<![CDATA[Indonesia a potential market for business because of a large number of smartphone users, especially developers of mobile applications. Each application stores allow the user to provide a review of the application used. The review is not only beneficial for prospective users of the application but also beneficial for the application developer. Review of the applications that are influenced by emotion (sentiment) are grouped or classified to determine positive and negative polarization. Therefore, it is necessary to have an application that can perform sentiment analysis for the mobile app reviews using Neighbor-Weighted K-Nearest Neighbor (NWKNN) classification method with high accuracy results. NWKNN method is able to classify mobile application review documents on the balanced data with current value of k = 20 gives the best f-measure average value of 0.9 with ratio of training data and test data 80%: 20%. However, for the imbalanced data with value of k = 45 gives the best f-measure average value of 0.797 with a ratio of training data and test data 80%: 20%. Based on the results, the effect of imbalanced data to the accuracy of the NWKNN methods by comparing NWKNN and KNN methods, it was found that the value of F-Measure NWKNN method is better than KNN method with gap of 0,27, due to the added weight on class minority overcome misclassification problem on minority class.]]>
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