Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText
Studying the Qur'an is a pivotal act of worship in Islam, which necessitates a structured understanding of its verses to facilitate learning and referencing. Reflecting this complexity, each Quranic verse is rich with unique thematic elements and can be classified into a range of distinct categ...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | Indonesian |
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Universitas Dian Nuswantoro
2024-02-01
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| Series: | Techno.Com |
| Subjects: | |
| Online Access: | https://publikasi.dinus.ac.id/index.php/technoc/article/view/9925 |
| _version_ | 1797301346275164160 |
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| author | Ahmad Rofiqul Muslikh Ismail Akbar De Rosal Ignatius Moses Setiadi Hussain Md Mehedul Islam |
| author_facet | Ahmad Rofiqul Muslikh Ismail Akbar De Rosal Ignatius Moses Setiadi Hussain Md Mehedul Islam |
| author_sort | Ahmad Rofiqul Muslikh |
| collection | DOAJ |
| description | Studying the Qur'an is a pivotal act of worship in Islam, which necessitates a structured understanding of its verses to facilitate learning and referencing. Reflecting this complexity, each Quranic verse is rich with unique thematic elements and can be classified into a range of distinct categories. This study explores the enhancement of a multi-label classification model through the integration of FastText. Employing a CNN+Bi-LSTM architecture, the research undertakes the classification of Quranic translations across categories such as Tauhid, Ibadah, Akhlak, and Sejarah. Based on model evaluation using F1-Score, it shows significant differences between the CNN+Bi-LSTM model without FastText, with the highest result being 68.70% in the 80:20 testing configuration. Conversely, the CNN+Bi-LSTM+FastText model, combining embedding size and epoch parameters, achieves a result of 73.30% with an embedding size of 200, epoch of 100, and a 90:10 testing configuration. These findings underscore the significant impact of FastText on model optimization, with an enhancement margin of 4.6% over the base model. |
| first_indexed | 2024-03-07T23:20:12Z |
| format | Article |
| id | doaj.art-a9cbbffbe3b44752a0c594b816fb54cf |
| institution | Directory Open Access Journal |
| issn | 2356-2579 |
| language | Indonesian |
| last_indexed | 2024-03-07T23:20:12Z |
| publishDate | 2024-02-01 |
| publisher | Universitas Dian Nuswantoro |
| record_format | Article |
| series | Techno.Com |
| spelling | doaj.art-a9cbbffbe3b44752a0c594b816fb54cf2024-02-21T06:28:16ZindUniversitas Dian NuswantoroTechno.Com2356-25792024-02-01231375010.62411/tc.v23i1.99253619Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastTextAhmad Rofiqul Muslikh0Ismail Akbar1De Rosal Ignatius Moses Setiadi2Hussain Md Mehedul IslamUniversity of Merdeka MalangUniversity of Merdeka MalangUniversitas Dian NuswantoroStudying the Qur'an is a pivotal act of worship in Islam, which necessitates a structured understanding of its verses to facilitate learning and referencing. Reflecting this complexity, each Quranic verse is rich with unique thematic elements and can be classified into a range of distinct categories. This study explores the enhancement of a multi-label classification model through the integration of FastText. Employing a CNN+Bi-LSTM architecture, the research undertakes the classification of Quranic translations across categories such as Tauhid, Ibadah, Akhlak, and Sejarah. Based on model evaluation using F1-Score, it shows significant differences between the CNN+Bi-LSTM model without FastText, with the highest result being 68.70% in the 80:20 testing configuration. Conversely, the CNN+Bi-LSTM+FastText model, combining embedding size and epoch parameters, achieves a result of 73.30% with an embedding size of 200, epoch of 100, and a 90:10 testing configuration. These findings underscore the significant impact of FastText on model optimization, with an enhancement margin of 4.6% over the base model.https://publikasi.dinus.ac.id/index.php/technoc/article/view/9925bi-lstmcnnfasttextmulti-label text classificationquran translation |
| spellingShingle | Ahmad Rofiqul Muslikh Ismail Akbar De Rosal Ignatius Moses Setiadi Hussain Md Mehedul Islam Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText Techno.Com bi-lstm cnn fasttext multi-label text classification quran translation |
| title | Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText |
| title_full | Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText |
| title_fullStr | Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText |
| title_full_unstemmed | Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText |
| title_short | Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText |
| title_sort | multi label classification of indonesian al quran translation based cnn bilstm and fasttext |
| topic | bi-lstm cnn fasttext multi-label text classification quran translation |
| url | https://publikasi.dinus.ac.id/index.php/technoc/article/view/9925 |
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