The comprehensive issue on the Keparakan Indonesia riverside is the dilemma faced by stakeholders regarding the best solution for the housing. On one hand, there is a perception that the top-down solution is to demolish the periphery housing to create broad access for volcanic flood mitigation while maintaining the interior thermal comfort resulting from solar heat gain. On the other hand, the citizens aspire to keep their previous housing to maintain their craftsmanship home industry, despite the risk of ecological disasters. This dilemma poses a potential clash between stakeholders if not solved properly. The proposed Artificial Intelligence (AI) definition on socio-ecological system resilience aims to address this issue by considering the physical, social and cultural aspects, leveraging the opportunity to hold the development and ensure the safety of the community. Experiments using AI tools such as LLM and computer vision for riverside housing; flood-safe; disaster resilience; tropical sustainability. the research aims to utilize AI tools to analyse riverside area both as structural buffer for disaster mitigation and to enhance the livelihood, building the economic development of the craftsmanship industry. A neighbourhood model then generated based on the result of content analysis applying guidelines of pitched roof; pro-riverside building orientation; vegetations shade; flood-buffer pedestrian lane and resulting on 28% reduction of solar heat gain. The result promotes energy efficiency by reducing cooling load in the housings interior while providing volcanic flood mitigation and socio-ecological resilience due to craftmanship enhancement. Furthermore, the study lists the limitations and future research.

Keywords:Artificial Intelligence; Housing morphology; energy efficiency; riverside housing.