An AI-Driven Policy Intelligence Framework for Transforming National Data into Evidence-Based Public Policy - Deva Yohand Pangestu; Laizza Natta Fatdaja; Hery Siswanto; Dzikrina Aqsha Mahardika (Jurnal Penelitian Sains Teknologi, 2026)
The increasing complexity of national development requires public policies that are adaptive, data-driven, and evidence-based. However, many governments, particularly in developing countries, still face significant challenges in utilizing national data effectively due to data fragmentation, limited analytical capabilities of information systems, and the underutilization of Artificial Intelligence (AI). These limitations hinder the formulation of accurate and proactive public policies. This study aims to propose a conceptual framework that integrates national data, Information Systems, and AI to support intelligent policymaking. This research adopts a Design Science Research (DSR) approach to develop an artifact in the form of the National AI-Driven Policy Intelligence Framework (NAPIF). The framework is designed using a layered architecture consisting of data, processing, and output layers, supported by AI capabilities such as pattern recognition, predictive analytics, and policy recommendation generation. The proposed model transforms fragmented data into actionable insights through an integrated system that supports decision-making processes. The results indicate that the proposed framework enhances data integration, improves analytical capabilities, and enables predictive and adaptive policymaking. Compared to conventional systems, the framework provides more comprehensive decision support and supports continuous policy improvement through a feedback-driven mechanism. The study contributes theoretically by integrating the domains of Information Systems, AI, and public policy into a unified framework, and practically by offering a strategic approach for governments to implement data-driven governance aligned with long-term development goals. This study is limited by its conceptual nature; therefore, future research is recommended to validate the framework through empirical implementation and real-world case studies.