A Determining the Law of Reading Tajweed (Idgham Qomariyah and Syamsiyah) in the Qur'an Using the Naïve Bayes Algorithm: paper

A Determining the Law of Reading Tajweed (Idgham Qomariyah and Syamsiyah) in the Qur'an Using the Naïve Bayes Algorithm

paper

Authors

  • Haikal Azhar Department of Informatics, UIN Sunan Gunung Djati Bandung
  • Ibham Bathsyi Hizbullah Department of Informatics, UIN Sunan Gunung Djati Bandung

DOI:

https://doi.org/10.15575/kjrt.v1i2.287

Keywords:

Naive Bayes, Tajweed, Qur'an

Abstract

Learning tajweed to recite the Qur’an is important. Because if there is a mispronunciation, the meaning will be different. This research aims to detect two of the many tajweed, namely idghom qomariyah and syamsiyah using machine learning technology with a classification approach. This research uses the Naive Bayes algorithm to classify idghom qomariyah and syamsiyah in Al-Quran text documents. Based on experimental results using 82,173 text data, Naive Bayes was able to classify idgham qomariyah and syamsiyah with an accuracy rate of 96,80%.

References

M. J. Aqel and N. M. Zaitoun, “Tajweed: An Expert System for Holy Qur’an Recitation Proficiency,” in Procedia Computer Science, 2015. doi: 10.1016/j.procs.2015.09.029.

N. J. Ibrahim, M. Y. I. Idris, Z. Razak, and N. N. A. Rahman, “Automated tajweed checking rules engine for Quranic learning,” Multicultural Education and Technology Journal, 2013, doi: 10.1108/METJ-03-2013-0012.

A. P. Andriyandi, W. Darmalaksana, D. S. adillah Maylawati, F. S. Irwansyah, T. Mantoro, and M. A. Ramdhani, “Augmented reality using features accelerated segment test for learning tajweed,” TELKOMNIKA (Telecommunication, Computing, Electronics and Control), vol. 18, no. 1, pp. 208–216, 2020, doi: 10.12928/TELKOMNIKA.V18I1.14750.

A. Alfaries, M. Albahlal, M. Almazrua, and A. Almazrua, “A Rule-Based Annotation System to Extract Tajweed Rules from Quran,” in Proceedings - 2013 Taibah University International Conference on Advances in Information Technology for the Holy Quran and Its Sciences, NOORIC 2013, 2015. doi: 10.1109/NOORIC.2013.63.

Nurul A’ayunnisa, Y. Salim, and H. Azis, “Analisis Performa Metode Gaussian Naïve Bayes untuk Klasifikasi Citra Tulisan Tangan Karakter Arab,” Indonesian Journal of Data and Science, vol. 3, no. 3, pp. 115–121, Dec. 2022, doi: 10.56705/ijodas.v3i3.54.

S. Hidayatullah, “Classification of Al-Qur’an Arabic Verses Used Naive Bayes,” Jurnal Mantik, vol. 6, no. 1, pp. 717–725, 2022.

A. Adiwijaya, A. Riyani, and M. S. Mubarok, “A Comparison of Naïve Bayes and Bayesian Network on The Classification of Hijaiyah Pronunciation with Punctuation Letters,” in Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018), Paris, France: Atlantis Press, 2019. doi: 10.2991/icoiese-18.2019.9.

M. Alatiyyah, “Quran Reciter Identification: Techniques and Challenges,” J Theor Appl Inf Technol, vol. 101, no. 16, 2023.

M. M. Al Anazi and O. R. Shahin, “A Machine Learning Model for the Identification of the Holy Quran Reciter Utilizing K-Nearest Neighbor and Artificial Neural Networks,” Information Sciences Letters, vol. 11, no. 4, pp. 1093–1102, Jul. 2022, doi: 10.18576/isl/110410.

M. Hadwan, H. A. Alsayadi, and S. AL-Hagree, “An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters,” Computers, Materials & Continua, vol. 74, no. 2, pp. 3471–3487, 2023, doi: 10.32604/cmc.2023.033457.

M. Maiyurita, Z. K. Simbolon, and N. Prihatin, “Aplikasi Pembelajaran Tajwid Menggunakan Linear Congruent Method Berbasis Android,” Jurnal Infomedia, 2018, doi: 10.30811/.v1i1.287.

D. Hamdhana, F. Fadlisyah, and S. Adani, “Sistem Pendeteksi Pola Tajwid Al-Qur’an Hukum Ikhfa Syafawi Dan Idgham Mimi Pada Citra Menggunakan Metode Euclid Distance Dan Bray Curtis Distance,” TECHSI - Jurnal Teknik Informatika, 2018, doi: 10.29103/techsi.v10i2.886.

K. M. Ting, “Confusion Matrix,” in Encyclopedia of Machine Learning and Data Mining, 2017. doi: 10.1007/978-1-4899-7687-1_50.

S. A. M. Mu’abbad, Buku Panduan Lengkap Ilmu Tajwid (Taqiya). Taqiya, 2015.

T. Jiang, J. L. Gradus, and A. J. Rosellini, “Supervised Machine Learning: A Brief Primer,” Behav Ther, vol. 51, no. 5, pp. 675–687, Sep. 2020, doi: 10.1016/j.beth.2020.05.002.

A. Rifai, “Kajian Algortma C4.5, Naive Bayes, Neural Network, dan SVM dalam Penentuan Kelayakan Kredit,” Jurnal Sistem Informasi, vol. 5, no. 2, pp. 176–182, 2017.

E. Darmawan, “C4.5 Algorithm Application for Prediction of Self Candidate New Students in Higher Education,” Jurnal Online Informatika, vol. 3, no. 1, p. 22, 2018, doi: 10.15575/join.v3i1.171.

L. Andiani, S. Sukemi, and D. P. Rini, “Analisis Penyakit Jantung Menggunakan Metode KNN Dan Random Forest,” in Annual Research Seminar (ARS), 2020, pp. 165–169.

B. Y. Pratama and R. Sarno, “Personality classification based on Twitter text using Naive Bayes, KNN and SVM,” in Proceedings of 2015 International Conference on Data and Software Engineering, ICODSE 2015, 2016, pp. 170–174. doi: 10.1109/ICODSE.2015.7436992.

M. H. Hassoun, “Fundamentals of Artificial Neural Networks,” Proceedings of the IEEE, 2005, doi: 10.1109/jproc.1996.503146.

A. Ahmad, “Mengenal artificial intelligence, machine learning, neural network, dan deep learning,” J. Teknol. Indones., no. October, p. 3, 2017.

M. Q. (Muhammad Q. Shihab, Tafsir Al-Mishbah : pesan, kesan, dan keserasian Al-Qur’an volume 1-15 / pengarang, M. Quraish Shihab., 1st ed., vol. 1–15. Tanggerang: Lentera Hati, 2021., 2021.

Downloads

Published

2023-12-25

Issue

Section

Articles
Loading...