefore, this study classified the news with clickbait and non-clickbait titles using the RNN-LSTM architecture. In this study, the classification of clickbait news titles uses the RNN-LSTM architecture. The classification results obtained calculation accuracy of 79% on training data and 77% accuracy on test data.