@thesis{thesis,
		author={ALIV  FARHAN NUGRAHA and Aziza  Rosida Nur and Yosrita  Efy},
		title ={KLASIFIKASI KATA BERDASARKAN POLA SINYAL EEG PADA AKTIVITAS DUA AKTIVITAS OTAK BERBEDA MENGGUNAKAN MODEL SVM},
		year={2022},
		url={http://156.67.221.169/4934/},
		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%.}
		}