MODEL IDENTIFIKASI JENIS NAGHAM ALQURAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

Velia Rahmadi, . (2020) MODEL IDENTIFIKASI JENIS NAGHAM ALQURAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

In tilawatil quran competition, the reciters reading Quran based on rhythm or nagham. The famous type of nagham in Indonesia consists of seven nagham. But there are many people who does not know about nagham’s Quran. People who have never learned reading Quran with nagham, still have difficulty recognizing the differences in the types of nagham. From that reason, this study aims to create a model to identification of the type Qur'an nagham. The model can be used to determine the type of nagham from Alfatihah's reading. So that it can help the people in learning nagham. And can be used by recitations teachers in assessing the reading of the Quran with nagham. Nagham used in this research are bayyati, hijaz, nahawand and rast. This research uses MFCC as feature extraction algorithm and the classification process using CNN. The results of making the CNN model with batch normalization and dropout regularization produce an average accuracy of 90%. The accuracy of each type of bayyati, hijaz, nahawand and rast is 92%, 85.9%, 91.2% and 91.4%.

Item Type:	Thesis (Skripsi)
Additional Information:	[No.Panggil: 1610511012] [Pembimbing: Jayanta] [Penguji 1: Ermatita] [Penguji 2: Henki Bayu Seta]
Uncontrolled Keywords:	Naqham Alquran, Audio Classification, Convolutional Neural Network
Subjects:	T Technology > T Technology (General)
Divisions:	Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User:	Velia Rahmadi
Date Deposited:	13 Jan 2022 02:11
Last Modified:	13 Jan 2022 02:11
URI:	http://repository.upnvj.ac.id/id/eprint/6609

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