KLASIFIKASI GELOMBANG OTAK UNTUK KEAMANAN MENGGUNAKAN METODE VOTING FEATURES INTERVAL 5 DAN DUA-TAHAP OTENTIKASI BIOMETRIK
Abstract
EEG merupakan suatu alat yang digunakan untuk melihat aktivitas kelistrikan pada otak manusia. Bentuk keluaran yang diterima EEG dikenal dengan gelombang otak. Pada perkembangannya gelombang otak tidak hanya dapat digunakan untuk hal medis saja namun dapat digunakan untuk hal lain seperti pendidikan, hiburan dan keamanan. Berbagai macam studi dan penelitian telah dilakukan untuk pengenalan pola gelombang otak yang ditujukan untuk keamanan atau otentikasi individu. Kekurangan yang didapatkan pada pengenalan pola sinyal EEG untuk otentikasi adalah masih perlu banyak penelitian mengenai pengenalan karakter pengganti password dan implementasi otentikasi pada aplikasi keamanan. Pada penelitian ini diusulkan suatu metode klasifikasi algoritme Voting Feature Interval 5 (VFI5) dan Otentikasi Dua-Tahap Biometrik dengan terlebih dahulu melalui proses ekstraksi ciri menggunakan metode Transformasi Wavelet. Menurut penemunya metode VFI5 mempunyai kemampuan komputasi lebih cepat daripada metode Bayes dan akurasinya lebih baik daripada metode KNearest Neighbor.
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