Emotion detection is a very exhausting job and need a complicated process; moreover, these processes also require the suitable data training and apropriate 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 in order to get a well data training. Furthermore, Support Vector Machine and a correct preproceesingare performed to classify the GSR data. In order 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.