Data Mining Clustering Analysis of Child Growth and Development Using the K-Means Method
DOI:
10.33395/sinkron.v8i3.13817Keywords:
Child Development; Clustering; Data Mining; K-Means; Knowledge Discovery in Databases (KDD)Abstract
This research aims to group children based on their growth and development characteristics. This method helps identify groups of children with normal growth and development, early signs of growth and development problems, and serious growth and development problems. The stages used in this research follow the Knowledge Discovery in Database (KDD) process, which consists of data selection, data pre-processing, data transformation, data mining, and evaluation or interpretation of results. By applying the K-Means method, this research aims to provide a clearer and more detailed picture of the distribution of children's growth and development problems and assist in decision making for more appropriate interventions. The K-Means method in data mining was used to group 102 sample data into three clusters based on children's growth and development characteristics. The results of this analysis show that 38 samples fall into Cluster 1 (C1), 36 samples into Cluster 2 (C2), and 28 samples into Cluster 3 (C3). Evaluation of clustering results is carried out using Box Plot and Scatter Plot. Box Plot shows a clear distribution of data for each cluster, ensuring that the data grouping corresponds to statistical evaluation. Cluster C1 is toddlers with normal growth and development. Cluster C2 shows early signs of growth and development problems. Cluster C3 indicates serious growth and development problemsDownloads
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