<![CDATA[Toddler nutrition issues are an important issue in public health because they affect children's physical and cognitive growth and development. The process of assessing nutritional status which is still done manually in health centers, such as in Winong Community Health Center, is often inefficient and prone to errors. This study aims to develop a web-based decision support system that can automatically classify toddler nutritional status based on age, weight, and height data. This system is built using the CRISP-DM approach as a development methodology and the KNN algorithm as a classification method. The benchmark data for nutritional status refers to the standards of the Ministry of Health of the Republic of Indonesia. The test results show that the system is able to classify nutritional status into categories of good nutrition, malnutrition, and excess nutrition with a satisfactory level of accuracy. The system also provides easy access and speed in the decision-making process for health workers. In conclusion, this system is effective in helping health centers monitor toddler nutritional status quickly, accurately, and efficiently based on valid data.]]>
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