<![CDATA[Posyandu plays a central role in child growth monitoring and stunting prevention at the commu-nity level; yet Posyandu Lelede 2, Desa Lelede, continues to rely on handwritten registers as its primary recording medium. This dependence generates three recurring operational problems: low data accuracy, difficulty retrieving children's growth histories, and chronic delays in submitting reports to the puskesmas, all of which compromise the effectiveness of early nutritional interven-tion. To address these limitations, the present study designed and implemented a web-based po-syandu information system incorporating the Gaussian Naive Bayes algorithm for automated stunting classification. Development followed the Waterfall methodology through five sequential phases- requirements analysis, design, implementation, testing, and maintenance- with the system constructed in PHP using the Laravel framework and MySQL as the database management system. The anthropometric variables processed included age, body weight, height, and sex of the children. A total of 1,164 records were utilized, comprising 1,000 training records constructed based on WHO anthropometric standards and 164 primary testing records obtained directly from Posyan-du Lelede 2 from December 2025 to February 2026. Evaluation via confusion matrix demonstrated a classification accuracy of 82.31% against WHO anthropometric standards, while Black Box Testing confirmed that all functional modules operated as specified. The system is therefore con-sidered a viable digital model for strengthening posyandu services and child health data manage-ment at the grassroots level.]]>
