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All Journal Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali)
Noor, M Galvanir
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Klasifikasi Sinyal EEG Menggunakan Algoritma Random Forest dan SVM pada Area Motor Cortex Pagiling, Luther; Nugroho, Yabes D; Noor, M Galvanir; Galugu, Indrayati
Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Vol 5, No 3 (2020): Jurnal Elektroda Vol 5 No 3
Publisher : Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/jfe.v5i3.13072

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%
Klasifikasi Sinyal EEG Menggunakan Algoritma Random Forest dan SVM pada Area Motor Cortex Pagiling, Luther; Nugroho, Yabes D; Noor, M Galvanir; Galugu, Indrayati
Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Vol 5, No 3 (2020): Jurnal Elektroda Vol 5 No 3
Publisher : Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/jfe.v5i3.13072

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%
Co-Authors Galugu, Indrayati Nugroho, Yabes D Pagiling, luther