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.
Collaboration network of the authors
13 authors ·
25 co-authorship links
· 17 across different organisations
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Atma Jaya University Yogyakarta
Universitas Gadjah Mada
National University of Malaysia
King Abdulaziz University
University of Surabaya
Universiti Teknikal Malaysia Melaka
Muria Kudus University
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Same organisation
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