<![CDATA[Economic policies introduced at the beginning of President Prabowo Subianto’s administration have generated diverse public discourses reflected in online media coverage. The large volume of textual data necessitates computational approaches to extract information systematically. This study aims to identify, label, and compare major economic policy topics using topic modeling techniques, namely Latent Dirichlet Allocation (LDA) and BERTopic.The dataset consists of 1,000 economic news articles collected through web scraping from an online news portal. Text preprocessing includes normalization, case folding, cleaning, tokenization, and lemmatization. LDA was implemented using a TF-IDF representation and evaluated with the Coherence Score (c_v). BERTopic employed IndoBERT embeddings, UMAP for dimensionality reduction, and HDBSCAN for hierarchical clustering, with evaluation based on topic coherence and semantic interpretability. The results show that LDA generated eight main topics with a Coherence Score (c_v) of 0.61, indicating moderate performance but limited semantic representation, leading to overlapping topics. In contrast, BERTopic produced nine main topics with a higher Coherence Score (c_v) of 0.72 and clearer, more contextual topic labels, including fiscal policy, energy, capital markets, and economic stimulus. Overall, BERTopic outperformed LDA in extracting and labeling economic policy topics due to its superior ability to capture semantic context and form stable topic clusters.]]>
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