OPTIMIZATION OF STUNTING INFANT DATA CLUSTERING WITH K-MEANS++ ALGORITHM USING DBI EVALUATION
Efmi Maiyana, Wizra Aulia · JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) · 2025
Stunting in infants is a serious health issue, particularly in developing countries like Indonesia. This study aims to optimize the clustering of stunting data in infants using the K-Means++ algorithm, evaluated with the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. The stunting data includes variables such as age, gender, weight, and height. The analysis results indicate that the optimal number of clusters is 5, with a DBI value of 0.837986204, confirming the quality of the clustering. This conclusion demonstrates that the combination of these evaluation methods produces effective clustering and provides significant insights into identifying groups of infants with varying stunting risk levels. These findings can serve as a basis for more targeted health interventions in addressing stunting