Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter
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...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2023-06-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4830 |
| _version_ | 1827362238574362624 |
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| author | Muhammad Ikram Kaer Sinapoy Yuliant Sibaroni Sri Suryani Prasetyowati |
| author_facet | Muhammad Ikram Kaer Sinapoy Yuliant Sibaroni Sri Suryani Prasetyowati |
| author_sort | Muhammad Ikram Kaer Sinapoy |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-08T07:23:09Z |
| format | Article |
| id | doaj.art-4cc9515aaa274012a376f9f8471e50b4 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T07:23:09Z |
| publishDate | 2023-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-4cc9515aaa274012a376f9f8471e50b42024-02-02T22:44:01ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602023-06-017365766210.29207/resti.v7i3.48304830Comparison of LSTM and IndoBERT Method in Identifying Hoax on TwitterMuhammad Ikram Kaer Sinapoy0Yuliant SibaroniSri Suryani PrasetyowatiTelkom UniversityIn 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.http://jurnal.iaii.or.id/index.php/RESTI/article/view/4830hoax detection, social media, lstm, indobert |
| spellingShingle | Muhammad Ikram Kaer Sinapoy Yuliant Sibaroni Sri Suryani Prasetyowati Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) hoax detection, social media, lstm, indobert |
| title | Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter |
| title_full | Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter |
| title_fullStr | Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter |
| title_full_unstemmed | Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter |
| title_short | Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter |
| title_sort | comparison of lstm and indobert method in identifying hoax on twitter |
| topic | hoax detection, social media, lstm, indobert |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4830 |
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