skip to main content

Sistem deteksi ketepatan pembacaan surah al-Kautsar berbasis kata menggunakan mel frequency cepstrum coefficient dan cosine similarity

Recites fidelity detection system of al-Kautsar verse based on words using mel frequency cepstrum coefficients and cosine similarity

Institut Teknologi Telkom Purwokerto, Indonesia

Received: 29 Jul 2019; Revised: 25 Sep 2019; Accepted: 7 Oct 2019; Available online: 5 Nov 2019; Published: 31 Jan 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
In this study, a system has been developed to help detect the accuracy of the reading of the Koran in the Surah Al-Kautsar based on the accuracy of the number and pronunciation of words in one complete surah. This system is very dependent on the accuracy of word segmentation based on envelope signals. The feature extraction method used was Mel Frequency Cepstrum Coefficients (MFCC), while the Cosine Similarity method was used to detect the accuracy of the reading. From 60 data, 30 data were used for training, while the rest were for testing. From each of the 30 training and test data, 15 data were correct readings, and 15 other data were incorrect readings. System accuracy was measured by word-for-word recognition, which results in 100 % of recall and 98.96 % of precision for the training word data, and 100 % of recall and 99.65 % of precision for the test word data. For the overall reading of the surah, there were 15 correct readings and 14 incorrect readings that were recognized correctly.
Keywords: speech signal recognition; Al-Kautsar recite; Mel coefficients; cosine similarity; MFCC
Funding: Institut Teknologi Telkom Purwokerto, Indonesia

Article Metrics:

  1. A. Muhammad, Z. U. Qayyum, W. I. Mirza, S. Tanveer, M-. Enriquez, and A. Z. Syed, “E-hafiz: Intelligent system to help muslims in recitation and memorization of Quran,” Life Science Journal, vol. 9, no. 1, pp. 534–541, 2012
  2. A. Elnagar, R. Ismail, B. Alattas, and A. Alfalasi, “Automatic classification of reciters of Quranic audio clips,” in 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications, Aqaba, Jordan, Nov. 2018, pp. 1-6. doi: 10.1109/AICCSA.2018.8612829
  3. M. Bezoui, A. Elmoutaouakkil, and A. Beni-Hssane, “Feature extraction of some Quranic recitation using mel-frequency cepstral coeficients (mfcc),” in International Conference on Multimedia Computing and Systems, Marrakech, Morocco, Oct. 2016, pp. 127–131. doi: 10.1109/ICMCS.2016.7905619
  4. N. R. R. Rashid, I. Venkat, F. Damanhoori, N. Mustaffa, W. Husain, and A. T. Khader, “Towards automating the evaluation of holy Quran recitations: a pattern recognition perspective,” in 2013 Taibah University International Conference on Advances in Information Technology for the Holy Quran and Its Sciences, Madinah, Saudi Arabia, Dec. 2013, pp. 424–428. doi: 10.1109/NOORIC.2013.88
  5. Y. Afrillia, H. Mawengkang, M. Ramli, F. Fadlisyah, and R. P. Fhonna, “Performance measurement of mel frequency ceptral coefficient (mfcc) method in learning system of al-quran based in nagham pattern recognition,” Journal of Physics: Conference Series, vol. 930, no. 1, pp. 1-6, 2017. doi: 10.1088/1742-6596/930/1/012036
  6. A. Mustofa, “Sistem pengenalan penutur dengan metode mel-frequency wrapping,” Jurnal Teknik Elektro, vol. 7, no. 2, pp. 88-96, 2007. doi: 10.9744/jte.7.2.88-96
  7. N. W. Arshad et al., “Makhraj recognition for al-quran recitation using mfcc,” IJIIP: International Journal of Intelligent Information Processing, vol. 4, no. 2, pp. 45-53, 2013
  8. T. Nasution, “Metoda mel frequency cepstrum coefficients (mfcc) untuk mengenali ucapan pada bahasa indonesia,” SATIN - Sains dan Teknologi Informasi, vol. 1, no. 1, pp. 22-31, 2012
  9. O. Nurdiana, J. Jumadi, and D. Nursantika “Perbandingan metode cosine similarity dengan metode jaccard similarity pada aplikasi pencarian terjemah al-qur’an dalam bahasa Indonesia,” Jurnal Online Informatika, vol. 1, no. 1, pp. 59-63, 2016. doi: 10.15575/join.v1i1.12
  10. M. R. Maarif and C. Hadi, “Implementasi cosine similarity dalam aplikasi pencarian ayat al-quran berbasis android,” Jurnal Teknologi Informasi dan Komunikasi, vol. 6, no. 2, pp. 71-79, 2017
  11. I. Wijayanto, B. Hidayat, and Suyanto, “Pemisahan suara musik instrumental menggunakan metode independent component analysis dan pemodelan autoregressive moving average,” in Konferensi Nasional Sistem dan Informatika, Bali, Indonesia, Nov. 2011, pp. 61-68
  12. R. Wulandari, A. Izzuddin, and T. Asrori, “Pengenalan ucapan menggunakan algoritma back propagation,” Energy, vol. 6, no. 1, pp. 28-36, 2016
  13. K. Liu et al., “Endpoint detection of distributed fiber sensing systems based on STFT algorithm,” Optics & Laser Technology, vol. 114, pp. 122-126, 2019. doi: 10.1016/j.optlastec.2019.01.036
  14. N. N. Lokhande, N. S. Nehe, and P. S. Vikhe, “Voice activity detection algorithm for speech recognition applications,” International Journal of Computer Applications, vol. ICCIA, no. 6, pp. 5-7, 2012
  15. I. McLoughlin, Applied science and audio processing with matlab examples. Cambridge University Press, 2009
  16. H. Erokyar, “Age and gender recognition for speech applications based on support vector machines,” master thesis, University of South Florida, 2014
  17. I. H. Witten, E. Frank, and M. A. Hall, Data mining: practical machine learning tools and techniques. Morgan Kaufmann Publishers, 2016

Last update:

No citation recorded.

Last update: 2024-02-22 02:43:52

No citation recorded.