<![CDATA[Children's nutritional status is an important indicator of growth and development. The determination of nutritional status at the Simpang Teritip Community Health Center is currently still performed manually and often faces the obstacle of unstable internet networks in the field, potentially leading to errors and delays in decision-making. This study aims to develop a toddler nutritional status classification system using a web-based Support Vector Machine (SVM) algorithm equipped with offline capabilities. The data used consists of anthropometric data, including weight-for-age (BB/U), height-for-age (TB/U), and weight-for-height (BB/TB), based on WHO standards. The research process includes data preprocessing, normalization, SVM modeling with an RBF kernel, and evaluation using a confusion matrix. The results show that the SVM algorithm is capable of classifying the nutritional status of toddlers with high accuracy, achieving 98.20% for the BB/U indicator, 97.11% for the TB/U indicator, and 91.54% for the BB/TB indicator. This system is expected to assist health workers in conducting early detection directly in the field regardless of network constraints, as well as improving the effectiveness of child health services.]]>
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