Optimization of Nutritional Meal Allocation Using the Greedy Algorithm : A Data – Driven Approach for Food Security in Indonesia - Irwansyah Sitorus; Aprilia, Katharina Tyas; Muhammad Rasyid Ridha; Ricky Martin Ginting (Journal of Computer Science and Research (JoCoSiR), 2024)
Food security and nutrition programs play a crucial role in improving public welfare, particularly in developing countries such as Indonesia. Efficient allocation of limited government resources to regions most in need remains a key challenge in reducing poverty and malnutrition. This study applies the Greedy Algorithm as a computational optimization method to determine the most effective and equitable distribution of nutritional meal program budgets cross Indonesian provinces. The algorithm prioritizes provinces with higher poverty rates and greater nutritional needs while ensuring that the total expenditure does not exceed the national budget constraint. By employing a data-driven approach and calculating the value-to-cost ratio for each province, the algorithm selects allocations that yield the maximum nutritional impact per unit of cost. The results indicate that the Greedy-based allocation model improves efficiency by approximately 18–25% compared to traditional allocation methods. This approach offers a transparent, adaptable, and computationally efficient framework that can support policymakers in enhancing food security, promoting social equity, and advancing sustainable development goals.