Rafiq, Abid (2024) Sentiment Analysis of Public Views Towards The Public Housing Savings (Tapera) Policy on Twitter Using The Support Vector Machine Method. S1 Undergraduate thesis, Universitas Darussalam Gontor.
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Abstract
People's Housing Savings (Tapera) is a policy implemented by the government to help the community, especially the Low-Income Community (MBR) in meeting the need for decent housing. This policy has drawn many pros and cons for the community, including on social media such as Twitter, because it is considered that Tapera will only increase the financial burden on the community. This study aims to conduct a sentiment analysis of the classification of public views on the Tapera policy on social media Twitter. The research dataset was taken via Twitter using the crawling technique which was then classified into positive, negative, and neutral sentiments. The method used is Support Vector Machine (SVM) and the results of the study were evaluated using Cross Validation as a validator. The results showed that the SVM model of this study produced an accuracy of 86% with a precision value of 87%, a recall of 86%, and an f1-score of 86%. In addition, it was shown that the negative class had the largest number of 1674 (57.0%), followed by the neutral class with 791 (27.0%), and the positive class with the lowest number of 470 (16.0%) data. With the results of this study, it is hoped that it can be additional information for policy makers to reconsider and improve the implementation of the Tapera policy and add references for future research related to sentiment analysis, especially those related to Tapera and SVM.
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