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Sebastian Bagya Gunawan
Universitas Atma Jaya Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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Galvanic Skin Response Data Classification for Emotion Detection Djoko Budiyanto Setyohadi; Sri Kusrohmaniah; Sebastian Bagya Gunawan; Pranowo Pranowo; Anton Satria Prabuwono
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (617.148 KB) | DOI: 10.11591/ijece.v8i5.pp4004-4014

Abstract

Emotion detection is a very exhausting job and needs a complicated process; moreover, these processes also require the proper data training and appropriate algorithm. The process involves the experimental research in psychological experiment and classification methods. This paper describes a method on detection emotion using Galvanic Skin Response (GSR) data. We used the Positive and Negative Affect Schedule (PANAS) method to get a good data training. Furthermore, Support Vector Machine and a correct preprocessing are performed to classify the GSR data. To validate the proposed approach, Receiver Operating Characteristic (ROC) curve, and accuracy measurement are used. Our method shows that the accuracy is about 75.65% while ROC is about 0.8019. It means that the emotion detection can be done satisfactorily and well performed.
Co-Authors Anton Satria Prabuwono Djoko Budiyanto Setyohadi Pranowo Pranowo Sri Kusrohmaniah, Sri