Kohesi: Jurnal Sains dan Teknologi

Articles

Vol. 10 No. 8 (2026): Kohesi: Jurnal Sains dan Teknologi, ISSN 3025-1311

APLIKASI DETEKSI AYAT ALQURAN DENGAN MENGGUNAKAN MODEL PENGENALAN SUARA MACHINE LEARNING R-CNN (STUDI KASUS SURAH AN-NABA)

Submitted
January 22, 2026
Published
2026-01-22

Abstract

This research develops a voice-based Quranic verse detection application using an R-CNN machine learning model adapted for audio data. Focusing on Surah An-Naba (40 verses), the system converts audio recordings into visual representations in the form of spectrograms and Mel Frequency Cepstral Coefficients (MFCC), which are then processed using Conv1D and BiLSTM architectures. The dataset includes 40 verses in various sound qualities and intonations. The implementation results show a training accuracy of 85.12%, validation of 51.16%, and testing of 40.86% for Top-1 Prediction. However, Top-3 Accuracy reached 72.09%, indicating that 30 of the 40 verses were successfully recognized in the top three predictions. The system is integrated with a web interface and API to enable audio uploads, automatic tajwid analysis, and verse identification. This research shows that the R-CNN approach adapted to audio data has significant potential to improve the accessibility of the Quran, especially for users with visual or literacy limitations.

References

  1. Ahmad, A., Azzeh, M., Alnagi, E., Abu Al-Haija, Q., Halabi, D., Aref, A., & AbuHour, Y. (2024). Hate speech detection in the Arabic language: corpus design, construction, and evaluation. Frontiers in Artificial Intelligence, 7.
  2. https://doi.org/10.3389/frai.2024.1345445
  3. Al Harere, A., & Al Jallad, K. (2023). Mispronunciation Detection of Basic Quranic Recitation Rules using Deep Learning. ArXiv, 1–16.
  4. Damayanti, A., Alhadad Farhan, E. M., Firdaus, K., Maghfiroh, N., & Rosyani, P. (2023). Klasifikasi Jenis Barang di Gudang Asera Baby Shop Menggunakan Metode R-CNN. Jurnal Artificial Inteligent Dan Sistem Penunjang Keputusan, 1(2), 185–189. https://jurnalmahasiswa.com/index.php/aidanspk
  5. Fadlisyah, F. (2021). Sistem Pengenalan Ayat Al-Qur’an Melalui Suara Menggunakan Ada-Boost. Jurnal Teknologi Terapan and Sains 4.0, 2(3), 579. https://doi.org/10.29103/tts.v2i3.6440
  6. Hadiyansah, R., & Andamira, R. (2023). Convolutional Neural Network (CNN) for Detecting Al-Qur’an Reciting and Memorizing. Khazanah Journal of Religion and Technology, 1(2), 44–48. https://doi.org/10.15575/kjrt.v1i2.235
  7. Harere, A. Al, & Jallad, K. Al. (2023). Quran Recitation Recognition using End-to-End Deep Learning. 1–22.
  8. http://arxiv.org/abs/2305.07034
  9. Meiyanti, R. (2023). Implementasi Transformasi Fourier Pada Aplikasi Dalam Bantu Menghafal Al-Qur’an. Jurnal Informatika Kaputama (JIK), 7(2), 210–215. https://doi.org/10.59697/jik.v7i2.130
  10. Paleva, H. R., & Prasetio, B. H. (2024). Penerapan Short Time Fourier Transform pada MFCC untuk Sistem Pengenalan Ucapan Tingkat Stres. 1(1), 1–10.
  11. Ratri Enggar Pawening, Meliana Eka Puteri, Agmelita Dwi Jianika, & Fitriyah Hidayati. (2023). Deteksi Objek pada Citra Makanan Sebagai Rekomendasi Diet Menggunakan Metode Mask R-CNN. Jurnal Ilmiah Teknik Informatika Dan Komunikasi, 4(1), 87–97. https://doi.org/10.55606/juitik.v4i1.733
  12. Widodo, A., & Annas, M. A. (2024). Deteksi Intensitas Suara Batuk pasien Infeksi Saluran Pernafasan Akut ( ISPA ) Menggunakan Edge Impulse Machine Learning berbasis Model Mel Frequency Cepstral Coefficients ( MFCC ) Detection of Cough Sound Intensity in Patients with Acute Respiratory Infe. 10(1), 12–21.

Similar Articles

1-10 of 58

You may also start an advanced similarity search for this article.