Kinan, Farras (2015) Deteksi Kelelahan Mental Berbasis Gelombang Otak Menggunakan Transformasi Fourier Dan Support Vector Machine. Undergraduate thesis, Institut Technology Sepuluh Nopember.
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Abstract
Dewasa ini, penyebab kematian akibat kecelakaan lalu
lintas menjadi sangat tinggi. Salah satu faktor utama penyebab
kecelakaan ini adalah kelelahan pengemudi. Hal ini dapat
terjadi karena pengemudi kurang menyadari keadaan
mentalnya yang sudah lelah. Tentu kelelahan mental dapat
menyebabkan kurangnya konsentrasi saat mengemudi.
Kelelahan mental ini dapat dideteksi dengan
menganalisis gelombang otak melalui sinyal EEG dari
pengemudi. Analisis gelombang otak ini dapat dilakukan
dengan berbagai metode. Pada Tugas Akhir kali ini, penulis
melakukan deteksi kelelahan mental berbasis gelombang otak
menggunakan transformasi fourier dan support vector
machine. Data sinyal EEG akan dilakukan ekstraksi fitur
menggunakan Transformasi Fourier. Lalu, hasil ekstraksi ini
akan digunakan untuk proses klasifikasi dengan metode
Support Vector Machine.
Berdasarkan hasil percobaan, klasifikasi kelelahan
mental menggunakan Support Vector Machine dengan linear
viii
kernel didapat rata-rata akurasi sebesar 85%. Dari hasil yang
diperoleh dari uji coba dapat disimpulkan bahwa kombinasi
metode yang digunakan pada Tugas Akhir ini mampu
melakukan klasifikasi sinyal EEG untuk kelelahan mental.
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MENTAL FATIGUE DETECTION BASED ON EEG
USING FOURIER TRANSFORM AND SUPPORT
VECTOR MACHINE
Student’s Name : FARRAS KINAN
Student’s ID : 5111100161
Department : Teknik Informatika FTIF-ITS
First Advisor : Prof. Ir. Handayani Tjandrasa, M.Sc.,
Ph.D.
Second Advisor : Dini Adni Navastara, S.Kom., M.Sc.
Abstract
These days, deaths caused by road accidents raise at
higher rates. One of the main factors that influences these
situations are driver drowsiness. An accident can happen if
the driver does not realize that he or she is tired. Mental
fatigue can the drivers lose their concentration.
Mental fatigue can be detected by analyzing the brain
wave via EEG signal from the driver. There are many methods
that could be applied to analyze brain wave. In this
undergraduate theses, the writer propose mental fatigue
detection based on EEG using Fourier Transform and Support
Vector Machine. EEG signal’s features are extracted using
Fourier Transform. Then, the result of feature extraction
process will be used for classification process using Support
Vector Machine.
By applying these methods, mental fatigue
classification using Support Vector Machine with linear
kernel, the writer obtains result the average of accuracy of
85%. From the results, it can be the concluded that
x
combination of methods that used in this undergraduate theses
can do the classification of the mental fatigue EEG signal.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSIf 004.019 Kin d |
| Uncontrolled Keywords: | Kelelahan Mental, EEG, Transformasi Fourier, Support Vector Machine. |
| Subjects: | Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) |
| Divisions: | Faculty of Information and Communication Technology > Informatics > 55201-(S1) Undergraduate Thesis |
| Depositing User: | Mr. Tondo Indra Nyata |
| Date Deposited: | 14 May 2019 04:16 |
| Last Modified: | 14 May 2019 04:16 |
| URI: | http://repository.its.ac.id/id/eprint/63023 |
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