Development of Integrated Nutritional Monitoring Application Utilizing Machine Learning and Artificial Intelligence for Child Growth Assessment

Stunting, defined as a condition characterized by nutritional deficiencies during the critical growth and development stages of early childhood, poses significant long-term consequences for affected individuals. This issue remains a pressing public health challenge for children in Indonesia. In response to the alarming rates of stunting in the country, this study aims to develop an integrated nutritional monitoring application that leverages machine learning and artificial intelligence (AI) to monitor child growth and prevent stunting. Employing a Research and Development (R&D) approach utilizing the 4D model (Define, Design, Develop, Disseminate), the application is specifically designed to assist parents and healthcare providers in effectively monitoring children's growth. Key features of the application include dietary pattern recognition, an AI-based nutritional calculator, personalized intake menus, virtual nutritionist consultations, and a directory of nearby nutritionists. The implementation of this application took place in the Special Region of Yogyakarta, involving collaborations with community health centers (puskesmas), integrated health service posts (posyandu), and early childhood education institutions. The evaluation of the application was conducted using the System Usability Scale (SUS), with 38 respondents yielding a usability score of 82.5%, indicating a high level of acceptance and ease of use. The results of this study demonstrate that the application is effective in providing accurate and personalized nutritional information, facilitating the monitoring of child growth, and enhancing parental awareness regarding stunting prevention. This application has the potential to serve as a valuable tool in efforts to reduce the prevalence of stunting in Indonesia, particularly in areas with limited access to conventional healthcare services.