LDA Topic Modeling: Twitter-Based Public Opinion on Indonesian Ministry of Finance

People in the modern era use social media daily to exchange opinions regarding government policies, such as discussions related to the Indonesian Ministry of Finance (Kemenkeu). This study aims to analyze the topics of discussion about the Ministry of Finance on the Twitter platform, now known as 'X', and to determine the results of more effective preprocessing. The data in this study was taken from Twitter using the Tweet Harvest Tool with the keyword 'Ministry of Finance' from January 2024 to July 2024. The data is then processed through cleaning, preprocessing, calculation of coherence values, LDA modeling, and visualization. The preprocessing process includes several scenarios to compare the best results that are easy for the reader to understand. The highest coherence value obtained is 0.572250 by using stemming from NLTK library. The most effective preprocessing results are normalization, tokenization, stopwords, and stemming using Sastrawi. Modeling is done to find latent topics through LDA topic modeling techniques. Visualizing the intertopic distance map provides information on the distance between each topic. The results show that the distance between one topic and another has a variety of distance variations. This study shows that social media platforms can serve as a source of evaluation for the Indonesian government. The findings of these topics are helpful as insights for readers and the Kemenkeu. Finally, the analysis identified several key topics in public discussion, including fiscal policy, budget transparency, and the Ministry of Finance's performance in addressing current economic issues.