Traditional Music Regional Classification using Convolutional Neural Network (CNN)

Luis, Raymond; Rokhman, Nur

doi:10.22146/ijccs.73910

Open access Jurnal SINTA 2

Traditional Music Regional Classification using Convolutional Neural Network (CNN)

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 16 No. 4 (2022) · pp. 379–388

DOI: 10.22146/ijccs.73910
ISSN: 2460-7258, 1978-1520
Published: 2022-10-31
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

Traditional Indonesian music is an Indonesian cultural heritage that is often forgotten by modern society. Many people do not know which area the traditional music came from. This is a problem because of the large amount of traditional music that loses its identity. Deep Learning technology can be a solution to this traditional music classification problem. The topic of traditional music classification was chosen because there has been no research using this topic before.This research will classify traditional music based on the area of origin using data from Youtube with the extraction method of the Mel-Frequency Cepstral Coefficients (MFCC) feature and the Convolutional Neural Network (CNN) classification model. There are 7 provinces that will be used as classification labels, namely Riau, Papua, Special Capital District of Jakarta, Special Region of Yogyakarta, North Sumatra, West Java, and South Sulawesi.The classification system produced in this study produced good classification accuracy with a value of 74.03%.

Keywords

Collaboration network of the authors

20 authors · 23 co-authorship links

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Universitas Gadjah Mada
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Co-author	Organisation	Joint articles
Anifudin Azis	Universitas Gadjah Mada	1
Praretno Wibowo	Affiliation unknown	1
Juwita Maharanti	Affiliation unknown	1
Iqnatius Dimas Nugroho	Universitas Gadjah Mada	1
Amelia Nursanti	Universitas Gadjah Mada	1
Tri Kuntoro Priyambodo	Universitas Gadjah Mada	1
Zaied Shouran	Universitas Gadjah Mada	1
Nur Azizul Haqimi	Universitas Gadjah Mada	1
Sigit Priyanta	Universitas Gadjah Mada	1
Friska Ayu Listya	Universitas Gadjah Mada	1
Wahyono Wahyono	Universitas Gadjah Mada	1
Riska Amalia Praptiwi	Universitas Gadjah Mada	1
Dayan Ramly Ramadhan	Universitas Gadjah Mada	1
Hendri Kurniawan Prakosa	Universitas Gadjah Mada	1
Anindhita Nisitasari	Universitas Gadjah Mada	1
Sarah Rosdiana Tambunan	Universitas Gadjah Mada	1
Luthfia Nurma Hapsari	Universitas Gadjah Mada	1
Reka Alamsyah	Universitas Gadjah Mada	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Traditional Music Regional Classification using Convolutional Neural Network (CNN)</dc:title>
	<dc:creator>Luis, Raymond</dc:creator>
	<dc:creator>Rokhman, Nur</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Traditional Music; Audio Classification; Convolutional Neural Network; CNN; Mel-Frequency Cepstral Coefficients</dc:subject>
	<dc:description xml:lang="en-US">Traditional Indonesian music is an Indonesian cultural heritage that is often forgotten by modern society. Many people do not know which area the traditional music came from. This is a problem because of the large amount of traditional music that loses its identity. Deep Learning technology can be a solution to this traditional music classification problem. The topic of traditional music classification was chosen because there has been no research using this topic before.This research will classify traditional music based on the area of origin using data from Youtube with the extraction method of the Mel-Frequency Cepstral Coefficients (MFCC) feature and the Convolutional Neural Network (CNN) classification model. There are 7 provinces that will be used as classification labels, namely Riau, Papua, Special Capital District of Jakarta, Special Region of Yogyakarta , North Sumatra, West Java, and South Sulawesi.The classification system produced in this study produced good classification accuracy with a value of 74.03%. </dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/73910</dc:identifier>
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	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 4 (2022): October; 379-388</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/73910/34986</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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