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S. N. Neyman
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Computer Science & IT Engineering

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Journal : ComEngApp : Computer Engineering and Applications Journal

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Accuracy Improvement of Incidence Level Detection Based on Electroencephalogram Using Fuzzy C-Means and Support Vector Machine Marwan Ramdhany Edy; S. Wahjuni; S. N. Neyman
Computer Engineering and Applications Journal Vol 8 No 3 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (266.244 KB) | DOI: 10.18495/comengapp.v8i3.307

Abstract

Some jobs require that the concentration level be maintained for a long time during work time. Lack of sleep will in disruption of someone concentration level. To find out a human concentration level can be done by recording his/her brain waves. This research uses Electroencephalography (EEG) technology which functions to capture human brain waves. The focus of this study is to build a model of the detection system of a human concentration level. The research datasets are data from brain wave recording using Neurosky Mindwave Mobile which has extracted in 19 features. Data will then be labeled using cluster techniques namely Fuzzy C-Means to become data to be input into the classification process using Support Vector Machine (SVM). The classification results show an accuracy of 98.34%. That results show FCM can be used to automatically label EEG data properly.
Co-Authors Marwan Ramdhany Edy S. Wahjuni