Implementasi Algoritma K-Means Clustering Status Gizi Balita
Abstract
Nutritional problems are a common problem in Indonesia, especially the problem of malnutrition. One of the factors that affect the nutritional status of toddlers is the lack of parental knowledge, because just looking at the physical development of toddlers is certainly not enough to know the category of nutritional status of toddlers. This study uses data mining techniques by applying the K-Means method, the grouping process using the K-Means algorithm is expected to make it easier for the community, especially mothers, to know the nutritional status of their children, we can know that the relevant K-Means Algorithm is used for the process of grouping toddler nutritional data. The output of this study is to classify the nutritional status of toddlers based on weight and height. The validation test in this study used the Davies Bouldin Index, according to the results of testing and data validation, the final result menu Submit the DBI Kmeans result 0.522483983 (Non-Negative). DBI is declared to be essentially optimal if the final value obtained is as low as possible (non-negative ≥0) in order to measure whether the final cluster obtained in the calculation is good or not. This value describes the high level of similarity/similarity of membership in a cluster that has a large degree of similarity/similarity and the distance between one cluster and other clusters is also large. The nutritional clustering process for toddlers is grouped according to their nutritional status
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