<![CDATA[In the era of big data and digital broadcasting, organizations face increasing challenges in transforming large-scale broadcasting datasets into actionable insights for effective decision-making. This study addresses the need for an integrated decision support framework that combines broadcasting data mining and interactive visualization to improve the interpretation of complex data patterns. The objective of this research is to develop an integrated approach that applies data mining techniques to broadcasting-related data, such as audience behavior, content performance, engagement patterns, and multi-source media data, supported by visual dashboards for strategic analysis. The method employed includes clustering, classification, and association rule mining to identify meaningful patterns, audience segments, trends, and anomalies within broadcasting datasets. These analytical results are then presented through interactive visualization dashboards that enable stakeholders to explore insights more efficiently and make data-driven decisions. The results show that integrating broadcasting data mining with visualization improves the speed, accuracy, and clarity of insight extraction compared to conventional analytics approaches. User evaluation also indicates that visualized analytical outputs enhance stakeholder understanding of complex broadcasting data and support more accurate strategic decisions. The conclusion of this study confirms that the integration of broadcast- ing data mining and visualization within a big data decision support framework can bridge the gap between raw media data and practical decision-making. This research contributes to the development of more adaptive, efficient, and human centered decision support systems for broadcasting industries and digital media organizations.]]>
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