Valonia Inge Santoso, Gloria Virginia, Yuan Lukito


The quality of lectures can be determined by some feedbacks from students. From the feedbacks, we can give appreciations for those lectures who get good feedback from students, and evaluations for those who get bad feedback. The problem is classifying large size of feedbacks manually isn t effective and took a lot of time. Therefore, we need a system that can classify feedbacks automatically. These feedbacks will be classified into positive, negative, and neutral, usually called as sentiment analysis. Sentiment analysis implementation can be done by several methods, one of them that has a good accuracy is Support Vector Machine (SVM). SVM performance in this research is measured with the level of accuracy. The number of accuracy indicate the success level of system. The conclusion of this research is factors that affects the accuracy. The factors are the range of each classes and number of unique words in the training document.


sentiment analysis, support vector machine, inverse matrix


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Jurnal Transformatika : Journal Information Technology  by  Department of Information Technology, Faculty of Information Technology and Communication, Semarang University  is licensed under a  Creative Commons Attribution 4.0 International License.