<![CDATA[Malnutrition is a serious problem in developing countries, caused by a lack of food intake containing essential substances such as protein and energy. The implementation of machine learning algorithms can provide an accurate diagnosis of malnutrition health conditions in toddlers, facilitating early detection and appropriate interventions. The purpose of this study is to determine the performance of the K-Nearest Neighbors (KNN) algorithm in predicting malnutrition based on clinical characteristics possessed by toddlers. The data used are clinical characteristics of malnutrition sourced from a nutritionist. From the research results, the most optimal accuracy value in predicting malnutrition is 87%. With the existing dataset, it can be proven that the K-Nearest Neighbors (KNN) algorithm is able to classify malnutrition into 2 conditions, namely marasmus and kwashiorkor.]]>
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