Melola Discovery

← Melola Discovery

Open access Journal

Klasifikasi Sinyal EEG Menggunakan Algoritma Random Forest dan SVM pada Area Motor Cortex

Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) · Vol. 5 No. 3 (2020) · pp. 33–38

DOI
10.33772/jfe.v5i3.13072
ISSN
2502-5562
Published
2020-08-25
Publisher
Universitas Halu Oleo
Language
eng
License
creativecommons.org/licenses/by/4.0

Abstract

This Study focuses on the performance of calculations and comparison of accuracy between 2 methods of classification of EEG signal motor Movement activity. Therefore, The EEG signal data that has been Bandpassed is then extracted using the extraction method of the commonspatial pattern feature. common spatial patern is used in feature extraction processes to solve artifact problems in EEG signals and find matrix projection patterns. the classification process uses 2 classification algorithms to get and compare the best accuracy. this study uses SVM and Random forest to detect motor movement patterns. Accuracy obtained from both algorithms shows that the random forest algorithm has a fairly high accuracy with an average achievement of 96.8% accuracy and SVM 91.67%

Collaboration network of the authors

23 authors · 58 co-authorship links · 1 across different organisations

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

  • Universitas Halu Oleo
  • University of Brawijaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Agustinus LolokAffiliation unknown4
Sahabuddin HayUniversitas Halu Oleo2
Sahrul HusuAffiliation unknown2
Yuni Aryanu KoedoesAffiliation unknown2
Muhammad Nadzirin Anshari NurAffiliation unknown2
Muhammad Nadzirin Anshari NurAffiliation unknown1
Hidayat HidayatAffiliation unknown1
Samuel JieAffiliation unknown1
Laode Rausyanul FikriAffiliation unknown1
Indrayati GalunguAffiliation unknown1
Dwi Eva Octaviani BatupadangAffiliation unknown1
Sudarsono SudarsonoUniversity of Brawijaya1
Irma NurjannahAffiliation unknown1
Ld.M.Golok JayaAffiliation unknown1
Muhammad HasbiAffiliation unknown1
Abd. KadirAffiliation unknown1
La HasanudinUniversitas Halu Oleo1
Andi WiwinAffiliation unknown1
Abd KadirAffiliation 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://ojs.uho.ac.id/index.php/JFE/oai, first harvested 31 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Klasifikasi Sinyal EEG Menggunakan Algoritma Random Forest dan SVM pada Area Motor Cortex</dc:title>
	<dc:creator>Pagiling, Luther</dc:creator>
	<dc:creator>Nugroho, Yabes D</dc:creator>
	<dc:creator>Noor, M Galvanir</dc:creator>
	<dc:creator>Galugu, Indrayati</dc:creator>
	<dc:description xml:lang="en-US">This Study focuses on the performance of calculations and comparison of accuracy between 2 methods of classification of EEG signal motor Movement activity. Therefore, The EEG signal data that has been Bandpassed is then extracted using the extraction method of the commonspatial pattern feature. common spatial patern is used in feature extraction processes to solve artifact problems in EEG signals and find matrix projection patterns. the classification process uses 2 classification algorithms to get and compare the best accuracy. this study uses SVM and Random forest to detect motor movement patterns. Accuracy obtained from both algorithms shows that the random forest algorithm has a fairly high accuracy with an average achievement of 96.8% accuracy and SVM 91.67%</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Halu Oleo</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2020-08-25</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs.uho.ac.id/index.php/JFE/article/view/13072</dc:identifier>
	<dc:identifier>10.33772/jfe.v5i3.13072</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali); Vol 5, No 3 (2020): Jurnal Elektroda Vol 5 No 3; 33-38</dc:source>
	<dc:source>2502-5562</dc:source>
	<dc:source>10.33772/jfe.v5i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs.uho.ac.id/index.php/JFE/article/view/13072/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 luther pagiling ., Yabes D Nugroho, M Galvanir Noor, Indrayati Galugu .</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>