Peningkatan Ekstrasi Ciri Sinyal Epilepsi Menggunakan Teknik Sampling - Ade Eviyanti; Hindarto Hindarto; M. Abror (Telematika, 2020)
Epilepsy is a brain disorder characterized by repeated seizures. Epileptic seizures are episodes that can vary from short periods to long periods and are almost undetectable. Electroencephalography (EEG) is a way to record the activities of the human brain. EEG is a brain sensor that can be used to determine epilepsy. The purpose of this study is to classify the signals of people who have epilepsy disease and the signals of people in good health. The method used is a sampling technique method to find the characteristics of EEG signals and the K-Nearest Neighbor (KNN) method to find the classification of EEG signals. The data used is EEG signal data consisting of five data sets (data set A, data set B, data set C, data set D, and data set E), this data comes from public data. Each data set contains 100 EEG signal data on one EEG sensor channel. This study only uses two classes, the first is Data Set A and the second is Data Set E. Data Set A is a person in normal condition and data set E is a person in a state of having epilepsy. In the sampling technique process, the values taken are the average value and the standard deviation value. Research that has been done yields an accuracy rate of 100%.