Data Driven Analysis of Borobudur Ticket Sentiment Using Naïve Bayes.

Abstract

 The recent growth of social media is hugely influential and plays a significant role in various aspects of people's lives in the digital era. Twitter is a social media network that is widely used in Indonesia. Twitter users can engage in multiple activities, such as communicating with individuals and groups, writing daily activities, promoting businesses, arguing, and expressing ideas about a topic of discussion. At the beginning of June 2022, raising the entrance charge for Borobudur Temple became one of the concerns that caused a lot of conversation in the real world and on other social media platforms, including Twitter. The plan to increase the price of entrance tickets to Borobudur Temple has drawn various pro and con reactions in the community. This study analyzes public sentiment toward the planned increase in ticket prices for Borobudur Temple. Sentiment analysis of Twitter data can be implemented using a classification algorithm. The classification algorithms widely used in sentiment analysis research are Nave Bayes (NB) and Decision Tree (DT). The reason for choosing Nave Bayes and Decision Tree is because this algorithm is the most popular algorithm used to process text data classification; the process is simple, efficient, and performs well. This study's dataset source was taken from social media sites like Twitter. In comparison to the Decision Tree, which generates a test percentage of 100%, the accuracy of the Naive Bayes approach, based on the evaluation of the test results, produces the highest accuracy number. At the same time, the Decision Tree method's accuracy test yields a test accuracy value of 35.97%.