HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL
Nowadays, the internet and social media grow fast. This condition has positive and negative effects on society. They become media to communicate and share information without limitation. However, many people use that easiness to broadcast news or information which do not accurate with the facts and...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Mercu Buana
2020-07-01
|
| Series: | Jurnal Ilmiah SINERGI |
| Subjects: | |
| Online Access: | https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/7201 |
| _version_ | 1797709989663473664 |
|---|---|
| author | Andi Apriliyanto Retno Kusumaningrum |
| author_facet | Andi Apriliyanto Retno Kusumaningrum |
| author_sort | Andi Apriliyanto |
| collection | DOAJ |
| description | Nowadays, the internet and social media grow fast. This condition has positive and negative effects on society. They become media to communicate and share information without limitation. However, many people use that easiness to broadcast news or information which do not accurate with the facts and gather people's opinions to get benefits or we called a hoax. Therefore, we need to develop a system that can detect hoax. This research uses the neural network method with Long Short-Term Memory (LSTM) model. The process of the LSTM model to identify hoax has several steps, including dataset collection, pre-processing data, word embedding using pre-trained Word2Vec, built the LSTM model. Detection model performance measurement using precision, recall, and f1-measure matrix. This research results the highest average score of precision is 0.819, recall is 0.809, and f1-measure is 0.807. These results obtained from the combination of the following parameters, i.e., Skip-gram Word2Vec Model Architecture, Hierarchical Softmax, 100 as vector dimension, max pooling, 0.5 as dropout value, and 0.001 of learning rate. |
| first_indexed | 2024-03-12T06:45:43Z |
| format | Article |
| id | doaj.art-f345dce7137146509d823e1152f0e632 |
| institution | Directory Open Access Journal |
| issn | 1410-2331 2460-1217 |
| language | English |
| last_indexed | 2024-03-12T06:45:43Z |
| publishDate | 2020-07-01 |
| publisher | Universitas Mercu Buana |
| record_format | Article |
| series | Jurnal Ilmiah SINERGI |
| spelling | doaj.art-f345dce7137146509d823e1152f0e6322023-09-03T00:39:36ZengUniversitas Mercu BuanaJurnal Ilmiah SINERGI1410-23312460-12172020-07-0124318919610.22441/sinergi.2020.3.0033498HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODELAndi Apriliyanto0Retno Kusumaningrum1Department of Informatics, Universitas DiponegoroDepartment of Informatics, Universitas DiponegoroNowadays, the internet and social media grow fast. This condition has positive and negative effects on society. They become media to communicate and share information without limitation. However, many people use that easiness to broadcast news or information which do not accurate with the facts and gather people's opinions to get benefits or we called a hoax. Therefore, we need to develop a system that can detect hoax. This research uses the neural network method with Long Short-Term Memory (LSTM) model. The process of the LSTM model to identify hoax has several steps, including dataset collection, pre-processing data, word embedding using pre-trained Word2Vec, built the LSTM model. Detection model performance measurement using precision, recall, and f1-measure matrix. This research results the highest average score of precision is 0.819, recall is 0.809, and f1-measure is 0.807. These results obtained from the combination of the following parameters, i.e., Skip-gram Word2Vec Model Architecture, Hierarchical Softmax, 100 as vector dimension, max pooling, 0.5 as dropout value, and 0.001 of learning rate.https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/7201hoax detectionneural networklong short-term memoryword2vec |
| spellingShingle | Andi Apriliyanto Retno Kusumaningrum HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL Jurnal Ilmiah SINERGI hoax detection neural network long short-term memory word2vec |
| title | HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL |
| title_full | HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL |
| title_fullStr | HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL |
| title_full_unstemmed | HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL |
| title_short | HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL |
| title_sort | hoax detection in indonesia language using long short term memory model |
| topic | hoax detection neural network long short-term memory word2vec |
| url | https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/7201 |
| work_keys_str_mv | AT andiapriliyanto hoaxdetectioninindonesialanguageusinglongshorttermmemorymodel AT retnokusumaningrum hoaxdetectioninindonesialanguageusinglongshorttermmemorymodel |