Performance of SVM in Classifying the Quartile of Computer Science Journals

Classification is a systematic grouping of objects or objects or certain patterns based on the similarity of characteristics. One method that can be used in classifying data is Support Vector Machine. Support Vector Machine is a technique for finding hyperlines that separate two sets from two different classes. The advantage of this method is that computing is fast in determining distance using support vector. In this study, the Support Vector Machine method was implemented to classify the journal quartile on ScimagoJR. Classification is carried out with 1441 data sets. The classification results show an accuracy of 61.09%.