<![CDATA[The Free Nutritious Food Program (MBG) is a government policy that has generated various public responses and opinions on social media. The large amount of unstructured text data. This study aims to compare the performance of the Latent Dirichlet Allocation (LDA) and BERTopic methods in identifying public issues related to the MBG program on TikTok data. The dataset used amounted to 13,538 data obtained through a scraping process based on keywords related to MBG. The research stages include text preprocessing, bigram and trigram formation, text representation using TF-IDF and embedding, topic modeling, and evaluation using coherence score and topic diversity. The results showed that the LDA method produced better evaluation performance with a coherence score of 0.5098 and a topic diversity of 0.9000. Meanwhile, BERTopic produced a coherence score of 0.4133 and a topic diversity of 0.7667, but was able to produce topics that were more contextual and semantically representative. Based on these results, LDA is superior in terms of the stability and quality of word associations between topics, while BERTopic is more effective in understanding the context of issues in short and unstructured social media data.]]>
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