<![CDATA[Toddlers are a vulnerable age group facing issues such as insufficient nutrition and malnutrition. Every year, malnutrition in toddlers emerges as a significant health issue. Several factors influencing this condition include limited access to nutritious food, a lack of parental understanding, and economic challenges. This research aims to develop an expert system for early detection of malnutrition in toddlers based on Android using the Certainty Factor method. Employing the research and development method and system development using the waterfall model, the analysis involves gathering information through interviews, observations, and literature reviews. Subsequently, the system is designed to utilize the Unified Modeling Language (UML) to provide an overview of the intended application. The implementation phase utilizes the Certainty Factor method, incorporating knowledge from experts and symptom data from diagnosed cases of toddler malnutrition as the foundation. The diagnosis results of malnutrition will be presented clearly to users, indicating the success rate in percentage form. Testing of this expert system demonstrates a high accuracy level of 99%. The results of the system testing align with its functions based on black-box testing. This system can make a significant contribution to early detection efforts for malnutrition in toddlers and reduce the burden of malnutrition in society. ]]>
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