Clusterization Of Infant Data Based on Posyandu Examination Using K-Means
Abstract
Nutrition is a very important aspect for the human body, especially in toddlers and children. Balanced nutrition not only supports children's growth and development, but also improves academic achievement and contributes positively to their future development. However, in Tumpiling Village, the problem faced is the low basic understanding of parents and Posyandu cadres regarding balanced nutrition in early childhood. This causes toddlers to still be found with malnutrition or obesity, as well as a lack of data collected based on children's nutritional characteristics. Clustering, as one of the popular methods in processing medical, biometric, and various other fields of data, is known for its simplicity and effectiveness in grouping large-scale data based on similar characteristic. This study aims to groups the nutritional status of toddlers based on height and weight parameters using the K-Means Clusterings algorithm. This grouping produces several categories of nutritional status, namely obesity, overnutrition, good nutrition, undernutrition, and poor nutrition. By applying the Clustering method using K-Means, the nutritional status of toddlers can be classified more clearly, so that it can be a basis for Posyandu cadres in taking early preventive measures against malnutrition and obesity. In this study, the author used 28 toddler data. From the data, the author randomly determined the cluster center of 5 data, which then resulted in the following grouping: 7 toddlers experienced malnutrition, 3 toddlers were undernourished, 6 toddlers with good nutrition, 7 toddlers were overnourished, and 5 toddlers were obese. These results indicate the need for further attention and action from Posyandu and Puskesmas cadres to help parents in overcoming toddler nutrition problems
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