Perbandingan Metode Klasifikasi untuk Menentukan Tingkat Kenyamanan Suhu pada Kondisi Rileks Berbasis Sinyal EEG

Temperature control on air conditioner devices is still oriented to the target environment. This control mode ignores one's physiological condition. A person's thermal comfort varies when indoors. Thermal comfort is closely related to environmental thermal satisfaction conditions. EEG signal is a signal that can reflect brain activity. This research objective is provide classifier model for classifiying person’s thermal comfort based on eeg signal. This research used three conditions of room’s temperature. The features used by classfier are avarage frequency band, HFD, PFD, and MSE features. Classifier performance was assessed using ROC curve evaluation. The results of the classification of thermal comfort levels with EEG signals with the KNN classifier are obtained only by using the band frequency average feature, which is equal to 0.878 with a standard deviation of 0.022. While the SVM classifier gets the highest performance by using a combination of the average band + HFD frequency feature, which is 0.877 with a standard deviation of 0.013 in the linear kernel and RBF.