Evaluation of Machine Learning Algorithms for Classifying User Perceptions of a Child Health Monitoring Application - Eka Rahmawati; Adi Wibowo; Budi Warsito (Jurnal Informatika, 2025)
Supporting children’s early development requires consistent attention, ensuring their growth aligns with health standards. PrimaKu is one of the mobile applications developed by the Indonesian Pediatric Society. That application was created to assist parents in recording developmental milestones, monitoring immunization schedules, and accessing practical health information. This study investigates user perceptions of the application by analyzing publicly available reviews and ratings from the Google Play Store. Four supervised machine learning algorithms were applied to classify the sentiment expressed in the reviews: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. Among the models tested, SVM achieved the highest classification accuracy (81%), followed by Random Forest (77%), Decision Tree (74%), and Naive Bayes (73%). Precision, recall, and F1-score were also used to evaluate the performance of each model. The results highlight the relevance of machine learning in capturing and interpreting user sentiment toward digital health tools. Further exploration of deep learning architectures is encouraged to enhance classification accuracy and understanding of features.