Farmers face various risks such as production risks in the use of technology, pests, climate change and natural disasters. Farmers in disaster-prone areas have different responses depending on their behavior towards the risks posed. The main problem in this research is how farmers behave towards production risks due to flooding and the technical efficiency of rice farming in flood-prone areas. The aim of this research is to analyze farmers' behavior towards production risks due to flooding and the technical efficiency of rice farmers in flood-prone areas. The results of this research will provide important information for policy simulations that the government can implement towards farmers affected by natural disasters and for sustainable disaster mitigation strategies. The novelty of this research is that it combines two theories, namely risk behavior and agricultural technical efficiency in producing disaster mitigation strategies. The research location was determined purposefully in Pasuruan and Bojonegoro Regencies. The data in this research are primary and secondary data with the sample in this research being farmers. The sampling technique in this research is a multi-stage cluster sampling technique. The analysis method in this research uses Just Pope. and the Cobb-Douglas production function model with the Stochastic Production Frontier approach. The target of these research findings is a model of the types of behavior regarding the risks of farmers who are flood victims, as well as the level of technical efficiency of rice farming and the factors that influence it. The expected findings are policy recommendations regarding disaster mitigation from economic and agricultural risk aspects to create sustainable agriculture.

This research aims to investigate the intricate interplay between Artificial Intelligence (AI), Organizational Databases, Risk Management, and profitability within the Indonesian banking sector. Employing a quantitative research design, the study seeks to understand the mediating role of Risk Management in the relationships among AI, Organizational Databases, and profitability. The key findings underscore the pivotal importance of Risk Management as a mediator, emphasizing the necessity for a comprehensive risk-aware strategy in optimizing the impact of AI and high-quality databases on financial performance. The research contributes theoretically by providing nuanced insights into the dynamic relationships between technology adoption, risk mitigation, and financial success. Limitations include contextual specificity and temporal constraints, acknowledging the need for caution in generalizing findings. The practical contributions involve recommendations for organizations to continuously monitor and adapt their AI implementations and risk management strategies. The study sets the stage for further research, advocating for cross-industry studies, longitudinal analyses, and in-depth case studies to enhance the understanding of these complex dynamics. Overall, this research provides actionable insights for practitioners and contributes to the ongoing discourse on the evolving landscape of technology, risk management, and financial success.