Classification of Nutritional Status Using the Fuzzy Mamdani Method : Case Study at Banjar City Hospital

Abstract

The problem of nutritional status in adults requires accurate and adaptive classification methods. This study aims to develop a decision support system using the Fuzzy Mamdani method to classify nutritional status based on Body Mass Index (BMI). A dataset consisting of 237 anthropometric records from Banjar City Regional General Hospital was utilized. The system applies five fuzzy rules to map BMI values into nutritional categories: malnutrition, underweight, normal, overweight, and obesity. The classification process involves fuzzification, inference, and defuzzification using the centroid method. System performance evaluation shows an overall accuracy of 91.13%, with the highest classification precision achieved in the normal category (98.54%) and the lowest in the malnutrition category (30.77%). The results demonstrate that the Fuzzy Mamdani method is effective for nutritional classification, although refinement is needed for underrepresented categories. This system can serve as a useful tool for supporting clinical decision-making in public health services.The problem of nutritional status in adults requires accurate and adaptive classification methods. This study aims to develop a decision support system using the Fuzzy Mamdani method to classify nutritional status based on Body Mass Index (BMI). A dataset consisting of 237 anthropometric records from Banjar City Regional General Hospital was utilized. The system applies five fuzzy rules to map BMI values into nutritional categories: malnutrition, underweight, normal, overweight, and obesity. The classification process involves fuzzification, inference, and defuzzification using the centroid method. System performance evaluation shows an overall accuracy of 91.13%, with the highest classification precision achieved in the normal category (98.54%) and the lowest in the malnutrition category (30.77%). The results demonstrate that the Fuzzy Mamdani method is effective for nutritional classification, although refinement is needed for underrepresented categories. This system can serve as a useful tool for supporting clinical decision-making in public health services.