<![CDATA[Online news media has become a primary channel shaping public opinion toward government performance, making the ability to monitor news coverage a strategic necessity for institutions such as the Department of Communication and Information (Diskominfo) of West Kalimantan Province. However, the large volume of news coverage renders manual monitoring inefficient and prone to subjectivity. This study aims to design and build a web-based online news sentiment monitoring system named SentimenIQ, which integrates automatic news collection through RSS Feed, sentiment classification using the IndoBERT model, and presentation of analysis results within a single service flow. The system was developed using the Waterfall method with a microservice architecture separating the main Laravel application from the Python FastAPI inference service. Functional testing was conducted using the black box testing method, while classification performance was measured using accuracy, precision, recall, and F1-score derived from a confusion matrix on 120 labeled news articles. The functional testing results show that all system features operated according to requirement specifications, while the classification testing produced an accuracy of 89.17% with a weighted average F1-score of 89.13%. These results prove that the IndoBERT model can be integrated into a web-based operational system and relied upon to monitor news coverage in near real-time, thus serving as a reference for developing similar systems in other government institutions.]]>
