<![CDATA[Film is a subject of interest by a large number of people among the social networking community who have significant differences in their opinions or sentiments. Sentiment analysis or opinion mining is one solution to overcome the problem to classify opinions or reviews into positive or negative opinions automatically. The technique used in this study is Naive Bayes and Support Vector Machines (SVM). Naive Bayes has advantages that are simple, fast and have high accuracy. Whereas SVM is able to identify a separate hyperplane that maximizes the margin between two different classes. The results of the sentiment classification in this study consisted of two class labels, namely positive and negative. The value of accuracy produced will be a benchmark for finding the best testing model for sentiment classification cases. Evaluation is done using 10 fold cross validation. Accuracy measurements were measured by confusion matrix and ROC curve. The results showed that the accuracy value for the Naive Bayes algorithm was 84.50%. While the accuracy value of the Support Vector Machine (SVM) algorithm is greater than Naive Bayes which is equal to 90.00%.]]>
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