Expert system for diagnosing measles in children based on android - Jupron Jupron; Tri Prasetyo; Cendra Harmon; Gigih Amrillah Ibnurhus (TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika, 2026)
Measles is a contagious disease caused by the Morbillivirus, and it can be fatal, especially in children. In Indonesia, cases of measles have significantly increased due to low vaccination coverage and limited access to healthcare services in rural areas. The main problem faced is the lack of early detection of measles by communities that do not have direct access to medical professionals. To address this challenge, an Android-based expert system has been developed that allows users to perform early diagnosis of measles independently based on the symptoms experienced. This system uses the Forward Chaining inference method to determine the possible diagnosis from the combination of symptoms chosen by the user. The system development process is carried out using Agile and Rapid Application Development (RAD) approaches to ensure that development can be conducted quickly and flexibly according to user needs. System testing is conducted using the Blackbox Testing method to evaluate outputs based on specific inputs, and the Whitebox Testing method assesses the internal logic of the system. The test results show that this expert system has an accuracy rate of 92.5% in diagnosing measles symptoms, based on test data that has been validated by medical professionals. In addition to diagnosis functionality, the system is also equipped with features for storing diagnosis history, food recommendations, and education about measles. This application is expected to be an effective solution in raising awareness and knowledge of the public about measles, as well as encouraging them to take medical action promptly to prevent more serious complications, especially in areas with limited health facilities.