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All Journal Journal La Multiapp Indonesian Journal of Multidisciplinary Science
Mahmudin, Hajon Mahdy
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Innovation of Al-Quran Learning Platform with Deepspeech Artificial Intelligence Technology Using Design Sprint Method Mahmudin, Hajon Mahdy; Pratiwi, Emmy
Journal La Multiapp Vol. 6 No. 1 (2025): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v6i1.1793

Abstract

The development of the digital world is increasingly rapid, especially in the era of Industry 4.0 which is marked by advances in information technology. One application of this technology is in learning the Qur'an, the holy book of Muslims which contains divine guidance. This study explores the potential of artificial intelligence (AI) technology, especially Deep Speech, in developing an interactive, adaptive, and easily accessible Qur'an learning platform. This study aims to overcome illiteracy of the Qur'an and improve understanding of the messages of the Qur'an among Indonesian Muslims. Some of the challenges faced in learning the Qur'an in Indonesia include limited accessibility and inadequate learning experiences. This study identifies these problems and offers solutions through the use of AI Deep Speech technology in mobile applications. This technology is expected to increase the effectiveness and interactivity of Qur'an learning and help overcome the barriers of illiteracy of the Qur'an. The results of this study are expected to provide significant benefits for both academics and practitioners in the fields of education and technology. The expected benefits include contributing to the eradication of illiteracy in the Qur'an, the development of AI applications in Qur'an learning, increasing the effectiveness and accessibility of learning, and the development of design sprint methods in the development of technological products. The training model uses Deep Speech supported by TensorFlow, with 30% of the samples used as a validation set to prevent overfitting. The research approach combines qualitative and quantitative methods to gain in-depth insights into user needs and challenges.
Qur’an Recitation Correction System Using Deepspeech Mahmudin, Hajon Mahdy; Akbar, Habibullah
Indonesian Journal of Multidisciplinary Science Vol. 2 No. 11 (2023): Indonesian Journal of Multidisciplinary Science
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/ijoms.v2i11.638

Abstract

The purpose of this study was to compare the performance of the two types of models used in the task of classifying Quran verses based on audio similarity. The first model is Model B which uses MFCC features and the MaLSTM architecture, while the second model is Model C, which is Model B with additional delta features. The stages in this study consist of determining the dataset, determining the parameters, preprocessing, training, and testing. The dataset in this study was obtained from the local dataset https://sahabatibadah.com/fasih/. This study conducted data analysis based on 172,895 samples of Al-Quran recitation sounds from Juzz 30, which includes a total of 37 surahs with 564 verses. This sound data were taken from the recording on the Qara'a application and collected from 500 users of the application. In this study, 3 out of 500 users were used as training data to train speech recognition models, while one user was used as testing data. The training model used was DeepSpeech supported by TensorFlow. In the model training process, 30% of the samples were used as a validation set. Based on the results, Model B with the MFCC feature is the best model in the task of recognizing and classifying audio-based Quran verses. The use of the delta feature in Model B and Model C show a negative impact on model performance. The MFCC feature is more recommended in the recognition and classification of audio-based Qur’an verses, especially in the LSTM model architecture.
Co-Authors Akbar, Habibullah Pratiwi, Emmy