Sound matching on the translation of Al-Quran ayat as a learning media for children using mobile-based fast fourier transform and divide conquer algorithm

Abdurrodjak, M; Mud’is, M H; Qodim, Husnul; Khaerani, I F S R; Rosidin, Utang; Busro, Busro

doi:10.1088/1742-6596/1402/7/077060

Cited by 2 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of testing the application on the surahs Al-Ikhlas, Al-Kautsar, and An-Naas received 100% accuracy. Research has done something similar [8] with test results obtaining an accuracy of 61.1% for appropriate readings and 38.9% for inappropriate readings. Other research [9] Several other studies utilize more renewable and more complex technologies such as machine learning and deep learning.…”

Section: Related Workmentioning

confidence: 87%

Convolutional Neural Network (CNN) for Detecting Al-Qur'an Reciting and Memorizing

Hadiyansah,

Andamira

2023

Khazanah Journal of Religion and Technology

View full text Add to dashboard Cite

This research aims to make it easier to memorize the Koran without having to need other people. Memorizers of the Koran (hafiz) often need other people to memorize them to find out if there are errors in their reading. Therefore, this research utilizes machine learning technology to make it easier to read and memorize the Al-Qur'an using the Convolutional Neural Network (CNN) algorithm. CNN was chosen because it is very good at classifying images and audio and can learn and extract features from raw data, such as image and audio data automatically. As a result, the model created succeeded in distinguishing one verse from another very well. The validation results show that the model can correctly detect 57 verses from 64 recorded data, which means it has an accuracy rate of 89.06%. With this verse classification model, it can then be implemented into an application to help memorize the Al-Qur'an even without using the internet.

show abstract

Section: Related Workmentioning

confidence: 87%

Convolutional Neural Network (CNN) for Detecting Al-Qur'an Reciting and Memorizing

Hadiyansah,

Andamira

2023

Khazanah Journal of Religion and Technology

View full text Add to dashboard Cite

show abstract

“…The application utilizes the Fast Fourier Transform (FFT) algorithm and Divide Conquer (DC) for voice matching and text conversion. As a result, testing indicates 61.1% accuracy for appropriate reading and 38.9% for inappropriate reading [36]. [36] With the advent of Quran applications, learners now have the opportunity to delve into the art of tarannum through interactive platforms.…”

Section: Independentmentioning

confidence: 99%

“…As a result, testing indicates 61.1% accuracy for appropriate reading and 38.9% for inappropriate reading [36]. [36] With the advent of Quran applications, learners now have the opportunity to delve into the art of tarannum through interactive platforms. The Tarannum-SLA, a Quran application, is discussed in the article, aiming to facilitate tarannum learning and raise awareness about the Quran's importance.…”

Section: Independentmentioning

confidence: 99%

Quran Mobile Application: A Structured Review

Nurul Husna Mat Isa,

Noor Hazirah Abd Aziz,

Mariyah Ishak

et al. 2023

ARASET

View full text Add to dashboard Cite

Mobile applications are growing rapidly and can be employed for various purposes. Presently, they have been applied widely in education. The pace of technological advancement is incredibly fast, impacting all segments of society. One noteworthy area is the facilitation of learning to read and write the Quran, where recent technological developments have significantly simplified the process. However, these applications have certain limitations as they solely concentrate on utilizing technology without incorporating more inventive approaches to aid Quran readers in interacting with, memorizing, comprehending, and establishing connections with the topics covered in the Quran. This paper aims to systematically review and analyze current research on the Quran mobile application. The study reviewed relevant literature indexed in Scopus, Web of Science (WOS), and Directory of Open Access Journals (DOAJ) databases. Consequently, three key study themes have been identified after thoroughly analyzing the “Quran mobile application”: learning and memorization, text analysis and information retrieval, and user experience and engagement. As Quran mobile applications have increased accessibility to learning the Qur’an, they frequently concentrate only on the use of technology meanwhile innovative approaches to improve engagement, memorization, comprehension, and topic connections for Quran readers remain lacking. The outcomes of this study can serve as inspiration for other researchers, providing a solid groundwork for the development and improvements in understanding the Quran.

show abstract

Sound matching on the translation of Al-Quran ayat as a learning media for children using mobile-based fast fourier transform and divide conquer algorithm

Cited by 2 publications

References 8 publications

Convolutional Neural Network (CNN) for Detecting Al-Qur'an Reciting and Memorizing

Convolutional Neural Network (CNN) for Detecting Al-Qur'an Reciting and Memorizing

Quran Mobile Application: A Structured Review

Contact Info

Product

Resources

About