The huge resources need effectiveness and efficiency, it can be processed by machine
learning. There have been many studies conducted using machine learning method and produced
quite good performance in sentiment analysis. Some machine learning methods that are often
used in general are Naive bayes (NB), K-nearest neighbor (KNN), Support vector machine
(SVM), and Random forest methods. Mostly, KNN did not achieve better performance than
other machine learning methods in sentiment analysis. In this study, the Polarity v2.0 from
Cornell movie review dataset will be used to test KNN with Information gain features selection
in order to achieve good performance. The purpose of this research are to nd the optimum
K for KNN and compare KNN with other methods. KNN with the help of Information gain
feature selection becomes the best performance method with 96.8% accuracy compared to the
NB, SVM, and Random forest while the optimum K is 3.