<![CDATA[The growth of digital media and online news in Indonesia has generated a massive volume of information that continues to expand daily. This situation makes it difficult to identify public issues quickly and accurately, as manual news monitoring requires significant time, effort, and resources. Furthermore, the multitude of news sources with varying editorial focuses results in fragmented information that is challenging to analyze comprehensively. Consequently, an automated approach is needed to detect and monitor public issues within large datasets of online news. This study aims to develop a public issue detection model for national online news using the Latent Dirichlet Allocation (LDA) method. Research data was obtained via web scraping from CNBC Indonesia, Detik.com, Kompas.com, and Liputan6.com between January and December 2025, yielding 149,335 news headlines; after preprocessing, 146,557 clean data points remained. Topic modeling was performed using LDA, followed by analysis involving temporal trends, spike detection, media comparisons, and sentiment analysis based on the InSet dictionary. The results demonstrate that the LDA model successfully identified 16 key topics representing various public issues. The analysis revealed differences in reporting focus across media outlets, spikes in specific issues during certain periods, and a predominance of negative sentiment across most topics. These findings indicate that the proposed approach is capable of supporting the automated and structured monitoring of public issues.]]>
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