SENTIMENT ANALYSIS OF IPUSNAS REVIEWS USING NAIVE BAYES AND K-NEAREST NEIGHBOR ALGORITHMS
DOI:
https://doi.org/10.34288/jri.v8i3.528Keywords:
sentiment analysis, Naive Bayes, K-Nearest Neighbor, text mining, iPusnasAbstract
This study aims to analyze the sentiment of iPusnas application user reviews using the classification method with the K-Nearest Neighbor (KNN) and Naive Bayes algorithms. The data used are secondary data in the form of user reviews obtained from the Google Play Store in the period of January to December 2025 totaling 2415 reviews. This study uses a text mining approach with text preprocessing stages, feature extraction using the Bag of Words (BoW) method, and sentiment clas, sification using the Naive Bayes and K-Nearest Neighbor (KNN) algorithms. Model evaluation uses Test and ScoreConfusion Matrix, and Word Cloud. The results show that the Naive Bayes method has better performance with an accuracy value of 0.705 compared to K-Nearest Neighbor (KNN) with an accuracy value of 0.615. Testing the K parameter in the KNN algorithm shows that the best K value is obtained at K = 6 with an accuracy of 0.615. This study shows that Naive Bayes is more effective in classifying sentiment in iPusnas application user reviews.
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