<![CDATA[Malnutrition is a public health problem that has a significant impact on the physical and mental development of individuals, especially children. Proper and accurate diagnosis is key in handling and preventing the adverse effects of malnutrition. In this study, the certainty factor (CF) method is used to support the malnutrition diagnosis process. The CF method enables the processing of uncertainty in medical decision-making based on clinical evidence and expert knowledge. The use of the Certainty Factor method on 6 types of diseases namely Overnutrition, Marasmic-Kwashiorkor, Malnutrition, Hunger, Marasmus and Stunting and 42 symptoms. The results of the research conducted obtained that the CF method successfully detected symptoms of overnutrition with a confidence level of 87.52% based on the symptoms tested by the user, namely G004, G024, G028, G036 and G005. This study concludes that the use of the certainty factor method can significantly improve the quality of malnutrition diagnosis. This makes an important contribution to the development of technology-based medical decision support systems, especially in handling complex public health problems such as malnutrition. Further development of this system is expected to be able to expand the scope of application for the diagnosis of other diseases that involve data uncertainty.]]>
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