Identification of tajwid using artificial neural networks / Zunnajah Kamaruddin
Al-Quran is, the most important book in Muslims' life as it gives knowledge in many areas for the use of their daily life. Therefore, it is needed to be read properly so the meaning of the reading is correct. In addition, learning tajwid is a must in order to improve better reading, The main pu...
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| Format: | Thesis |
| Language: | en |
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2005
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| Online Access: | https://ir.uitm.edu.my/id/eprint/1019/1/TB_ZUNNAJAH%20KAMARUDDIN%20CS%2005_5%20P01.pdf https://ir.uitm.edu.my/id/eprint/1019/ |
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| _version_ | 1833058000711974912 |
|---|---|
| author | Kamaruddin, Zunnajah |
| author_facet | Kamaruddin, Zunnajah |
| author_sort | Kamaruddin, Zunnajah |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Al-Quran is, the most important book in Muslims' life as it gives knowledge in many areas for the use of their daily life. Therefore, it is needed to be read properly so the meaning of the reading is correct. In addition, learning tajwid is a must in order to improve better reading, The main purpose of the project is to train artificial neural network (ANN) data to identify the tajwid. It is also trying to classify the tajwid based on letters and signs by defining their shape and location. Images are used as samples to be processed for the used of classification. In order to have a system which has an ability to learn, back-propagation learning algorithm is used. The results of the experiments done shows that the accurate results produced by the prototype is 20%. From the accurate results, 60% results are Mad Asli and 40% is lkhfa' Haqiqi. From the identification of Mad Asli, 40% accurate results are from the letter alif ( l ), 40% is from the letter wau ( و ) and 20% is from the letter ya ( ي ). As conclusion, it is hope that this project can be the starting point for a better learning of tajwid. |
| format | Thesis |
| id | my.uitm.ir-1019 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2005 |
| record_format | eprints |
| spelling | my.uitm.ir-10192018-10-19T08:17:58Z https://ir.uitm.edu.my/id/eprint/1019/ Identification of tajwid using artificial neural networks / Zunnajah Kamaruddin Kamaruddin, Zunnajah Electronic Computers. Computer Science Al-Quran is, the most important book in Muslims' life as it gives knowledge in many areas for the use of their daily life. Therefore, it is needed to be read properly so the meaning of the reading is correct. In addition, learning tajwid is a must in order to improve better reading, The main purpose of the project is to train artificial neural network (ANN) data to identify the tajwid. It is also trying to classify the tajwid based on letters and signs by defining their shape and location. Images are used as samples to be processed for the used of classification. In order to have a system which has an ability to learn, back-propagation learning algorithm is used. The results of the experiments done shows that the accurate results produced by the prototype is 20%. From the accurate results, 60% results are Mad Asli and 40% is lkhfa' Haqiqi. From the identification of Mad Asli, 40% accurate results are from the letter alif ( l ), 40% is from the letter wau ( و ) and 20% is from the letter ya ( ي ). As conclusion, it is hope that this project can be the starting point for a better learning of tajwid. 2005 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/1019/1/TB_ZUNNAJAH%20KAMARUDDIN%20CS%2005_5%20P01.pdf Identification of tajwid using artificial neural networks / Zunnajah Kamaruddin. (2005) Degree thesis, thesis, Universiti Teknologi MARA. |
| spellingShingle | Electronic Computers. Computer Science Kamaruddin, Zunnajah Identification of tajwid using artificial neural networks / Zunnajah Kamaruddin |
| title | Identification of tajwid using artificial neural networks / Zunnajah Kamaruddin |
| title_full | Identification of tajwid using artificial neural networks / Zunnajah Kamaruddin |
| title_fullStr | Identification of tajwid using artificial neural networks / Zunnajah Kamaruddin |
| title_full_unstemmed | Identification of tajwid using artificial neural networks / Zunnajah Kamaruddin |
| title_short | Identification of tajwid using artificial neural networks / Zunnajah Kamaruddin |
| title_sort | identification of tajwid using artificial neural networks / zunnajah kamaruddin |
| topic | Electronic Computers. Computer Science |
| url | https://ir.uitm.edu.my/id/eprint/1019/1/TB_ZUNNAJAH%20KAMARUDDIN%20CS%2005_5%20P01.pdf https://ir.uitm.edu.my/id/eprint/1019/ |
| url_provider | http://ir.uitm.edu.my/ |