<![CDATA[Infectious diseases in children remain a serious health problem due to their high vulnerability resulting from an immune system that is not yet fully developed. Limited access to medical personnel and delays in early detection often result in ineffective treatment. Therefore, this study aims to design and implement an Android-based intelligent system application capable of detecting infectious diseases in children early on by utilizing the Certainty Factor and Naïve Bayes methods. This system is designed as an expert system that mimics the way pediatricians analyze symptoms and determine preliminary diagnoses. The research methods used include collecting disease and symptom data based on the knowledge of pediatric health experts, data analysis, rule base formation, and the design and implementation of an Android-based system. The Certainty Factor method is used to handle the uncertainty of the level of confidence in the symptoms selected by the user, while the Naïve Bayes method is used to calculate the probability of disease based on historical data. The combination of these two methods aims to improve the accuracy and reliability of diagnostic results. The results of the study show that the developed expert system application is capable of providing initial diagnostic information on infectious diseases in children quickly and easily accessible to parents and health workers. This system is expected to be an effective early detection tool, support initial medical decision-making, and contribute to the development of artificial intelligence-based health technology in Indonesia.]]>
