Evaluation of Machine Learning Algorithms for Classifying User Perceptions of a Child Health Monitoring Application

Child growth and development are crucial aspects that every parent should monitor carefully. Proper growth and development foster the creation of a high-quality generation for the nation’s future. Recognizing the importance of monitoring children's growth, the Indonesian Pediatric developed PrimaKu, an application designed to assist parents in tracking the children's growth and development. The application includes health guidelines, growth monitoring tools, and immunization schedules. To maximize the application’s effectiveness, it is essential to evaluate its acceptance by the community, which can be assessed through user perceptions. This study evaluates the performance of machine learning algorithms, including Random Forest, Support Vector Machine, Naive Bayes, and Decision Tree, in classifying user perceptions of the PrimaKu application. The results revealed that the Support Vector Machine model achieved the highest accuracy of 81%, followed by Random Forest at 77%, Decision Tree at 74%, and Naive Bayes at 73%. Precision, recall, and F1-score used to validate the models' performance as the evaluation metrics. The findings underscore the potential of machine learning techniques in effectively classifying user feedback, providing valuable insights for improving application development and enhancing user satisfaction. This study contributes to understanding user acceptance of digital tools for child health monitoring, paving the way for better application usability and community impact