Constructing Qur’an Recitation Classification using Alexnet Algorithm

Rosyid, Harits Ar and Abdullah, Dzulkifli and Alqahtani, Mohammed S. (2024) Constructing Qur’an Recitation Classification using Alexnet Algorithm. Knowledge Engineering and Data Science, 7 (2). pp. 152-163. ISSN 2597-4637

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Abstract

The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel-Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC and AlexNet yields promising accuracy in classifying Surah Al-Ikhlas recitations, suggesting its potential application for automatic reading correction. This approach significantly improves over traditional methods, contributing to more effective tools for Qur'an memorization assistance. Future work could explore its application in other significant improvement contexts and address potential challenges related to varying audio quality

Item Type:	Artikel Umum
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User:	M.Eng. Alfian Ma'arif
Date Deposited:	21 Apr 2025 05:52
Last Modified:	21 Apr 2025 05:52
URI:	http://eprints.uad.ac.id/id/eprint/83111

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