<![CDATA[One of the main challenges in disaster mitigation is delivering information that various stakeholders, including the public, policymakers, and emergency response teams, can easily understand. Visualization of disaster damage serves as an intuitive and informative tool to support better decision-making during emergencies. It also enhances public and stakeholder understanding of risk levels, thereby improving preparedness for future disasters. This study develops a disaster damage visualization model integrated with a decision-support system, using Multiple Criteria Decision-Making (MCDM) techniques. The model applies the CILOS method to determine objective weights of disaster criteria by evaluating the influence and loss of importance when a criterion is optimized. These weights are then used in the MARCOS method to perform ranking analysis, combining normalization and ideal-solution concepts to ensure a robust evaluation. The results demonstrate high reliability, with a Spearman rank correlation coefficient of 0.96, indicating strong agreement between the CILOS-MARCOS ranking results and the official JITUPASNA priority document. The final rankings are visualized in an interactive 3D format using the Unity Engine, allowing users to explore affected areas spatially, view detailed sectoral damage values, and compare subdistrict-level data. Integrating MCDM-based analysis with 3D visualization provides a novel, data-driven approach to enhance the effectiveness and transparency of disaster mitigation and rehabilitation planning. This model contributes to more adaptive and efficient strategies by linking disaster data, multi-criteria evaluation, and interactive visualization for comprehensive decision support.]]>
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