Skripsi

KLASIFIKASI GENRE MUSIK BERDASARKAN COVER ALBUM MENGGUNAKAN METODE DEEP LEARNING

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Penilaian

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Classification of music genres has been carried out using various features. Research by Zhang (2022) conducted music genre classification based on sound frequency waves, resulting in an accuracy of 91% with a dataset of 1000 audio files. Music genre classification based on lyrics has also been studied by Oramas et al. (2017), the research used a dataset of 31,471. This study focuses on solving the problem of music genre classification using a dataset of music album covers with VGG-16. VGG-16 itself is a deep learning model developed by Simonyan & Zisserman (2015) as part of the ILSVRC-2014 competition. By using VGG-16 and a dataset consisting of music album covers from 7 genres of music, the study was conducted with 30 epochs. The trained VGG-16 model was then tested using a separate dataset for testing purposes. Initially, music genres were classified into 19 labels, with labels having less than 20% representation. Subsequently, adjustments were made to reduce the labels to 7, with a configuration of 30 epochs. The testing results of this study showed an accuracy of 56%. The accuracy improvement occurred with the adjustment of music genres by 166%. It can be concluded that the number of music genres determines the success rate of the VGG-16 method in classification. Keywords: music genre classification, VGG-16, deep learning, album covers, Convolutional Neural Networks (CNNs)

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003626	T145878	T1458782024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1458782024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer., 2024

Collation

xvii, VI-1 hlm.; tab.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.507

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

-

Subject(s)

Prodi Teknik Informatika
Deep Learning

Specific Detail Info

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Statement of Responsibility

UIN Farrah

Other version/related

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