Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter
Dublin Core
Title
Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter
Subject
hoax detection; social media; twitter; deep learning; LSTM; IndoBERT
Description
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
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
Creator
Muhammad Ikram Kaer Sinapoy, Yuliant Sibaroni, Sri Suryani Prasetyowati
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
June 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Muhammad Ikram Kaer Sinapoy, Yuliant Sibaroni, Sri Suryani Prasetyowati, “Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10003.