Comparative Analysis of Naive Bayes Model Performance in Hate Speech Detection in Media Social Twitter
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CONCLUSION The conclusion that can be drawn from the research is Sentiment Analysis on Social Distancing Social Distancing and Physical Distancing on Twitter Social Media Using the
Comments and tweets can have words of a varying formats and style, and using character-level would be the most effective to process this text and extract the most important words
Based on the explanation above, this research is aimed to detect the depression in social media Twitter by detect the tweet and give recommendation if the method succeeded to process
RESEARCH METHODOLOGY 2.1 Research Phases The system used in this research is a system for sentiment analysis on Twitter social media on climate change in Indonesia using the
CONCLUSION Researchers concluded that the results of the email phishing classification process using the Naïve Bayes Classifier algorithm can help classify data that includes phishing
This means that of the four digital payments that have the best digital payment services when viewed from sentiment analysis on Twitter social media using the Naïve Bayes Classifier
CLASSIFICATION PERFORMANCE OF FLOOD REPORTS USING K-MEANS SMOTE AND MULTINOMIAL NAÏVE BAYES Presisi Recall F1-score 68,01% 67,44% 66,04% The third test on forest fire
This system is also equipped with sentiment analysis process using Naïve Bayes which is optimized by the PSO method, based on the results of testing the sentiment analysis method of the
