Issue

Vol. 9, No. 2, May 2024

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Multi-Label Classification of Indonesian Qur'an Translation using Long Short-Term Memory Model

https://doi.org/10.22219/kinetik.v9i2.1901
Ismail Akbar
Maulana Malik Ibrahim State Islamic University Malang
Muhammad Faisal
Maulana Malik Ibrahim State Islamic University Malang
Totok Chamidy
Maulana Malik Ibrahim State Islamic University Malang

Corresponding Author(s) : Muhammad Faisal

mfaisal@ti.uin-malang.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 9, No. 2, May 2024

Abstract

Studying the Quran is an integral act of worship in Islam, necessitating a nuanced comprehension of its verses to ease learning and referencing. Recognizing the diverse thematic elements within each verse, this research pioneers in applying Deep Learning techniques, specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM), coupled with Word Embedding methods like Word2Vec and FastText, to refine the multi-label classification of the Quran's translations into Indonesian. Targeting core thematic categories such as Tawheed, Worship, Akhlaq, and History, the study aims to elevate classification accuracy, thereby enhancing the textual understanding and educational utility of the Quran's teachings. The employment of Bi-LSTM in conjunction with FastText and meticulous hyperparameter optimization has yielded promising results, achieving an accuracy of 71.63%, precision of 64.06%, recall of 63.60%, and a hamming loss of 36.17%. These outcomes represent a significant advancement in the computational analysis of religious texts, offering novel insights into the complex domain of Quranic studies. Furthermore, the research accentuates the critical role of selecting suitable word embedding techniques and the necessity of precise parameter adjustments to amplify model performance, thereby contributing to the broader field of religious text analysis and understanding. Through such computational approaches, this study not only fosters a deeper appreciation of the Quran's multifaceted teachings but also sets a new precedent for the interdisciplinary integration of Islamic studies and artificial intelligence.

Keywords

LSTM Bi-LSTM Word Embedding Qur'an Translation Classification
Akbar, I., Faisal, M., & Chamidy, T. (2024). Multi-Label Classification of Indonesian Qur’an Translation using Long Short-Term Memory Model. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 9(2), 119-128. https://doi.org/10.22219/kinetik.v9i2.1901
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. S. Wu, Y. Chen, Z. Li, J. Li, F. Zhao, and X. Su, “Towards multi-label classification: Next step of machine learning for microbiome research,” Computational and Structural Biotechnology Journal, vol. 19. Elsevier B.V., pp. 2742–2749, Jan. 01, 2021. https://doi.org/10.1016/j.csbj.2021.04.054
  2. Y. Guo, F. L. Chung, G. Li, and L. Zhang, “Multi-Label Bioinformatics Data Classification with Ensemble Embedded Feature Selection,” IEEE Access, vol. 7, pp. 103863–103875, 2019. https://doi.org/10.1109/ACCESS.2019.2931035
  3. Z. M. Chen, X. S. Wei, P. Wang, and Y. Guo, “Multi-label image recognition with graph convolutional networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Jun. 2019, pp. 5172–5181. https://doi.org/10.1109/CVPR.2019.00532
  4. H. K. Maragheh, F. S. Gharehchopogh, K. Majidzadeh, and A. B. Sangar, “A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification,” Mathematics, vol. 10, no. 3, Feb. 2022. https://doi.org/10.3390/math10030488
  5. G. Liu and J. Guo, “Bidirectional LSTM with attention mechanism and convolutional layer for text classification,” Neurocomputing, vol. 337, pp. 325–338, Apr. 2019. https://doi.org/10.1016/j.neucom.2019.01.078
  6. F. Amalia and M. Arif Bijaksana, “Semantic Model Evaluation Dataset For Indonesian In Al-Qur’an Vocabulary: Similarity And Relatedness,” 2020. https://doi.org/10/25047/jtit.v7i1.137
  7. M. Fauzan, H. Junaedi, and E. Setyati, “KLASIFIKASI AL – QUR’AN TERJEMAHAN BAHASA INDONESIA DENGAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM),” KONVERGENSI, vol. 18, no. 2, pp. 42–49, Dec. 2022. https://doi.org/10.30996/konv.v18i1.6912
  8. M. Robani and A. Widodo, “Algoritma K-Means Clustering Untuk Pengelompokan Ayat Al Quran Pada Terjemahan Bahasa Indonesia,” Jurnal Sistem Informasi Bisnis, vol. 6, no. 2, p. 164, 2016,. https://doi.org/10.21456/vol6iss2pp164-176
  9. M. Irfan, W. Uriawan, N. Lukman, O. Kurahman, and W. Darmalaksana, “The Qur’anic Classification Uses Algorithm C4.5,” 2020. http://dx.doi.org/10.4108/eai.2-10-2018.2295558
  10. A. Hanafi, A. Adiwijaya, and W. Astuti, “Klasifikasi Multi Label pada Hadis Bukhari Terjemahan Bahasa Indonesia Menggunakan Mutual Information dan k-Nearest Neighbor,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 9, no. 3, pp. 357–364, 2020. http://dx.doi.org/10.32736/sisfokom.v9i3.980
  11. T. H. Putrisanni, A. Adiwijaya, and S. Al Faraby, “Klasifikasi Ayat Al-Quran Terjemahan Bahasa Inggris Menggunakan K-Nearest Neighbor (Knn) Dan Information Gain,” KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 3, no. 1, pp. 362–369, 2019. https://doi.org/10.30865/komik.v3i1.1614
  12. A. Abdullahi, N. A. Samsudin, M. H. A. Rahim, S. K. A. Khalid, and R. Efendi, “Multi-label classification approach for Quranic verses labeling,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 24, no. 1, pp. 484–490, 2021. http://doi.org/10.11591/ijeecs.v24.i1.pp484-490
  13. B. A. H. Kholifatullah and A. Prihanto, “Penerapan Metode Long Short Term Memory Untuk Klasifikasi Pada Hate Speech,” Journal of Informatics and Computer Science (JINACS), vol. 04, pp. 292–297, Jan. 2023. https://doi.org/10.26740/jinacs.v4n03.p292-297
  14. D. I. Af’idah, D. Dairoh, S. F. Handayani, and R. W. Pratiwi, “Pengaruh Parameter Word2Vec terhadap Performa Deep Learning pada Klasifikasi Sentimen,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 6, no. 3, pp. 156–161, 2021. https://dx.doi.org/10.30591/jpit.v6i3.3016
  15. R. B. Afrianto and L. Y. Kurniawati, “Kategorisasi Dokumen Teks Secara Multi Label Menggunakan Fuzzy C-Means Dan K-Nearest Neighbors Pada Artikel Berbahasa Indonesia,” JUTI: Jurnal Ilmiah Teknologi Informasi, vol. 11, no. 1, p. 23, 2013. https://dx.doi.org/10.12962/j24068535.v11i1.a17
  16. S. Sayyida, “Ayat-Ayat Tauhid Terhadap Budaya Pemeliharaan Keris Di Jawa (Studi Kasus Buku Mt Arifin),” Journal of Qur’an and Hadith Studies, vol. 6, no. 1, pp. 24–52, 2019. https://doi.org/10.15408/quhas.v6i1.13403
  17. A. Kallang, “Konteks Ibadah Menurut Al-Quran,” Al-Din: Jurnal Dakwah dan Sosial Keagamaan, vol. 4, no. 2, pp. 1–13, 2018. http://dx.doi.org/10.35673/ajdsk.v4i2.630
  18. M. Murharyana, I. I. Al Ayyubi, and R. Rohmatulloh, “Pendidikan Akhlak Anak Kepada Orang Tua Dalam Perspektif Al-Quran,” Piwulang: Jurnal Pendidikan Agama Islam, vol. 5, no. 2, pp. 175–191, 2023. http://dx.doi.org/10.32478/piwulang.v5i2.1515
  19. J. Mirdad and S. Rahmat, “Sejarah Dalam Perspektif Islam,” El -Hekam, vol. 6, no. 1, p. 9, 2021. https://doi.org/10.31958/jeh.v6i1.3303
  20. S. Bessou and R. Aberkane, “Subjective Sentiment Analysis for Arabic Newswire Comments,” Journal of Digital Information Management, vol. 17, no. 5, p. 289, 2019. https://doi.org/10.48550/arXiv.1911.03776
  21. A. A. Firdaus, A. Yudhana, and I. Riadi, “Public Opinion Analysis of Presidential Candidate Using Naïve Bayes Method,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 4, no. 2, 2023. https://doi.org/10.22219/kinetik.v8i2.1686
  22. E. Y. Sari, A. D. Wierfi, and A. Setyanto, “Sentiment Analysis of Customer Satisfaction on Transportation Network Company Using Naive Bayes Classifier,” 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding, vol. 2019-Novem, 2019. https://doi.org/10.1109/CENIM48368.2019.8973262
  23. H. A. Almuzaini and A. M. Azmi, “Impact of Stemming and Word Embedding on Deep Learning-Based Arabic Text Categorization,” IEEE Access, vol. 8, pp. 127913–127928, 2020. https://doi.org/10.1109/ACCESS.2020.3009217
  24. Y. D. Prabowo, T. L. Marselino, and M. Suryawiguna, “Pembentukan Vector Space Model Bahasa Indonesia Menggunakan Metode Word to Vector,” Jurnal Buana Informatika, vol. 10, no. 1, p. 29, 2019. https://doi.org/10.24002/jbi.v10i1.2053
  25. E. H. Mohamed and W. H. El-Behaidy, “An Ensemble Multi-label Themes-Based Classification for Holy Qur’an Verses Using Word2Vec Embedding,” Arabian Journal for Science and Engineering, vol. 46, no. 4, pp. 3519–3529, 2021. https://doi.org/10.1007/s13369-020-05184-0
  26. J. Hermanto, “Klasifikasi Teks Humor Bahasa Indonesia Memanfaatkan SVM,” Journal of Information System,Graphics, Hospitality and Technology, vol. 3, no. 01, pp. 39–48, 2021. https://doi.org/10.37823/insight.v3i01.118
  27. L. Xiao, G. Wang, and Y. Zuo, “Research on Patent Text Classification Based on Word2Vec and LSTM,” Proceedings - 2018 11th International Symposium on Computational Intelligence and Design, ISCID 2018, vol. 1, pp. 71–74, 2018. https://doi.org/10.1109/ISCID.2018.00023
  28. R. P. Hastuti, V. Riona, and M. Hardiyanti, “Content Retrieval dengan Fasttext Word Embedding pada Learning Management System Olimpiade,” Journal of Internet and Software Engineering, vol. 4, no. 1, pp. 18–22, 2023. https://doi.org/10.22146/jise.v4i1.6766
  29. A. Nurdin, B. Anggo Seno Aji, A. Bustamin, and Z. Abidin, “Perbandingan Kinerja Word Embedding Word2Vec, Glove, Dan Fasttext Pada Klasifikasi Teks,” Jurnal Tekno Kompak, vol. 14, no. 2, p. 74, 2020. https://doi.org/10.33365/jtk.v14i2.732
  30. Y. Taher, A. Moussaoui, and F. Moussaoui, “Automatic Fake News Detection based on Deep Learning, FastText and News Title,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 1, pp. 146–158, 2022. https://dx.doi.org/10.14569/IJACSA.2022.0130118
  31. A. Saifudin, “Metode Data Mining Untuk Seleksi Calon Mahasiswa,” vol. 10, no. 1, pp. 25–36, 2018, doi: https://dx.doi.org/10.24853/jurtek.10.1.25-36.
  32. M. Kim and K.-H. Kang, “Comparison of Neural Network Techniques for Text Data Analysis,” International Journal of Advanced Culture Technology, vol. 8, no. 2, pp. 231–238, 2020. https://doi.org/10.17703/IJACT.2020.8.2.231
  33. X. H. Le, H. V. Ho, G. Lee, and S. Jung, “Application of Long Short-Term Memory (LSTM) neural network for flood forecasting,” Water (Switzerland), vol. 11, no. 7, 2019. http://dx.doi.org/10.3390/w11071387
  34. M. T. Vu, A. Jardani, N. Massei, and M. Fournier, “Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network,” Journal of Hydrology, vol. 597, 2021. https://doi.org/10.1016/j.jhydrol.2020.125776
  35. A. M. Ertugrul and P. Karagoz, “Movie Genre Classification from Plot Summaries Using Bidirectional LSTM,” Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018, vol. 2018-Janua, pp. 248–251, 2018. https://doi.org/10.1109/ICSC.2018.00043
  36. A. R. Isnain, A. Sihabuddin, and Y. Suyanto, “Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 14, no. 2, p. 169, 2020. https://doi.org/10.22146/ijccs.51743
  37. A. A. Ningrum, I. Syarif, A. I. Gunawan, E. Satriyanto, and R. Muchtar, “Algoritma Deep Learning-Lstm Untuk Memprediksi Umur Deep Learning-Lstm Algorithm for Power Transformer Lifetime,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 3, pp. 539–548, 2021. https://doi.org/10.25126/jtiik.202184587
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 37 Download : 10

Downloads

Download data is not yet available.