Innovation of Al-Quran Learning Platform with Deepspeech Artificial Intelligence Technology Using Design Sprint Method

Hajon Mahdy Mahmudin; Emmy  Pratiwi

doi:10.37899/journallamultiapp.v6i1.1793

Hajon Mahdy Mahmudin Interdisciplinary School of Management and Technology Sepiluh November Institute of Technology, Surabaya, Indonesia
Emmy Pratiwi Interdisciplinary School of Management and Technology Sepiluh November Institute of Technology, Surabaya, Indonesia

DOI: https://doi.org/10.37899/journallamultiapp.v6i1.1793

Keywords: Artificial Intelligence, Profound Speech, Design Sprint

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.

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