Agfianto Eko Putra, Catur Atmaji, and Fajrul Ghaleb
In the area of affective computing technology, the classification of emotions can be used for a variety of things such as health, entertainment, education, etc. This study determined the classification of emotions based on EEG (Electroencephalography) signals, which is emotions are classified according to the 2-dimensional graphics modeling of arousal and valence. This research uses a wavelet decomposition method to get features from the EEG signal. Features taken from the signal is a power signal decomposition of sub-band theta, alpha, beta, and gamma. These features are derived from the 5 levels decomposition using Coiflet2 and Daubechies2 mother wavelet. Classification is done using k-Nearest Neighbor (kNN) with the closest neighbor calculated based on correlation distance. Data validation is done using 5-folds cross-validation for validation of test data and training data. The highest accuracy obtained by using the mother wavelet Coiflet 2 with kNN parameter k=21. Valence classification accuracy is 57.5%, and accuracy of arousal is 63.98%.
Dalam sebuah penelitian, 5 (lima) ekor monyet ditempatkan dalam suatu kandang yang dilengkapi dengan sebuah tangga yang menuju ke setandan pisang. Seekor monyet mencoba untuk menaiki tangga untuk mendapatkan pisang tersebut, tapi ada semprotan air yang disembunyikan di balik tangga bagian atas yang kemudian menyemprotkan air pada monyet tersebut sedemikian hingga monyet tersebut menghentikan usahanya mendapatkan pisang (turun tangga). Monyet lain juga mencoba, hal yang sama terjadi lagi dan akhirnya mereka menyerah.