<![CDATA[Indonesia generates a wide range of data, including both public data and sensitive personal data. Generally, cyber attacks can occur through software vulnerabilities, human intermediaries using social engineering techniques, or combination of both. If social engineering involves software vulnerabilities, the CVSS can be used to assess software vulnerabilities and the EPSS to estimate the probability of the exploitation. However, CVSS and EPSS cannot be applied to social engineering techniques that do not involve software or merely use it as a medium without exploiting its vulnerabilities. Based on a literature review, no study has been found that develops social engineering vulnerability index at the subnational level. Therefore, this research aims to fill this gap by constructing Social Engineering Vulnerability Index for 38 Indonesian provinces in 2025 using Principal Component Analysis (PCA), preserving 79.33% of the original data's information. Subsequently, the vulnerability levels were analyzed using K-Medoids clustering, where the optimal number of clusters was based on the interpretation of Silhouette Coefficient (SC) and average Silhouette Coefficient. The index ranges from 0 to 1, representing 5 vulnerability levels. This research indicates that there are variations in social engineering vulnerability due to socioeconomic and digital ecosystem disparities]]>
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