<![CDATA[Examination of the nutritional status of toddlers is one way to monitor and identify toddlers who are at risk of experiencing nutritional problems. Implementation of the method in predicting nutritional status using relevant parameters. The Decision Tree method is used as a predictive model, using a dataset with parameters such as age, sex, height, weight, and nutritional status as labels. At the mining stage, data processing starts from preprocessing, namely the cleansing process to clean up incorrect data and data transformation to change the data type so that it is easy to process during the classification process. Furthermore, the Decision Tree model will be trained, tested and measured based on accuracy. The model is described in the form of a decision tree so that it can be used as a rule in system implementation. The implementation results provide accurate predictions with an accuracy value of 92.73%. The prediction system is designed to assist health workers in supporting decisions on predicting the nutritional status of children under five, as well as facilitating the community to carry out independent checks. This prediction can help identify toddlers at risk of nutritional disorders so that early intervention steps can be taken appropriately..]]>
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