Classification of Al-Qur'an Arabic Verses Used Naive Bayes
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Published:
Jun 1, 2022
Keywords:
Al-Quran, Classification, Naïve Bayes, Text Mining
Section
Computer Science
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Abstract
The development of information globally is very fast, it demands the provision of media information can be enjoyed and felt quickly and precisely. Al-Qur'an as a medium of information in the classical form and contains of science, life, etc. that must be distributed to all human beings orally, in writing, and daily behavior, especially in the field of Islamic laws and aspects of social procedures in Islam. This study applies a classification technique using the method Naïve Bayes which there are three classes or categories namely Sholat (Prayer), Hajj and Wedding. The grouping of these verses takes data from the book of LubaabutTafsir Min Ibn Kathir which later obtained accuracy results 75.52%.
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How to Cite
Hidayatullah, S. (2022) “Classification of Al-Qur’an Arabic Verses Used Naive Bayes”, Jurnal Mantik, 6(1), pp. 717-725. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/2356 (Accessed: 22May2026).
References
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[15] M. El koudi, A. Bensaid and T.-e. Rachidi, "Automatic Arabic Document Categorization Based on the Naïve Bayes Algorithm," Proceedings of COLING 20th Workshop on Computational Approaches to Arabic Script-based Language, pp. 51-58, 2004.
[16] M. Bilal, H. Israr, M. Shahid and A. Khan, "Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques," Journal of King Saud University - Computer and Information Sciences, 2015.
[17] M. Q. Shihab, Membumikan Al-Quran, Bandung: Mizan, 1996, p. 1.
[18] R. Al-Shalabi, G. Kanaan and M. H. Gharaibeh, "Arabic text categorization using KNN," computer science and information, 2006.
[2] A. Nizhan, Buku Pintar Al-Qur'an, Jakarta Selatan: Qultum Media, 2008, p. 10.
[3] A. M. El-Halees, "Arabic Text Classification Using Maximum Entropy," The Islamic University Journal (Series of Natural Studies and Engineering), vol. 15, no. 1, pp. 157-167, 2007.
[4] A. M. El-Halees, "Mining Arabic Association Rules for Text Classification," 2006.
[5] A. Al-Zoghby, A. . S. Eldin, N. A. Ismail and T. Hamza, "Mining Arabic Text Using Soft Matching," Computer Engineering and Systems, pp. 421-426, 2007.
[6] A. Hamzah, "Klasifikasi teks dengan naïve bayes classifier (nbc) untuk pengelompokan teks berita dan abstract akademis," Prosiding Seminar Nasional Aplikasi Sains & Teknologi (SNAST) Periode III, no. 2011, pp. 269-277, 2012.
[7] A. S. Altheneyan and M. E. B. Menai, "Naïve Bayes classifiers for authorship attribution of Arabic texts," Journal of King Saud University - Computer and Information Sciences, vol. 26, no. 4, pp. 473-484, 2014.
[8] B. Ramesh and J. G. R. Sathiaseelan, "An Advanced Multi Class Instance Selection based Support Vector Machine for Text Classification," Procedia Computer Science, vol. 57, pp. 1124-1130, 2015.
[9] C. Darujati and A. . B. Gumelar, "Pemanfaatan Teknik Supervised Untuk Klasifikasi Teks Bahasa Indonesia," Link, vol. 16, no. 1, pp. 1-8, 2012.
[10] F. Harrag, E. El-Qawasmeh and P. Pichappan, "Improving Arabic Text Categorization using Decision Trees," p. 110 – 115, 2009.
[11] H. Faozi and E. El-Qawasmah, "Neural Network for Arabic text classification," 2009 Second International Conference on the Applications of Digital Information and Web Technologies, pp. 778-783, 2009.
[12] H. Sawaf, J. Zaplo and H. Ney, "Statistical Classification Methods for Arabic News Articles," Natural Language Processing in ACL2001, Toulouse, p. 6, 2001.
[13] L. Khreisat, "A machine learning approach for Arabic text classification using N-gram frequency statistics," Journal of Informetrics, vol. 3, no. 1, pp. 72-77, 2009.
[14] L. Khreisat, "A machine learning approach for Arabic text classification using N-gram," Journal of Informetrics, vol. 3, no. 1, pp. 72-77, 2009.
[15] M. El koudi, A. Bensaid and T.-e. Rachidi, "Automatic Arabic Document Categorization Based on the Naïve Bayes Algorithm," Proceedings of COLING 20th Workshop on Computational Approaches to Arabic Script-based Language, pp. 51-58, 2004.
[16] M. Bilal, H. Israr, M. Shahid and A. Khan, "Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques," Journal of King Saud University - Computer and Information Sciences, 2015.
[17] M. Q. Shihab, Membumikan Al-Quran, Bandung: Mizan, 1996, p. 1.
[18] R. Al-Shalabi, G. Kanaan and M. H. Gharaibeh, "Arabic text categorization using KNN," computer science and information, 2006.
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