<![CDATA[The growing volume of online news makes it difficult for the Sumatera Ekspres editorial team to manually track popular topics in a timely manner. This study implements the BERTopic algorithm based on IndoBERT to automatically model trending topics of online news, following the CRISP-DM stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Data were collected through web scraping from eight national news portals and YouTube, then processed through case folding, cleaning, tokenization, and stopword removal before being modeled using IndoBERT as the embedding, UMAP for dimensionality reduction, HDBSCAN for clustering, and c-TF-IDF for keyword extraction, complemented by a trend_score metric to identify topics that are genuinely gaining momentum. The main contribution of this study lies in integrating more heterogeneous multi-source data than prior work and introducing the trend_score metric as a more accurate indicator of momentum, realized as a ready-to-use dashboard for the editorial team. Testing across four data periods (1, 7, 14, and 30 days) shows that the model consistently produces topics of good coherence and diversity quality. The results were implemented into a website-based dashboard built with FastAPI and SQLite that presents trending topic visualizations, word cloud, and news headline recommendations, which has been tested and confirmed to function as required, making it suitable as a decision-support tool for the editorial team to monitor popular issues quickly, efficiently, and in a data-driven manner.]]>
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