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All Journal PROtek : Jurnal Ilmiah Teknik Elektro
Osman, Hanaa Mohammed
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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A Deep Learning Approach for Recognizing the Noon Rule for Reciting Holy Quran Osman, Hanaa Mohammed; Mustafa, Ban Sharief; Mahmood, Basim Mohammed
PROtek : Jurnal Ilmiah Teknik Elektro Vol 11, No 2 (2024): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v11i2.7026

Abstract

Ahkam Al-Tajweed represents the most precious religious heritage that is in critical need to be preserved and kept for the next generation. This study tackles the challenge of learning Ahkam Al-Tajweed by developing a model that considers one of the rules experienced by early learners in the Holy Quran. The proposed model focuses, specifically, on the "Hukm Al-Noon Al-Mushaddah," which pertains to the proper pronunciation of the letter "Noon" when it is accompanied by a Shaddah symbol in Arabic. By incorporating this rule into the proposed model, learners will benefit the model because it will improve their Tajweed skills and facilitate the learning process for those who do not have access to private tutors or experts. The proposed approach involved three models namely, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Random Forest models in the context of a classification task. The models were evaluated based on their validation accuracy, and the results indicate that the CNN model achieved the highest validation accuracy of 0.8613. The other contribution of this work is collecting a novel dataset for this kind of study. The findings show that the Random Forest model outperformed the other models in terms of accuracy.
Enhancing Quranic Recitation Accuracy Using State-of-the-Art Audio Classification Techniques Osman, Hanaa Mohammed; Hussein, Maher Khalaf
PROtek : Jurnal Ilmiah Teknik Elektro Vol 12, No 3 (2025): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v12i3.8514

Abstract

- In this study, we investigate the potential of using state-of-the-art neural network architectures to increase the accuracy in classification of Quranic recitations per verse. Using a dataset of more than 4000 audio recordings annotated with rich metadata, the research has concentrated on differentiating accurate recitations through Hukm Al-Noon Al-Mushaddah specifications. This study uses three pre-trained deep learning models (Inception-V3, EfficientNet and MobileNet), as well as hybrid model proposed in this paper to perform classification of recitations. The raw audio inputs were converted into spectrograms for feature extraction and classification in each of the models. Experiments demonstrate that through the fusion, this hybrid model significantly outperforms individual predictions by dramatically improving precision, recall and F1-scores in five different verses. The total accuracy for the proposal model is 0.79 which is the highest comparing with Inception-V3 was 0.75 and EfficientNet was 0.73. The results underline the ability of such systems to provide immediate feedback for learners and thereby assist them in adhering to traditional recitation standards, a feature that helps maintain the originality of Quranic recitation. To enable usage on real data, further work should build a bigger dataset (samples of data) and optimize the model to providing feedback with larger latency
A Deep Learning Approach for Recognizing the Noon Rule for Reciting Holy Quran Osman, Hanaa Mohammed; Mustafa, Ban Sharief; Mahmood, Basim Mohammed
PROtek : Jurnal Ilmiah Teknik Elektro Vol 11, No 2 (2024): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v11i2.7026

Abstract

Ahkam Al-Tajweed represents the most precious religious heritage that is in critical need to be preserved and kept for the next generation. This study tackles the challenge of learning Ahkam Al-Tajweed by developing a model that considers one of the rules experienced by early learners in the Holy Quran. The proposed model focuses, specifically, on the "Hukm Al-Noon Al-Mushaddah," which pertains to the proper pronunciation of the letter "Noon" when it is accompanied by a Shaddah symbol in Arabic. By incorporating this rule into the proposed model, learners will benefit the model because it will improve their Tajweed skills and facilitate the learning process for those who do not have access to private tutors or experts. The proposed approach involved three models namely, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Random Forest models in the context of a classification task. The models were evaluated based on their validation accuracy, and the results indicate that the CNN model achieved the highest validation accuracy of 0.8613. The other contribution of this work is collecting a novel dataset for this kind of study. The findings show that the Random Forest model outperformed the other models in terms of accuracy.
Co-Authors Hussein, Maher Khalaf Mahmood, Basim Mohammed Mustafa, Ban Sharief