The level of children's health is one of the problems that the Indonesian government pays attention to, especially in the health sector. Data from the Indonesian Demographic and Health Survey (IDHS) in 2017 showed that the infant mortality rate (IMR) reached 24 deaths out of 1000 live births. Diseases and health problems that cause child mortality such as perinatal problems, infectious disorders, and malnutrition problems are difficult problems to handle at the health care center level in remote areas due to the lack of diagnostic equipment and medicines. Along with the rapid development of technology today, there are many applications that function to simplify the process of managing and finding information, one of which is chatbot technology. Like a pediatrician, later the chatbot will diagnose the disease from a series of questions submitted by the child's parents. This final project research aims to develop an android-based chatbot application to help identify diseases in children using a back-propagation artificial neural network. The data used in this research is based on the Integrated Management of Sick Toddler (IMCI) chart book. Based on the results obtained in this final project, the best BPNN model for children's disease problems in a chatbot application with the number of neurons (128, 64), epochs as many as 800, dropout rate 0.5, optimizer Adam, getting training loss values of 0.11, training accuracy of 96%, validation loss of 1.05 and has a validation accuracy of 64%.