ANALISIS SENTIMEN PEMINDAHAN IBU KOTA NEGARA DENGAN KLASIFIKASI NAÏVE BAYES UNTUK MODEL BERNOULLI DAN MULTINOMIAL

Abstract

Text mining is a variation on a field called data mining that tries to find interesting patterns from large databases. Indonesian President affirmed that the capital would be moved to East Kalimantan on August 26, 2019. That planning would receive pros and cons from public. Sentiment analysis is part of text mining that typically involves taking data from opinion, comment, or response. Sentiment analysis is the choice to do on this topic to get results about the public’s opinion. As the most used social media in Indonesia, Youtube is able to be data source by crawling the comments on a video uploaded by Kompas TV channel. Those comments were crawled on October 15, 2019, and selected 1500 latest comments (August 26 – October 12, 2019). The selected comments get transformed by using data pre-processing technique that involves case folding, removing mention, unescaping HTML, removing numbers, removing punctuation, text normalization, stripping whitespace, stopwords removal, tokenizing, and stemming. Labeling of sentiment class uses the sentiment scoring technique. The number of negative comments is 849, while the number of positive comments is 651. The ratio between training data and testing data is 80%: 20%. The classification method used to do sentiment analysis is the Naive Bayes Classifier for Bernoulli and Multinomial model. Bernoulli model only uses occurrence information, whereas the multinomial model keeps track of multiple occurrences. The results show that Bernoulli Naïve Bayes has a 93,45% level of sensitivity (recall) and Multinomial Naïve Bayes has a 90,19% level of sensitivity (recall). It means that both Bernoulli and Multinomial have a good result for this research.