<![CDATA[Abstract — In the era of widespread use of the internet today, thenumber of consumers who wrote the opinion and experience ofonline continues to increase. Read the review as a whole can betime consuming, however, if only a few reviews that read, then theevaluation will be biased. Sentiment analysis aims to address thisproblem by automatically classifying user review be positive ornegative opinion. Naïve Bayes classifier is a popular machinelearning techniques for text classification, because it is very simple,efficient and has a good performance in many domains. However,Naïve Bayes has the disadvantage that is very sensitive to featuretoo much, resulting in a classification accuracy becomes low.Therefore, in this study used the integration method of featureselection, namely Information gain and Genetic algorithm in orderto improve the accuracy of Naïve Bayes classifier. This researchresulted in the classification of the text in the form of positive ornegative review of the book. Measurement is based on the accuracyof Naive Bayes before and after the addition of feature selectionmethods. The evaluation was done using a 10 fold cross validation.While the measurement accuracy is measured by confusion matrixand ROC curves. The results showed an increase in the accuracy ofNaïve Bayes from 78.50% to 84.50%.]]>
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