Sentiment Analysis of the Tapera Law on Platform X Using Naive Bayes Algorithm
DOI:
https://doi.org/10.35870/ijsecs.v4i3.3077Keywords:
Tapera Law, Sentiment Analysis, Naïve Bayes Method, TwitterAbstract
The implementation of the 2016 Public Housing Savings Law (UU Tapera) aims to help legal and informal workers have decent houses through the management of housing savings funds by BP Tapera. However, when implemented, this program experienced obstacles amidst various problems including the transparency of the fund collection and management system, the unevenness of benefit provision, and variations in public perception. Sentiment analysis was conducted on Twitter data for sentiment regarding the Tapera Law to obtain public perception with Naïve Bayes. This approach classifies sentiment into positive, negative, and neutral. The accuracy of the Analysis Results was 62.47% (343 negative sentiments, 23 neutral, and finally 32 positive sentiments). The public mostly has negative sentiment towards the Tapera Law, because many of them are afraid of losing justice and effectiveness with this policy. These results underline the need to intensify transparency and communication of the benefits of the Tapera Law and its mechanisms to increase public acceptance and trust.
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