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K-Means Clustering for Toddler Nutrition by Aji Laksono is a document available to read on EtoBox.

This document discusses using the K-Means clustering method to determine the nutritional status of toddlers in a village in Indonesia. It involves collecting data on 50 toddlers, grouping them into 5 clusters of nutritional status (malnourished, undernourished, well-nourished, over-nourished, obese) using the K-Means algorithm in SPSS software. The results of the K-Means clustering were then compared to growth chart classifications, finding that only 34% matched, indicating limited accuracy of the K-Means m

Author

Aji Laksono

Language

EN