Detection of Mad Lazim Harfi Musyba Images Uses Convolutional Neural Network

Achmad, Noeman and Dwipa, Handayani (2020) Detection of Mad Lazim Harfi Musyba Images Uses Convolutional Neural Network. In: 2nd International Conference on Engineering and Applied Sciences (2nd InCEAS) Yogyakarta, Indonesia, 16 November 2019, Yogyakarta, Indonesia.

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Abstract

Reading of Al Quran is an obligation for all Muslims. Lack of knowledge about the knowledge of recitation in reading the Al Quran is certainly a problem. The purpose of this study makes it easier for everyone to learn the law of recitation, especially Madd Lazim Harfi Musyba in the Al Quran verses. The method used is one of the deep neural network methods, namely Convolutional Neural Network (CNN), as real-time detection of the law of Madd Lazim Harfi Musyba, implementation of the method using the help of Tensorflow GPU (Graphic Processor Unit) library. The results of trials with the Deep Convolutional Neural Network model show the detection performance of 9 verses with an average accuracy of 93.25%. The conclusion is that the training data on the CNN model is very reliable in detecting the Mad lazim harfi musyba law. Therefore this system can be used to assist in applying the Mad Lazim Hafi Musyba legal recitation while reading the Al Quran.

Item Type: Conference or Workshop Item (Paper)
Subjects: Teknologi dan Ilmu Terapan > Komputer > Data File-file dan Database, Pangkalan Data, Pusat Data
Teknologi dan Ilmu Terapan > Teknik Informatika
Divisions: Fakultas Teknik > Teknik Informatika
Creators:
CreatorsNIM/NIDN
Achmad, Noeman0328048402
Dwipa, Handayani0317078008
Contributors:
ContributionNameNIDN/NIDK/NUP
AuthorNoeman, Achmad0328048402
AuthorHandayani, Dwipa0317078008
Depositing User: Achmad Noeman
Date Deposited: 29 Dec 2020 08:10
Last Modified: 29 Dec 2020 08:10
URI: http://repository.ubharajaya.ac.id/id/eprint/5008

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