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Abdul Rauf
Informatika - Institut Teknologi PLN
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Klasifikasi Sinyal EEG Menggunakan Model K-Nearest Neighbor Untuk Pengenalan Kata Yang Dibayangkan Abdul Rauf; Efy Yosrita; Rosida Nur Aziza
PETIR Vol 15 No 1 (2022): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/petir.v15i1.1335

Abstract

Locked in syndrome (LIS) is a condition of complete paralysis in which people with LIS are conscious but unable to move or communicate verbally except to move their eyes or blink. One way that can help LIS sufferers to communicate and interact is through recording brain signals called Electroencephalogram (EEG). In this study, the data from the recording of the EEG signal has gone through the extraction stage. The extracted data is preprocessed and classified using the K-Nearest Neighbor (K-NN) algorithm to be visualized using a web-based application. The results of the classification using the K-Nearest Neighbor algorithm with a value of K = 1 resulted in 82% accuracy, 82% precision and 82% recall. Keywords: LIS, EEG, K-Nearest Neighbor.
Co-Authors Efy Yosrita Rosida Nur Aziza