Implementasi Algoritma K-Means untuk Pengelompokan Anak Kurang Gizi di Puskesmas
DOI:
https://doi.org/10.31849/jurkim.v5i2.27968Keywords:
Malnutrisi, K-Mean, Gizi, Malnutrition, K-Means Clustering, Child Nutrition Status, Community Health Center, Davies-Bouldin IndexAbstract
Malnutrition in children remains a major challenge that affects their quality of life and development. Early identification and grouping of children at risk of malnutrition are crucial for effective intervention. This study aims to apply the K-Means algorithm to cluster the nutritional status of toddlers at Umban Sari Community Health Center, using variables such as height, weight, age, and gender. A total of 1,764 toddler data records were analyzed and grouped into two clusters: a cluster of well-nourished children and a cluster of malnourished children. The results show that 34.6% of children belong to the well-nourished cluster, while 65.4% are classified in the malnourished cluster. Validation using the Davies-Bouldin Index (DBI) yielded a value of 0.525, indicating a reasonably good separation between clusters. These findings provide a strong foundation for the Community Health Center to conduct more targeted interventions and improve the effectiveness of monitoring child nutrition status in the Umban Sari area
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