Design of a Digital Platform for PAUD Child Development Monitoring Using the Dynamic Systems Development Method and Machine Learning - Destriana, Rachmat; Aksani, Muhamad Luthfi; Yudi Priyanggodo, Dyas; Farzani, Revalina (Informatika, Universitas Jenderal Soedirman, 2025)
This study aims to design a digital platform for monitoring early childhood development in PAUD (Pendidikan Anak Usia Dini) institutions by integrating Machine Learning (ML) into the Dynamic Systems Development Method (DSDM) framework. The research addresses persistent challenges in traditional monitoring systems, which are typically manual, fragmented, and lack real-time responsiveness. Utilizing a Research and Development (R&D) approach, the platform was developed iteratively with active involvement from teachers, parents, and administrators of PAUD institutions. System modeling employed Unified Modeling Language (UML), while ML techniques such as Decision Trees were trained on datasets sourced from PAUD Flamboyan in Tangerang. Key platform features include child data input, growth visualization, predictive analytics, and interactive dashboards. The system underwent black-box testing and usability assessments, achieving an average usability score of 4.5 out of 5. The ML model demonstrated statistically valid and reliable performance with 89% accuracy, 85% precision, and 87% recall in predicting developmental delays. The findings highlight the effectiveness of the DSDM approach in facilitating adaptive system development, and underscore the value added by ML integration in enhancing decision-making within early childhood education. The platform not only streamlines developmental monitoring but also supports early interventions. Future work is recommended to broaden data sources, enrich personalization, and scale deployment across varied PAUD contexts. This study contributes to the advancement of intelligent decision support systems in early childhood education, enabling more accurate developmental monitoring and timely interventions.