Analysis of General Election Campaign Topics of Candidates for President and Vice President of the Republic of Indonesia Using Lattent Dirichlet Allocation on Social Media Data

Abstract

The election of the President and Vice President of the Republic of Indonesia 2024 is an important moment for the Indonesian people in determining future leaders. Social media plays a major role as a platform used to deliver campaign programs by presidential and vice presidential candidates. In conducting social media analysis, one approach that can be used is using topic modeling. Topic modeling produces output in the form of topics of conversation from a document, one of the models is Latent Dirichlet Allocation (LDA). In previous research, LDA has been widely used to search for topics of conversation on social media. This research analyzes the campaign programs of the 2024 Presidential and Vice Presidential Candidates of the Republic of Indonesia on social media using the Latent Dirichlet Allocation (LDA) method for intent classification in campaign program detection and sentiment analysis to check sentiment analysis. Data from the Twitter social media platform during the campaign period was processed and analyzed with LDA to understand the trend of campaign topics.