Clickbait Detection for Internet News Title with Deep Learning Feed Forward

Clickbait has been widely circulated on social media and has become one of the ways used to increase reader traffic and website/website visitors, but this clickbait is often misused by website managers in increasing visitor traffic to get an income or profit by ignoring the satisfaction of news readers with how to display a trapping title and hyperbole and the information in the content does not match what is stated in the news title. Today's society is in an emergency for clickbait news, even on national news pages sometimes they still use the title clickbait. In this study, a clickbait news prediction system is proposed on the news circulating. A deep learning neural network method has been proposed, and the architecture we use is flexible feed forward, namely by providing classes with semantic or multiple-meaning languages. Our proposed deep learning architecture on the neural network is able to classify clickbait news with accuracy values of 80%. The purpose of this research is to provide intelligent education to the public to be able to sort out news easily.