Islamophobia is formed by "Islam" with "-phobia" which means "fear of Islam". This shows the view of Islam as "other" and can threaten Western culture. The recent horrific terror attack that took place at the Christchurch mosque in New Zealand, is the result of allowing an attitude of hatred towards Islam in the West. Twitter is social media that allows users send real-time messages and can be used for sentiment analysis because it has a large amount of data. The lexical based method using VADER is used for automatic labeling of crawling data from Twitter. And then compare supervised machine learning Naïve Bayes and SVM algorithm. Addition of SMOTE for imbalanced data. As result, SVM with SMOTE is proven the highest performance value and short processing time.
The pandemic that occurred in Indonesia has not yet subsided and far from under control. Indonesian Ministry of Health is most appropriate person to responsible for providing an explanation of actual situation and extent to which state has handled it. However, he has rarely appeared in public lately to explain about handling of Covid-19 pandemic. In response, many people are pros and cons come to give their opinions and feedback. The increasing use of internet during pandemic, especially on social media, where one of them is Twitter, which is a means of expressing opinions. Posting tweets is a community habit to assess or respond to events, as well as represent public's response to an event, especially Ministry of Health steps and policies in handling and breaking chain of Covid-19 pandemic.The tweet posts were taken only in Indonesian-language and also related to performance of Government, especially Ministry of Health. After that, a label is given so that sentiment of tweets is known. To test results of these sentiments, an algorithm is used by comparing two methods of Support Vector Machine (SVM) and Naïve Bayes (NB). Validation was carried out using k-Fold Cross Validation to obtain an accuracy value. The results show that accuracy value for NB algorithm is 66.45% and SVM algorithm has a greater accuracy value of 72.57%. So it can be seen that SVM algorithm managed to get the best accuracy value in classifying positive comments and negative comments related to sentiment analysis towards Ministry of Health. Keywords—Support Vector Machine, Naïve Bayes, Analisis sentimen, K-Fold Cross Validation
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