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Open access Jurnal SINTA 2

Klasifikasi Lagu Berdasarkan Genre pada Format WAV

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 8 No. 2 (2014) · pp. 145–154

DOI
10.22146/ijccs.6542
ISSN
2460-7258, 1978-1520
Published
2014-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

AbstrakDalam dunia yang berkembang pesat, media audio semakin komplek. Karena itulah diperlukan sebuah mekanisme penentuan jenis lagu (genre) yang tepat secara efektif dan efisien. Pencarian secara manual sudah tidak efektif dan efisien lagi karena banyaknya data yang tersimpan. Zero Crossing Rate (ZCR), Average Energy (E) dan Silent Ratio (SR) adalah 3 Feature Extraction yang digunakan untuk klasifikasi pencarian 12 genre.Tiga dimensi adalah bentuk visualisasi pengukuran tingkat kemiripan sebuah data berdasarkan hasil klasifikasi yang diinput oleh user. Dalam penelitian ini pengujian klasifikasi menggunakan metode 3, 6, 9 dan 12 genre melalui jarak terdekat (Euclidean Distance). Hasil pengujian yaitu menunjukkan bahwa 3 genre yaitu Balada, Blues dan Classic menunjukkan = 96,67%, 6 genre yaitu Balada, Blues, Classic, Harmony, Hip Jop dan Jazz menunjukkan = 70% dan 9 genre yaitu Balada, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin dan Pop menunjukkan = 53,33% serta 12 genre = 33,33% Kata Kunci— Zero Crossing Rate (ZCR), Average Energy (E), Silent Ratio (SR), Euclidean Distance Abstract Music genre is getting complex from time to time. As the size of digital media grows along with amount of data, manual search of digital audio files according to its genre is considered impractical and inefficient; therefore a classification mechanism is needed to improve searching. Zero Crossing Rate (ZCR), Average Energy (E) and Silent Ratio (SR) are a few of features that can be extracted from digital audio files to classify its genre. This research conducted to classify digital audio (songs) into 12 genres: Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop, Electronic, Reggae and Rock using above mentioned features, extracted from WAV audio files. Classification is performed several times using selected 3, 6, 9 and 12 genres respectively. The result shows that classification of 3 music genres (Ballad, Blues, Classic) has the highest accuracy (96.67%), followed by 6 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz) with 70%, and 9 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop) with 53.33% accuracy. Classification of all 12 music genres yields the lowest accuracy of 33.33%. Keywords— Zero Crossing Rate (ZCR), Average Energy (E), Silent Ratio (SR), Euclidean Distance

Collaboration network of the authors

40 authors · 52 co-authorship links · 15 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Universitas Stikubank
  • University of Indonesia
  • State University of Semarang
  • Universitas Pendidikan Ganesha
  • State University of Medan
  • Duta Wacana Christian University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Edi WinarkoUniversitas Gadjah Mada3
Retantyo WardoyoUniversitas Gadjah Mada2
Sri T. HartatiUniversitas Gadjah Mada2
Edy WinarnoUniversitas Stikubank2
Aniati Murni ArymurthyUniversity of Indonesia2
Kridalaksana Harsa AwangAffiliation unknown1
Zaenal AbidinState University of Semarang1
Yustina RetnoAffiliation unknown1
Abriyono AbriyonoAffiliation unknown1
I Gede Aris GunadiUniversitas Pendidikan Ganesha1
Rocky Yefrenes DillakUniversitas Gadjah Mada1
Ikhwan RusliantoUniversitas Gadjah Mada1
Rahmi HidayatiUniversitas Gadjah Mada1
Salman AliajiUniversitas Gadjah Mada1
Refta ListiaUniversitas Gadjah Mada1
Mahmuddin YunusAffiliation unknown1
Hermawan SyahputraState University of Medan1
Sri SuwarnoDuta Wacana Christian University1
Rajif Agung YunmarSumatera Institute of Technology1
La Ode Hasnuddin S. SagalaUniversitas Gadjah Mada1
Nicodemus Mardanus SetiohardjoPoliteknik Negeri Kupang1
I Putu Adi PratamaUniversitas Gadjah Mada1
Herman HermanUniversitas Gadjah Mada1
Dian Hafidh ZulfikarUniversitas Gadjah Mada1
Made Leo RadhityaInstitut Bisnis dan Teknologi Indonesia1
Kharis SyabanUniversitas Gadjah Mada1
Pawit RiantoAffiliation unknown1
Adri PriadanaUniversitas Gadjah Mada1
Ade NurhopipahUniversitas Amikom Purwokerto1
Aina MusdholifahUniversitas Gadjah Mada1
Nelson RumuiPoliteknik Negeri Ketapang1
Angga Maulana PurbaUniversitas Gadjah Mada1
Mohammad Edi WibowoAffiliation unknown1
Wahyono WahyonoUniversitas Gadjah Mada1
Abdul KholikUniversitas Gadjah Mada1
Anny Kartika SariUniversitas Gadjah Mada1
Alfian Abdul JalidUniversitas Gadjah Mada1
Nicolaus Euclides Wahyu NugrohoUniversitas Gadjah Mada1

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 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Klasifikasi Lagu Berdasarkan Genre pada Format WAV</dc:title>
	<dc:creator>Tamatjita, Nurmiyati</dc:creator>
	<dc:creator>Harjoko, Agus</dc:creator>
	<dc:description xml:lang="en-US">AbstrakDalam dunia yang berkembang pesat, media audio semakin komplek. Karena itulah diperlukan sebuah mekanisme penentuan jenis lagu (genre) yang tepat secara efektif dan efisien.  Pencarian secara manual sudah tidak efektif dan efisien lagi karena banyaknya data yang tersimpan.          Zero Crossing Rate (ZCR), Average Energy (E) dan Silent Ratio (SR) adalah 3 Feature Extraction yang digunakan untuk klasifikasi pencarian 12 genre.Tiga dimensi adalah bentuk visualisasi pengukuran tingkat kemiripan sebuah data berdasarkan hasil klasifikasi yang diinput oleh user.            Dalam penelitian ini pengujian klasifikasi menggunakan metode 3, 6, 9 dan 12 genre melalui jarak terdekat (Euclidean Distance). Hasil pengujian yaitu menunjukkan bahwa 3 genre yaitu Balada, Blues dan Classic menunjukkan = 96,67%, 6 genre yaitu Balada, Blues, Classic, Harmony, Hip Jop dan Jazz menunjukkan = 70% dan 9 genre yaitu Balada, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin dan Pop menunjukkan = 53,33% serta 12 genre = 33,33% Kata Kunci— Zero Crossing Rate (ZCR), Average Energy (E), Silent Ratio (SR), Euclidean Distance  Abstract            Music genre is getting complex from time to time. As the size of digital media grows along with amount of data, manual search of digital audio files according to its genre is considered impractical and inefficient; therefore a classification mechanism is needed to improve searching.            Zero Crossing Rate (ZCR), Average Energy (E) and Silent Ratio (SR) are a few of features that can be extracted from digital audio files to classify its genre. This research conducted to classify digital audio (songs) into 12 genres: Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop, Electronic, Reggae and Rock using above mentioned features, extracted from WAV audio files. Classification is performed several times using selected 3, 6, 9 and 12 genres respectively.            The result shows that classification of 3 music genres (Ballad, Blues, Classic) has the highest accuracy (96.67%), followed by 6 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz) with 70%, and 9 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop) with 53.33% accuracy. Classification of all 12 music genres yields the lowest accuracy of 33.33%.   Keywords— Zero Crossing Rate (ZCR), Average Energy (E), Silent Ratio (SR), Euclidean Distance</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">IndoCEISS</dc:contributor>
	<dc:date>2014-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/6542</dc:identifier>
	<dc:identifier>10.22146/ijccs.6542</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 8, No 2 (2014): July; 145-154</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/6542/5136</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2014 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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