<![CDATA[Stunting is a serious problem in the field of child health in Indonesia caused by chronic malnutrition, especially in the first 1,000 days of life. To overcome this problem, an efficient and accurate early detection method is needed to accelerate intervention. This study aims to develop a web-based information system that is able to group toddler data and identify potential risks of stunting using the K-means clustering algorithm. The system was developed with a waterfall approach and uses anthropometric attributes such as weight, height, age, and gender as clustering parameters. The grouping results are displayed in the form of tables and graphic visualizations to facilitate analysis. This system is designed to be easily accessible to health workers and policy makers, so that it can support more targeted decision making in efforts to overcome stunting.]]>
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