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All Journal Jurnal Informatika: Jurnal Pengembangan IT
Kafabi, Muhammad Hilmi
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Computer Science & IT

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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 Vol 7, No 2 (2022)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i2.3401

Abstract

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.
Co-Authors Adriansyah, Hikari Afandi, Widi Kusumaningrum, Andini Mulia Saputro, Satria Nur Sudianto, Sudianto