Sri Redjeki, Setyawan Widyarto
Sentiment Analysis to Identify Public Opinion for Zakat Implementation in Indonesia Using Machine Learning Algorithms
This research aims to explore and identify public opinion related to Zakat in Indonesia by utilizing big data technology through sentiment analysis. The source of Twitter social media public opinion data is used in the research. Proper keywords need to be determined before crawling Twitter data collections. The Twitter data collected in the research was 1060 Twit at the beginning of the year 2020. Indonesian community opinion identification, a part of the machine learning method, was a method used in training and testing data requirements. Opinions are classified into 3 classes i.e. positive, neutral and negative. The identification shows that the Random Forest Classifier method and the Naïve Bayes method provide an accuracy value of 86% and 81%. While the method that produces the lowest accuracy is the Support Vector Machine method of 53%. The accuracy results show that the sentiment of analysis can be used as an "early warning system" for the decision-makers of Zakat in Indonesia.
Machine Learning, Opinion, Sentiment Analysis, Twitter, Zakat
Cite this paper
Sri Redjeki, Setyawan Widyarto. (2022) Sentiment Analysis to Identify Public Opinion for Zakat Implementation in Indonesia Using Machine Learning Algorithms. International Journal of Computers, 7, 38-44