Implementasi Metode Adaptive Neuro-Fuzzy Inference System (ANFIS) untuk Prediksi Status Gizi Balita Studi Kasus Wilayah Kabupaten Blitar - Kusuma, Mochammad Rizky; Chulkamdi, Mukh Taofik; Lestanti, Sri (CV. Information Technology Training Center - Indonesia (ITTC), 2025)
This study aims to develop a predictive model for the nutritional status of toddlers using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on anthropometric data. According to WHO data (2023), global nutritional problems, such as wasting and stunting, are alarming, with 45 million toddlers experiencing wasting and 149 million experiencing stunting. In Indonesia, the prevalence of stunting was recorded at 24.4%, higher than the WHO threshold of 20%. In Blitar Regency, the prevalence of stunting also increased from 14.3% (2022) to 20.3% (2023), a contributing factor being the manual recordingsystem at community health centers (Puskesmas) and integrated health posts (Posyandu). This study used data from 5,000 toddlers from the Kanigoro Community Health Center and Gogodeso Integrated Health Post (Posyandu), with 70% of the data allocated for training and 30% for testing. Model evaluation was conducted using three metrics: Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The evaluation results demonstrated the best prediction accuracy in the MSE for weight/age, height/age, and weight/height, indicating stable data vriation and sensitivity to outlier detection. This prediction system was implemented using MATLAB GUIDE, making it practical for use by healthcare professionals. The results of this study can support efforts to accelerate stunting reduction through faster and more accurate predictions of toddler nutritional status