Stunting is a condition characterized by a height or body length below -2 SD according to WHO standards, caused by insufficient nutritional intake or recurrent infections, particularly during the First 1000 Days of Life (HPK). Stunting can affect physical and cognitive development, as well as increase the risk of diseases in adulthood. This study aims to develop a Web-Based Expert System for Early Diagnosis of Stunting in Toddlers using the Naïve Bayes method. The system is expected to assist parents and posyandu (integrated health post) workers in improving awareness of nutrition and child growth in an integrated manner, thereby enabling faster and more efficient early detection of stunting. The system diagnoses stunting status by utilizing toddler data, including name, gender, age, weight, height, and health condition, while also providing recommendations for prevention and intervention. Based on the testing results, the system successfully generated outputs that matched all tested cases. This was proven by an accuracy rate of 100% using Expert Judgement and Blackbox testing, with all features functioning properly.