RSM-based optimization of analog rice production using kimpul tuber

Indonesia ranks third globally in rice consumption, with an annual per capita intake of 139.15 kg—almost double that of other Asian countries (65–70 kg). Such dependency on rice contributes to high prices, unstable supply, and vulnerability to poverty and malnutrition. One critical nutritional issue in Indonesia is Protein-Energy Malnutrition (PEM), particularly among toddlers, manifested in conditions such as kwashiorkor and marasmus. The contribution of this research is to demonstrate how statistical modeling can be used to predict nutritional responses (protein and energy) from changes in raw material formulations and to expand the application of RSM in the field of local foods based on tubers and soybeans. Kimpul flour contains up to 145 g of carbohydrates per 100 g, compared to only 79.3 g in conventional rice, while soybeans are rich in protein. This study aims to optimize the formulation of composite flour from kimpul and soybean using Response Surface Methodology (RSM). A Box–Behnken three-variable design was applied with kimpul flour (X1), soybean flour (X2), and tapioca (X3) at three concentration levels, generating 15 treatment combinations to determine the optimum protein content and energy value. The optimum points for each variable of energy content and protein content are the variables of cassava flour concentration, soybean flour concentration, and tapioca concentration, respectively, which are 135,5 grams; 63,16 grams; 1,52 grams.