<![CDATA[The nutrition fulfillment for children under five years old is a factor that must be considered in maintaining their health, because toddlers are in a developmental period that is vulnerable to nutrition. The attention of parents and officers of the Public Health Service Center (PUSKESMAS) is very necessary to monitor toddler nutrition. Therefore we need a system that can help to classify the nutritional status of children. The system is designed using the KnA method (a combination of K-means and Agglomerative Hierarchical Clustering) so that clustering results are better by combining the advantages of bottom-up clustering (agglomerative) and top-down clustering (k-means). This study attempts to categorize the data from Pulung Ponorogo health center with 7336 data (testing 3641 and learning 3695) with variables of sex, age, weight and height into 4 clusters of each toddler's nutritional status, namely for BB / U there 4 clusters BB S.Krg, BB Krg, BB Normal, BB Lbh, 4 clusters TB / U consisting of S. Short, Short, Normal, High and BB / TB 4 clusters S.Kurus, Skinny, Normal and Fat. In this study, the KnA method was able to properly classify toddlers' data in large numbers. The learning data is as much as 3695 produces an accuracy of BBU = 94%, TBU = 98%, and BBTB = 93%. From the results of the grouping conducted, it is used to determine the nutritional status of status according to BB / U, TB / U, and BB / TB, each of which i s equipped with a handling solution. From the testing with the black-box method as a whole the system is running well, as well as the questionnaire method and as well as validation of nutri]]>
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