Data Driven Analysis of Borobudur Ticket Sentiment Using Naïve Bayes.

A data-driven analysis of public sentiment toward the proposed increase in Borobudur Temple entrance fees reveals that the Naive Bayes algorithm outperformed the Decision Tree method, achieving a test accuracy of 100% compared to 35.97%. This study leveraged Twitter data to classify sentiments using these algorithms, which are renowned for their efficiency and effectiveness in text classification tasks. The findings highlight the potential of Naive Bayes for sentiment analysis, offering valuable insights into public opinion on significant cultural and economic issues.

Kundana, D., & Chairani. (2023). Data Driven Analysis of Borobudur Ticket Sentiment Using Naïve Bayes. Aptisi Transactions on Technopreneurship (ATT), 5(2sp), 221–233. https://doi.org/10.34306/att.v5i2sp.353