<![CDATA[Purpose: This study aims to assess Indonesia's national resilience in the VUCA (Volatility, Uncertainty, Complexity, Ambiguity) era by evaluating the characteristics of regional resilience and their interrelationships. The research focuses on the use of computational methods, including artificial intelligence, to classify regions based on national security conditions and identify effective measures to enhance national resilience. Study Design/Methodology/Approach: The study utilizes public policy simulations and a national resilience index developed by Lemhannas. This system assesses national resilience through regional evaluations, considering both natural and social determinants. Computational methods such as K-means clustering and the Davies-Bouldin index were applied to classify regional resilience. The interactions between variables were analyzed, emphasizing the importance of regional characteristics in policy formulation. Findings: The research identified seven regional clusters through the K-means clustering method and the Davies-Bouldin index test, which effectively enhance national resilience. These clusters provide detailed insights into regional similarities, which are critical for designing targeted policies aimed at improving national resilience. The findings highlight the role of computational methods in processing extensive data and guiding policy decisions to strengthen regional and national resilience. Originality/Value: This study presents an innovative approach to assessing and enhancing national resilience in Indonesia by employing artificial intelligence and computational methods. By identifying and analyzing regional clusters and characteristics, the research offers valuable insights for policymakers to develop strategic, data-driven policies for strengthening national resilience. The integration of both natural and social determinants into the resilience assessment contributes to a more comprehensive understanding of the resilience dynamics at both regional and national levels]]>
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