KLASIFIKASI KATA BERDASARKAN POLA SINYAL EEG PADA AKTIVITAS DUA AKTIVITAS OTAK BERBEDA MENGGUNAKAN MODEL SVM
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
ALIV, FARHAN NUGRAHA
Yosrita, Efy
Aziza, Rosida Nur
Yosrita, Efy
Aziza, Rosida Nur
Subject
Teknik Informatika
Datestamp
2022-09-20 03:09:06
Abstract :
This study aims to classify EEG signal patterns from the words "eat", "drink", "hungry", "thirst", "happy", "sad", "sick", and "toilet" on the condition of seeing pictures, reading with normal voice, reading silently, in a relaxed state and in a state of imagining words with closed eyes. The model used in this study is Support Vector Machines with variations in the Linear, RBF and Sigmoid kernel functions. The results of the research are 3 classification models, namely the SVM model with Linear, Sigmoid and RBF kernel functions. The accuracy value obtained by the Linear kernel is 5%, Sigmoid is 2% and RBF is 13%.
This study aims to classify EEG signal patterns from the words "eat", "drink", "hungry", "thirst", "happy", "sad", "sick", and "toilet" on the condition of seeing pictures, reading with normal voice, reading silently, in a relaxed state and in a state of imagining words with closed eyes. The model used in this study is Support Vector Machines with variations in the Linear, RBF and Sigmoid kernel functions. The results of the research are 3 classification models, namely the SVM model with Linear, Sigmoid and RBF kernel functions. The accuracy value obtained by the Linear kernel is 5%, Sigmoid is 2% and RBF is 13%.
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