Automatic Detection of Hijaiyah Letters Pronunciation using Convolutional Neural Network Algorithm

Authors

  • Yana Aditia Gerhana Faculty of Information and Communication Technology, Asia e-University; Department of Informatics, UIN Sunan Gunung Djati Bandung, Malaysia http://orcid.org/0000-0003-0105-9170
  • Aaz Muhammad Hafidz Azis UIN Sunan Gunung Djati Bandung, Indonesia
  • Diena Rauda Ramdania UIN Sunan Gunung Djati Bandung, Indonesia
  • Wildan Budiawan Dzulfikar UIN Sunan Gunung Djati Bandung, Indonesia
  • Aldy Rialdy Atmadja UIN Sunan Gunung Djati Bandung, Indonesia
  • Deden Suparman UIN Sunan Gunung Djati Bandung
  • Ayu Puji Rahayu Faculty of Education Fujian Normal University Fuzhou, China, China

DOI:

https://doi.org/10.15575/join.v7i1.882

Keywords:

Hijaiyah, Speech recognition, MFCC, CNN, CRISP-DM,

Abstract

Abstract— Speech recognition technology is used in learning to read letters in the Qur'an. This study aims to implement the CNN algorithm in recognizing the results of introducing the pronunciation of the hijaiyah letters. The pronunciation sound is extracted using the Mel-frequency cepstral coefficients (MFCC) model and then classified using a deep learning model with the CNN algorithm. This system was developed using the CRISP-DM model. Based on the results of testing 616 voice data of 28 hijaiyah letters, the best value was obtained for accuracy of 62.45%, precision of 75%, recall of 50% and f1-score of 58%.

Author Biography

Yana Aditia Gerhana, Faculty of Information and Communication Technology, Asia e-University; Department of Informatics, UIN Sunan Gunung Djati Bandung

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Published

2022-06-30

Issue

Vol. 7 No. 1 (2022)

Section

Article

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