ANALISIS SENTIMEN DEBAT CALON PRESIDEN DAN WAKIL PRESIDEN INDONESIA 2019 MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER
Keywords:Sentiment Analysis, Naïve Bayes Classifier, Debates Candidates of ThePresidential and Vice Presidential For The Republic of Indonesia In 2019
News is an interesting timely report for a large number of people. The information in the news can be a opinions or facts, both positive and negative. Sentiment analysis is a part of text mining research to classify an entity in a text document. This research was conducted by classifying sentiments using a headline dataset and news content about the debates candidates of the presidential and vice presidential for the Republic of Indonesia in 2019. Sentiment analysis in this research is divided into two classes, positive and negative. The data used was taken from the news media Detik, Kompas, Sindonews, Viva, Republika, and CNNIndonesia related to research. Weighting is done using TF-IDF and the algorithm used in this research is the Naïve Bayes Classifier algorithm with the Naïve Bayes Multinomial model. Based on the calculation that has been done, the comparison value of each debating activity is obtained where 42.3% is positive and 57.7% is negative from 130 datasets for the first debate, 48.0% is positive and 52.0% is negative from 122 datasets for the second debate, 44.0% positive and 56.0% negative from 124 datasets for the third debate, and 44.0% positive and 56.0% negative from 100 datasets for the fourth debate. While the accuracy value obtained for each debate is 76.923% for the first debate, 80% for the second debate, 84% for the third debate, and 95% for the fourth debate.
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