<![CDATA[This journal's abstract addresses sentiment analysis of public opinion in relation to the odd-even system's implementation on Twitter, utilizing the K-NN and Naïve Bayes Classifier algorithms. The odd-even system was discussed in tweets by Twitter users, which served as the source data. The tweets were categorized into three sentiment categories: positive, negative, and neutral. The analysis's findings indicate that, of the total number of tweets gathered, 391 were categorized as neutral, 50 as negative, and 59 as positive. In addition, it was found that the Naïve Bayes algorithm and the K-Nearest Neighbor algorithm both had an average accuracy rate of approximately 79.72%. This suggests that both algorithms do similarly well when it comes to classifying the sentiment of the tweets under discussion. With respect to sentiment analysis of public opinion on the Twitter platform, this conclusion clarifies the performance comparison between the Naïve Bayes and K-Nearest Neighbor algorithms.]]>
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