<![CDATA[Sentiment analysis is the classification of texts in several categories, usually positive, negative, happy or sad. In recent years, this technique has been used to analyze online product reviews, online news, general elections, disasters, stock markets, and social media, especially twitter. The results of the sentiment analysis can be used as one of the decisions making considerations. In helping to analyze the results of sentiments, visual analytics are used that can help to navigate data, compare various data sets, and explore data distribution. This research aims to analyze sentiment in Bahasa Indonesia using Tweepy and TextBlob as a python library to access and classify Tweets and with the help of visual analytics to explore and observe the distribution of Tweets based on geographical location, especially in provinces in Indonesia. In addition, this research also provides a comparison of the results of the validation of TextBlob using the Naïve Bayes and SVM algorithms with the results of each accuracy of 62.26% and 69.18% which are higher accuracy compared to SentiWordNet.]]>
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