<![CDATA[Stunting is one of the chronic nutritional problems that affects physical growth, cognitive development, and human productivity in the future. This condition is caused by prolonged nutritional deficiencies and health issues during the early stages of life. This study aims to develop an expert system for diagnosing stunting in toddlers using the Dempster Shafer method, which assists medical personnel in performing early detection based on symptoms and expert belief levels. The Dempster Shafer approach is applied due to its ability to handle uncertainty in data and combine multiple pieces of evidence to produce a rational diagnostic conclusion. The research data were obtained from the Posyandu in Babul Makmur District, Southeast Aceh Regency, consisting of 30 test data samples. The system was developed using the Python programming language, Flask framework, and SQLite database. The testing results show that the system achieved an accuracy rate of 36.66%, with 11 out of 30 test data correctly classified according to expert diagnosis. Although the accuracy remains low, this study demonstrates the potential of the Dempster Shafer method as a foundation for evidence-based diagnostic systems in stunting detection.]]>
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