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All Journal JINAV: Journal of Information and Visualization
Fauzaan Rakan Tama
Telkom Unversity
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Mathematics

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Fake News (Hoaxes) Detection on Twitter Social Media Content through Convolutional Neural Network (CNN) Method Fauzaan Rakan Tama; Yuliant Sibaroni
JINAV: Journal of Information and Visualization Vol. 4 No. 1 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1525

Abstract

The use of social media is very influential for the community. Users can easily post various activities in the form of text, photos, and videos in social media. Information on social media contains fake news and hoaxes that will have an impact on society. One of the most social media used is Twitter. This study aims to detect fake news found on the Tweets using the Convolutional Neural Network (CNN) method by comparing the weighting features used of the Term Frequency Inverse Document Frequency (TF-IDF) and the Term Frequency-Relevance Frequency (TF-RF). The highest accuracy was obtained in the Term Frequency-Relevance Frequency (TF-RF) weighting feature with an accuracy of 84.11%, while in the Term Frequency Inverse Document Frequency (TF-IDF) weighting feature with an accuracy of 80.29%.
Co-Authors Yuliant Sibaroni