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EEG CLASSIFICATION FOR EPILEPSY BASED ON WAVELET PACKET DECOMPOSITION AND RANDOM FOREST

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 11 No. 1 (2018) · pp. 27–33

DOI
10.21609/jiki.v11i1.549
ISSN
2502-9274, 2088-7051
Published
2018-02-28
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

EEG (electroencephalogram) can detect epileptic seizures by neurophysiologists in clinical practice with visually scan long recordings. Epilepsy seizure is a condition of brain disorder with chronic noncommunicable that affects people of all ages. The challenge of study is how to develop a method for signal processing that extract the subtle information of EEG and use it for automating the detection of epileptic with high accuration, so we can use it for monitoring and treatment the epileptic patient. In this study we developed a method to classify the EEG signal based on Wavelet Packet Decomposition that decompose the EEG signal and Random Forest for seizure detetion. The result of study shows that Random Forest classification has the best performance than KNN, ANN, and SVM. The best combination of statisctical features is standard deviation, maximum and minimum value, and bandpower. WPD is has best decomposition in 5th level.

Keywords

Collaboration network of the authors

30 authors · 72 co-authorship links · 11 across different organisations

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

  • Institut Teknologi Sepuluh Nopember
  • Universitas Airlangga
  • National Research and Innovation Agency
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nanik SuciatiInstitut Teknologi Sepuluh Nopember3
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember2
Imam KuswardayanInstitut Teknologi Sepuluh Nopember2
Ratri Enggar PaweningAffiliation unknown1
Tio DarmawanInstitut Teknologi Sepuluh Nopember1
Rizqa Raaiqa BintanaInstitut Teknologi Sepuluh Nopember1
Nursuci Putri HusainInstitut Teknologi Sepuluh Nopember1
Nursanti Novi ArisaInstitut Teknologi Sepuluh Nopember1
Putri Nur RahayuInstitut Teknologi Sepuluh Nopember1
Joko Lianto BulialiInstitut Teknologi Sepuluh Nopember1
Giri WiriapradjaAffiliation unknown1
Supria SupriaInstitut Teknologi Sepuluh Nopember1
Wijayanti Nurul KhotimahInstitut Teknologi Sepuluh Nopember1
Ridho Rahman HariadiInstitut Teknologi Sepuluh Nopember1
Imaduddin Al FikriInstitut Teknologi Sepuluh Nopember1
Deny Prasetia HermawanInstitut Teknologi Sepuluh Nopember1
Musta'inul AbdiInstitut Teknologi Sepuluh Nopember1
Ari Mahardika Ahmad NafisInstitut Teknologi Sepuluh Nopember1
Hadziq FabroyirInstitut Teknologi Sepuluh Nopember1
Matthew Vieri SuriawanInstitut Teknologi Sepuluh Nopember1
Muhammad Zain Fawwaz Nuruddin SiswantoroAffiliation unknown1
Chastine FatichahInstitut Teknologi Sepuluh Nopember1
Nur Nafi’iyahInstitut Teknologi Sepuluh Nopember1
Eha Renwi AstutiUniversitas Airlangga1
Ramadhan Hardani PutraUniversitas Airlangga1
Esa PrakasaNational Research and Innovation Agency1
Yosi KristianAffiliation 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.

Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">EEG CLASSIFICATION FOR EPILEPSY BASED ON WAVELET PACKET DECOMPOSITION AND RANDOM FOREST</dc:title>
	<dc:creator>Sugianela, Yuna</dc:creator>
	<dc:creator>Sutino, Qonita Luthfia</dc:creator>
	<dc:creator>Herumurti, Darlis</dc:creator>
	<dc:subject xml:lang="en-US">EEG</dc:subject>
	<dc:subject xml:lang="en-US">epilepsy</dc:subject>
	<dc:subject xml:lang="en-US">seizure</dc:subject>
	<dc:subject xml:lang="en-US">wavelet</dc:subject>
	<dc:subject xml:lang="en-US">random forest</dc:subject>
	<dc:description xml:lang="en-US">EEG (electroencephalogram) can detect epileptic seizures by neurophysiologists in clinical practice with visually scan long recordings. Epilepsy seizure is a condition of brain disorder with chronic noncommunicable that affects people of all ages. The challenge of study is how to develop a method for signal processing that extract the subtle information of EEG and use it for automating the detection of epileptic with high accuration, so we can use it for monitoring and treatment the epileptic patient. In this study we developed a method to classify the EEG signal based on Wavelet Packet Decomposition that decompose the EEG signal and Random Forest for seizure detetion. The result of study shows that Random Forest classification has the best performance than KNN, ANN, and SVM. The best combination of statisctical features is standard deviation, maximum and minimum value, and bandpower. WPD is has best decomposition in 5th level.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2018-02-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/549</dc:identifier>
	<dc:identifier>10.21609/jiki.v11i1.549</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 11 No. 1 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 27-33</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v11i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/549/386</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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