Enhancing Mujawwad Qur'anic Recitation Rhythm Classification Using Optimized LSTM Algorithm

Japar Sidik; Ade Irma Purnamasari; Edi Tohidi

doi:10.59934/jaiea.v4i3.984

Authors

Japar Sidik STMIK IKMI Cirebon
Ade Irma Purnamasari STMIK IKMI Cirebon
Edi Tohidi STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i3.984

Keywords:

Deep Learning, LSTM, Rhythm Classification, Mujawwad, MFCC

Abstract

This research develops an automated classification system for Qur'anic recitation rhythms using the Long Short-Term Memory (LSTM) deep learning approach. The study aims to enhance rhythm identification accuracy by applying hyperparameter optimization techniques. Audio data was collected from mujawwad recitation recordings at Al-Falah 2 Nagreg Islamic Boarding School. Mel-frequency Cepstral Coefficients (MFCC) was extracted as acoustic features, and a systematic GridSearch approach was used to optimize the LSTM model. The proposed model achieved 88.07% classification accuracy, significantly outperforming the Naïve Bayes Classifier (38.97%). Confusion matrix analysis revealed superior performance in classifying complex rhythms, particularly bayati (95%), jiharkah (92%), and rast (90%) styles. This research demonstrates the potential of deep learning in understanding intricate Qur'anic recitation patterns and provides a foundation for developing advanced learning and assessment tools.

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References

S. Suarni and S. Syukrinur, “History of the Development of Nagham Al-Qur’an in Indonesia,” J. Ilm. Al-Muashirah Media Kaji. Al-Quran Dan Al-Hadits Multi Perspekt., vol. 20, no. 2, pp. 288–298, Jul. 2023, doi: 10.22373/JIM.V20I2.18726.

R. B. Qureshi and K. Nelson, “The Art of Reciting the Qu’ran,” Ethnomusicology, vol. 33, no. 3, p. 522, 1989, doi: 10.2307/851774.

V. Y. Mafula, Abd. C. Fauzan, and T. R. Fernando, “Identifikasi Irama Tilawah al-Quran dengan Gaya Mujawwad Menggunakan Naive Bayes Classifier,” Ilk. J. Comput. Sci. Appl. Inform., vol. 4, no. 2, pp. 242–251, Aug. 2022, doi: 10.28926/ilkomnika.v4i2.464.

S. Shahriar and U. Tariq, “Classifying Maqams of Qur’anic Recitations Using Deep Learning,” IEEE Access, vol. 9, pp. 117271–117281, 2021, doi: 10.1109/ACCESS.2021.3098415.

C. R. Wairata, E. R. Swedia, and M. Cahyanti, “PENGKLASIFIKASIAN GENRE MUSIK INDONESIA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK,” Sebatik, vol. 25, no. 1, pp. 255–261, Jun. 2021, doi: 10.46984/sebatik.v25i1.1286.

A. S. Ma`ruf, “IMPLEMENTASI DEEP LEARNING PADA KLASIFIKASI NATURE MUSIC DENGAN METODE EKSTRAKSI FITUR MEL-FREQUENCY CEPSTRAL COEFFICIENT (MFCC),” 2023, [Online]. Available: http://repositori.uin-alauddin.ac.id/id/eprint/27149

B. Logan, “Mel Frequency Cepstral Coefficients for Music Modeling,” Int. Symp. Music Inf. Retr., vol. 28, p. 11p., 2000, doi: 10.1.1.11.9216.

D. Prabakaran and S. Sriuppili, “Speech Processing: MFCC Based Feature Extraction Techniques- An Investigation,” J. Phys. Conf. Ser., vol. 1717, no. 1, p. 12009, Dec. 2021, doi: 10.1088/1742-6596/1717/1/012009.

K. Zaman, M. Sah, C. Direkoglu, and M. Unoki, “A Survey of Audio Classification Using Deep Learning,” IEEE Access, vol. 11, pp. 106620–106649, 2023, doi: 10.1109/ACCESS.2023.3318015.

F. Wolf-Monheim, “Spectral and Rhythm Features for Audio Classification with Deep Convolutional Neural Networks,” Oct. 2024, [Online]. Available: http://arxiv.org/abs/2410.06927

M. Z. Adam, N. Shafie, H. Abas, and A. Azizan, “Analysis of Momentous Fragmentary Formants in Talaqi-like Neoteric Assessment of Quran Recitation using MFCC Miniature Features of Quranic Syllables,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 9, pp. 533–540, 2021, doi: 10.14569/IJACSA.2021.0120960.

P. Anggeli, S. Suroso, and M. Z. Agung, “Klasifikasi Alat Musik Tradisional dengan Metode Machine Learning dengan Librosa dan Tensorflow pada Python,” J-SAKTI J. Sains Komput. Dan Inform., vol. 5, no. 2, Art. no. 2, Sep. 2021, doi: 10.30645/j-sakti.v5i2.390.

“Pondok Pesantren Al-Qur’an Al-Falah,” OfficialPonpesAlfalah. Accessed: Apr. 11, 2025. [Online]. Available: https://officialponpesalfalah.com

B. McFee et al., “librosa/librosa: 0.10.2.post1.” Zenodo, May 2024. doi: 10.5281/zenodo.11192913.

G. Samara, E. Al-Daoud, N. Swerki, and D. Alzu’bi, “The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning,” Adv. Multimed., vol. 2023, pp. 1–14, Mar. 2023, doi: 10.1155/2023/2642558.

T. O’Malley et al., “KerasTuner.” 2019.

I. Markoulidakis and G. Markoulidakis, “Probabilistic Confusion Matrix: A Novel Method for Machine Learning Algorithm Generalized Performance Analysis,” Technol. 2024 Vol 12 Page 113, vol. 12, no. 7, p. 113, Dec. 2024, doi: 10.3390/TECHNOLOGIES12070113.