Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter

• Published in: Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (Online) Vol. 7; no. 3; pp. 657 - 662
• Main Authors: Muhammad Ikram Kaer Sinapoy, Yuliant Sibaroni, Sri Suryani Prasetyowati
• Format: Journal Article
• Language: English
• Published: Ikatan Ahli Informatika Indonesia 01.06.2023
• Subjects: hoax detection, social media, lstm, indobert
• ISSN: 2580-0760; 2580-0760
• DOI: 10.29207/resti.v7i3.4830

In recent years, social media users have been increasing significantly, in January 2022 social media users in Indonesia reached 191 million people which has an increase of 12.35% from the previous year as many as 170 million people, With this massive increase every year, more and more people tend to seek and consume information through social media. Despite the many advantages provided by social media, However, the quality of information on social media is lower than in traditional news media there is a lot of hoax information spreading. With many disadvantages felt by hoax information, it has led to many research to detect hoax information on social media, especially information that is widely spread on Twitter. There are several previous researches that use various models using machine learning and also using deep learning to detect hoax. deep learning is very well used to perform several text classification tasks, especially in detecting hoax. The aim of this paper is to compare the LSTM and IndoBERT methods in detecting hoax using datasets taken from Twitter. In this study, two experiments work are conducted, LSTM and IndoBERT methods. The experimental results is average value obtained from experiments using 10-fold cross-validation. The IndoBERT model shows good performance with an average accuracy value of 92.07%, and the LSTM model provides an average accuracy value of 87.54%. The IndoBERT model can show good performance in hoax detection tasks and is shown to outperform the LSTM model which can provide the best average accuracy results in this study.