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Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 7 No. 1 (2017)

DOI
10.11591/ijece.v7i1.pp486-495
ISSN
2088-8708, 2088-8708
Published
2017-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music. This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm. In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process. For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data. The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious. Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021. Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second.

Collaboration network of the authors

19 authors · 27 co-authorship links

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

  • Udayana University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ida Bagus Alit SwamardikaUdayana University2
Adinata Mas PratamaUdayana University2
Sri Widyanti GintingUdayana University1
I Nyoman Angga PrabawaUdayana University1
I Made Oka WidyantaraUdayana University1
Ida Bagus Gede ManuabaUdayana University1
I Gede Bagus Premana PutraUdayana University1
Ni Wayan Sri AriyaniUdayana University1
I Nyoman AdiputraUdayana University1
Sri AriyaniUdayana University1
I Ketut Gede Darma PutraUdayana University1
P M PrihatiniUdayana University1
Ida Ayu Dwi GiriantariUdayana University1
IBA SwamardikaAffiliation unknown1
IW Dani PranataUdayana University1
Anak Agung Kompiang Oka SudanaAffiliation unknown1
Irwansyah CahyaAffiliation unknown1

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.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction</dc:title><dc:creator>Sudarma, Made</dc:creator><dc:creator>Harsemadi, I Gede</dc:creator><dc:description>&lt;jats:p&gt;Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music.  This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm.  In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process.  For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data.  The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious.  Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021.  Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2017-02-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v7i1.pp486-495</dc:identifier><dc:identifier>10.11591/ijece.v7i1.pp486-495</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 7 No 1 (2017); 486</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/6302/6052</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>