Implementasi Logika Fuzzy Mamdani Dalam Klasifikasi Kategori Berat Badan Berbasis IMT - Ambon, Matelda Yunanta; Lili, Juniver Veronika; Bandhaso, Victor; Wati, Masna; Septiarini, Anindita (Infotek: Jurnal Informatika dan Teknologi, 2025)
Body Mass Index (BMI) is a common method used to classify body weight based on the ratio of weight to height. However, its accuracy is often questioned because it does not account for age and gender, which also influence body composition. This study implements the Mamdani fuzzy logic approach to classify body weight based on BMI while considering age and gender. The system utilizes fuzzy membership functions to dynamically determine categories such as Underweight, Normal, Overweight, and Obese, and is developed using the Python programming language with interactive visualizations. Testing results show that the system can provide more adaptive and personalized classifications. Defuzzification values, such as 59.48 for a BMI of 24.22, indicate a classification consistent with WHO standards—namely, the Normal category. The system also demonstrates that classification results may vary for the same BMI when age or gender differs, as illustrated in multi-demographic visualizations. The centroid defuzzification method produces stable and representative outputs. Evaluation results show high accuracy, consistency in rule base, and an ability to handle data uncertainty. Thus, this system serves as a more flexible alternative to conventional methods in body weight classification.