<![CDATA[The rapid acceleration of digital transition has become an inevitable reality of the modern era. The proliferation of online communication platforms, news portals, and heterogeneous data formats has substantially increased big data volumes, leading to large-scale collections of unstructured data. This study aims to analyze dominant public policy–related topics concerning the Prabowo–Gibran administration by applying topic modeling techniques to national online news media. Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) were employed as unsupervised learning approaches to extract latent semantic structure from a corpus of 200 credible news articles collected through URL fetching using Python 3. Data preprocessing included text cleaning, tokenization, bigram and trigram construction, and the development of a dictionary and corpus. Model performance was evaluated using topic coherence metrics, yielding scores of 0.3709 for LDA and 0.68 for NMF. To examine temporal dynamics, the dataset was divided based on the official inauguration date of the president and vice president, enabling a comparative analysis of dominant topics before and after the inauguration. Topic similarity across both periods was measured using cosine similarity, with the highest similarity score of 0.663 observed between Topic 4 in the pre-inauguration period and Topic 1 in the post-inauguration period. The findings provide insights into evolving media discourse and policy-related topic trends across the two periods, demonstrating the potentials of topic modeling in analyzing large-scale unstructured news data for diverse purposes to bridge computational science and empirical evidence of social science.]]>
