Klasifikasi Wazan pada Kata-Kata Al Qur’an Menggunakan Natural Language Processing
DOI:
https://doi.org/10.37802/joti.v3i2.224Keywords:
Al Qur’an, Morfologi, Wazan, NLPAbstract
Pengolahan bahasa Arab merupakan pengembangan teknik yang dapat digunakan untuk menganalisis bahasa Arab dalam konteks tertulis dan lisan. Natural Language Processing (NLP) memberikan kontribusi terhadap banyak sistem yang dikembangkan. Saat ini NLP telah dikembangkan dengan menggunakan teknik Machine Learning (ML). Algoritma ML banyak digunakan dalam NLP karena akurasinya yang tinggi. Penelitian ini membahas review penelitian pada kajian morfologi dalam Al Qur’an serta hubungannya dengan penerapan bidang komputasi sekarang, Natural Language Processing (NLP), klasifikasi wazan menggunakan NLP dengan beberapa tahapannya, termasuk pre-processing dan ekstraksi fitur. Penelitian ini menguji pola pemrosesan klasifikasi wazan menggunakan NLP dengan tahapan proses tokenization dan Term Frequency Inverse Document Frequency (TD-IDF). Hasil evaluasi model menghasilkan angka “1” untuk nilai precision, recall, F1-score, dan akurasi. Hal ini mengartikan bahwa program mampu mengklasifikasi secara tepat kata dalam pola wazan يَفْعُلُ dari pengujian sebanyak 30 data.
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References
B. Rahima, Z. Samir and M. Farhi, "Extracting semantic relations from the Quranic Arabic based on Arabic conjunctive patterns,Journal of King Saud University, vol. 30, no. Computer and Information Sciences, p. 382–390, 2018.
J. Dror, D. Shaharabani, R. Talmon and S. Wintner, "Morphological Analysis of the Qur'an," Literary and Linguistic Computing, vol. 19, pp. 431-452., 2004.
E. Atwell, K. Dukes, A. Sharaf and N. Habash, "Understanding the Quran: A new Grand Challenge for Computer Science and Artificial Intelligence," Edinburgh, 2010.
S. Rahmath and K. Abdullah, "Quranic Computation A Review of research and application," in Quranic Computation A Review of research and application RahIEEE Xplore: International Conference on Advances in Information Technology for the Holy Quran and Its Sciences, Taibah, 2013.
Y. Salem, "A Generic Framework for Arabic to English Machine Translation of Simplex Sentences Using the Role and Reference Grammar Linguistic Model and Engineering," School of Informatics at the Institute of Technology Blanchardstown, Blanchardstown, 2009.
K. Dukes, "Computational Analysis of the Quran through Traditional Arabic Linguistics," 2011.
A.-K. H.S, M. Al-Yahya, A. Bahanshal and I. Al-Odah, "“SemQ: A Proposed Framework for Representing Semantic Opposition in the Holy Quran using Semantic Web Technologies," in CTIT-2009, Dubai, 2009.
D. K and T. Buckwalter, "A Dependency Treebank of the Quran using Traditional Arabic Grammar," in INFOS 2010, Cairo, 2010.
M. Shoaib, M. Yasin, K. H. Ullah and M. M.I. Saeed, "Relational WordNet model for semantic search in Holy Quran," in 2009 International Conference on Emerging Technologies, Islamabad, Pakistan, 2009.
A. Farghaly, "Arabic Natural Language Processing: Challenges and Solutions," ACMTransactions on Asian Language Information Processing, vol. 8, no. 12, 2009.
B. Yassine, D. Mona and R. Paolo, "Arabic Named Entity Recognition: An Svm-Based Approach," In First Arab International Conference and Exhibition on The Uses of White Cement, Cairo, 2008.
M. D. Rehab and Q. Islam, "Arabic Sentiment Analysis using Supervised Classification," in Arabic Sentiment Analysis using Supervised Classification, RehabIEEE: 2014 International Conference on Future Internet of Things and Cloud, Barcelona, 2014.
M. Dr. Amrah Kasim, "Linguistiq Al Qur’an," Jurnal Shaut Al-'Arabiyah, vol. V, no. 1, 2017.
Rahima, Z. Samir and M. Farhi," Extracting semantic relations from the Quranic Arabic based on Arabic conjunctive patterns, Journal of King Saud University – Computer and Information Science, vol. 30, p. 382–390, 2018.
Nur, H. Nurul and H. M. Ahamed, "Text categorisation in Quran and Hadith: Overcoming the interrelation challenges using machine learning and term weighting," Journal of King Saud University, vol. 33, p. 658–667, 2021.
Atwell, "An artificial intelligence approach to Arabic and Islamic content on the internet," IEEE: Proceedings of NITS 3rd National Information Technology Symposium, Leeds, 2011.
W. Cherif, A. Madani and M. Kissi, "Building a syntactic rules-based stemmer to improve search effectiveness for arabic language, IEEE: 9th International Conference on Intelligent Systems: Theories and Applications (SITA-14) , 2014.
D. Agustina, Y. Yoyo And M. T. Bin Pa, "Pola Kata Jama’taksīr Dalam Novel “Qātilu Hamzah” Karya Najib Kailani (Analisis Morfosintaksis,A Jamiy: Jurnal Bahasa Dan Sastra Arab, Vol. 10, No. 2, Pp. 308-325, 2021.
Y. Liu, C. Sun, L. Lin and X. Wang, " Learning natural language inference using bidirectional LSTM model and inner-attention," arXiv preprint arXiv:1605.09090., 2016.
A. H. Mohammad, T. Alwada’n and O. Al-Momani, "Arabic text categorization using support vector machine, Naïve Bayes and neural network,Journal on Computing (JoC), vol. 5, no. 1, 2016.
K. Dukes, E. Atwell and N. Habash, "Supervised collaboration for syntactic annotation of Quranic Arabic," Journal of Language resources and evaluation, 47(1), vol. 47, no. 1, pp. 33-62, 2013.
A. O. Adeleke, N. A. Samsudin, A. Mustapha and N. M. Nawi, "A.,Comparative analysis of text classification algorithms for automated labelling of Quranic verses,J. Adv. Sci. Eng. Inf. Technol, vol. 7, no. 4, p. 1419, 2017.
Z. Touati-Hamad, M. R. Laouar and I. Bendib, "Quran content representation in NLP, Proceedings of the 10th International Conference on Information Systems and Technologies, 2020.
H. Bassam, S. Azzam and E.-H. Mahmoud," Enhancing retrieval effectiveness of diacritisized arabic passages using stemmer and thesaurus,The 19th Midwest Artificial Intelligence and Cognitive Science Conference (MAICS2008), 2008.
O. Ahmad, I. Hyder, R. Iqbal, M. A. A. M. Murad, S. N. M. A. and M. Mansoor, " A survey of searching and information extraction on a classical text using ontology-based semantics modeling: A case of Quran,Life Science Journal, vol. 10, no. 4, pp. 1370-1377, 2013.
S. L. Marie-Sainte, Alalyani, A. S. N., S. Ghouzali and I. Abunadi, " Arabic natural language processing and machine learning-based systems," IEEE Access, vol. 7, pp. 7011-7020, 2018.
H. A. R. T. Khasawneh, M. N. Al-Kabi and I. M. Alsmadi, "Sentiment analysis of Arabic social media content: A comparative study,Proc. 8th Int. Conf. Internet Technol. Secured Trans. (ICITST), 2013.
N. H. M., S. Elmougy, A. Ghoneim, T. Hamza, "Naive Bayes classier based Arabic document categorization, Proc. 7th Int. Conf. Inform. Syst. (INFOS), 2010.
J. R, T. Saleh, S. Khattab and I. Farag, "Detecting Arabic spam Web pages using content analysis," Int. J. Rev. Comput, vol. 6, p. 18, 2011.
S. S. A, A. Q. AlHamad, M. Al-Emran and K. Shaalan, "A survey of arabic text mining in Intelligent Natural Language Proces", Switzerland: Springer, vol. 740, 2018.
