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  1. Home
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  3. Vol. 9, No. 2, May 2024
  4. Articles

Issue

Vol. 9, No. 2, May 2024

Issue Published : May 31, 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
Article Published : May 27, 2024

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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
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References
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Read More

References


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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Address

Program Studi Elektro dan Informatika

Fakultas Teknik, Universitas Muhammadiyah Malang

Jl. Raya Tlogomas 246 Malang

Phone 0341-464318 EXT 247

Contact Info

Principal Contact

Amrul Faruq
Phone: +62 812-9398-6539
Email: faruq@umm.ac.id

Support Contact

Fauzi Dwi Setiawan Sumadi
Phone: +62 815-1145-6946
Email: fauzisumadi@umm.ac.id

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