<![CDATA[The detection of pneumonia cases in children under five at Jabung Public Health Center has not reached the targeted rate. From 2019 to 2022, the number of identified cases remained below the expected target of 4.45%. This study aimed to design and develop an early detection system for childhood pneumonia based on medical records using the C4.5 algorithm. The research applied the waterfall development method and utilised data collection techniques including interviews and document analysis. The subjects were program officers for childhood pneumonia and medical record staff, while the objects were medical records of children diagnosed with pneumonia and acute respiratory infections (ARI). System development involved several stages, starting with data preprocessing, including data cleaning, selection, reduction, and transformation. Data mining was conducted using the C4.5 algorithm with the help of RapidMiner software. The result was an early detection system tailored to the needs of Jabung Public Health Center. The system achieved an accuracy rate of 97.50% based on the confusion matrix. This system was expected to assist health workers in identifying pneumonia cases in children more effectively, thereby improving disease monitoring and early treatment efforts at the community healthcare level.]]>
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