Design of a Digital Platform for PAUD Child Development Monitoring Using the Dynamic Systems Development Method and Machine Learning

Rachmat Destriana; Muhamad Luthfi  Aksani; Dyas  Yudi Priyanggodo; Revalina  Farzani

doi:10.52436/1.jutif.2025.6.5.4958

Authors

Rachmat Destriana Informatics Study Program, Muhammadiyah University of Tangerang, Tangerang, Indonesia
Muhamad Luthfi Aksani Informatics Study Program, Muhammadiyah University of Tangerang, Tangerang, Indonesia
Dyas Yudi Priyanggodo Informatics Study Program, Muhammadiyah University of Tangerang, Tangerang, Indonesia
Revalina Farzani Informatics Study Program, Muhammadiyah University of Tangerang, Tangerang, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.5.4958

Keywords:

Digital Monitoring System, DSDM Method, Early Childhood Development, Machine Learning, Predictive Analytics

Abstract

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.

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References

Suharsiwi and W. Pandia, “Description of Teachers’ and Parents’ Practices in Dealing with Young Children’s Developmental Delay,” pp. 236–240, 2020, doi: 10.2991/assehr.k.200130.122.

A. Lestariningrum, “Management and Sustainability Challenges of Early Childhood Education Institutions in Pandemic Era,” pp. 18–23, 2021, doi: 10.2991/ASSEHR.K.211028.087.

I. Kertati, “IMPLEMENTASI KEBIJAKAN PENYELENGGARAAN PENDIDIKAN USIA DINI (PAUD) DI KOTA SEMARANG,” Mimb. Adm. FISIP UNTAG Semarang, 2021, doi: 10.56444/mia.v18i1.2163.

I. Istiniah, L. P. Syakema, L. Susanti, M. Merlina, and S. H. Julianti, “Partisipasi 3 PAUD Kota Palangka Raya atas APK dan Sisdiknas-RPJMN Tahun 2020-2024,” Real Kiddos J. Pendidik. Anak Usia Dini, 2023, doi: 10.53547/realkiddos.v1i2.313.

A. Salsabila and B. Budyanra, “Determinan status partisipasi pendidikan anak usia dini di Pulau Jawa tahun 2019,” J. Kependud. Indones., 2022, doi: 10.14203/jki.v17i1.677.

N. Wati, D. Brata, W. Firdaus, Y. C. Ummah, and R. A. K. Mahatmaharti, “THE ROLE OF TEACHERS COMMUNICATION THROUGH THE PATTERN OF EARLY CHILDHOOD,” Humanit. Soc. Sci., vol. 8, pp. 596–601, 2020, doi: 10.18510/hssr.2020.8171.

B. Mardina et al., “Integration of Artificial Intelligence in Pediatric Education: Perspectives from Pediatric Medical Educators and Residents,” Healthc. Inform. Res., vol. 30, pp. 244–252, 2024, doi: 10.4258/hir.2024.30.3.244.

S. Rohajawati, S. Indria, P. Rahayu, H. Akbar, and D. Sensuse, “Implementing DSDM and OO Method to Develop Billing in Mental Hospital,” J. Phys. Conf. Ser., vol. 1566, 2020, doi: 10.1088/1742-6596/1566/1/012059.

M. Sobarnas, Iskandar, and A. Imamuddin, “The Development of a Software Tool for Improvement Tracking System Using Dynamic Systems Development Methodology,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1156, 2021, doi: 10.1088/1757-899X/1156/1/012009.

B. Pardamean, G. Elwirehardja, M. Isnan, R. Rahutomo, and F. Asadi, “Machine Learning Implementations in Childhood Stunting Research: A Systematic Literature Review,” 2023 Int. Conf. Inf. Manag. Technol., pp. 229–234, 2023, doi: 10.1109/ICIMTech59029.2023.10277881.

A. Pujitresnani et al., “Differences in syntactic and semantic analysis based on machine learning algorithms in prodromal psychosis and normal adolescents.,” Asian J. Psychiatr., vol. 85, p. 103633, 2023, doi: 10.1016/j.ajp.2023.103633.

Suparno and E. K. Ulni, “Developing a model of teaching patterns recognition based on sorting predict-think discovery for children aged 5-6 years,” J. Phys. Conf. Ser., vol. 1511, 2020, doi: 10.1088/1742-6596/1511/1/012050.

A. Permadi, A. N. Ismiatun, Andrisyah, B. Aditya, and A. R. Atika, “Digital disruption in early childhood education: A qualitative research from teachers’ perspective,” Procedia Comput. Sci., 2022, doi: 10.1016/j.procs.2021.12.169.

R. Rohita, “The Ability of Ece Teachers to Use ICT in The Industrial Revolution 4.0,” vol. 4, pp. 502–511, 2020, doi: 10.31004/OBSESI.V4I2.339.

I. Journal, O. Humanities, J. A. Lim, S. Chae, and C. Author, “LAKSA: The Digitally- Based Realization of Tangerang City’s Community Public Service 1,2),” vol. 4, no. 5, pp. 2496–2507, 2025.

M. C. D. Lestari, Ayu Citra Dewi, Sri Intan Wahyuni, Juliwis Kardi, Yendri Junaidi, and Alif Laini, “Implementation of Stimulation, Early Detection, and Intervention Programs for Monitoring the Growth and Development of Children Aged 2-3 Years,” JPUD - J. Pendidik. Usia Dini, vol. 18, no. 1, pp. 183–194, 2024, doi: 10.21009/jpud.181.13.

U. Awan et al., “Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions,” Ann. Oper. Res., pp. 1–25, 2022, doi: 10.1007/s10479-022-05015-5.

M. Nampijja, N. K. Langat, E. Kimani-Murage, E. Mwaniki, P. Kitsao-Wekulo, and K. Okelo, “Development and feasibility testing of a mobile phone application to track children’s developmental progression,” PLoS One, vol. 16, 2021, doi: 10.1371/journal.pone.0254621.

. N., . T., T. Siswati, H. Widyawati, and M. P. Rialihanto, “The design of growth and development children’s monitoring application: a user-centered approach,” Int. J. Community Med. Public Heal., 2022, doi: 10.18203/2394-6040.ijcmph20223198.

D. Mukherjee et al., “Scalable Transdiagnostic Early Assessment of Mental health (STREAM): Study Protocol,” 2024, doi: 10.1101/2024.05.07.24306697.

W. Chao and Weicong, “Structure-Behavior Coalescence Abstract State Machine for Metamodel-Based Language in Model-Driven Engineering,” IEEE Syst. J., vol. 15, pp. 4105–4115, 2021, doi: 10.1109/jsyst.2020.3027195.

S. Anvar, “Introduction to UML,” Prof. C++, 2021, doi: 10.1002/9781119695547.app4.

K. Parkin et al., “Machine learning for prediction of childhood mental health problems in social care,” BJPsych Open, vol. 11, 2024, doi: 10.1192/bjo.2025.32.

F. Alfiyasin and G. Febriani, “Workshop Information System Design Using Dynamic System Development Method,” J. Comput. & Bisnis, 2024, doi: 10.56447/jcb.v18i1.296.

E. Puspitasari, R. Pamungkas, and H. A. Mumtahana, “ANALISA DAN PERANCANGAN PENGEMBANGAN E-LEARNING DENGAN METODE DSDM (STUDI KASUS SDN 01 MANISREJO KOTA MADIUN),” J. Inform. Dan Tekonologi Komput., 2023, doi: 10.55606/jitek.v3i3.2344.

S. G. Tetteh, “Empirical Study of Agile Software Development Methodologies: A Comparative Analysis,” Asian J. Res. Comput. Sci., 2024, doi: 10.9734/ajrcos/2024/v17i5436.

S. Vuotto, L. Pulina, M. Narizzano, and A. Tacchella, “Automated Requirements-Based Testing of Black-Box Reactive Systems,” pp. 153–169, 2020, doi: 10.1007/978-3-030-55754-6_9.

B. Dai, W. Pan, and X. Shen, “Significance Tests of Feature Relevance for a Black-Box Learner,” IEEE Trans. Neural Networks Learn. Syst., vol. 35, pp. 1898–1911, 2021, doi: 10.1109/TNNLS.2022.3185742.

S. Houde, D. Gonz’alez, J. Richards, and D. Piorkowski, “Towards evaluating and eliciting high-quality documentation for intelligent systems,” ArXiv, vol. abs/2011.0, 2020, [Online]. Available: https://consensus.app/papers/towards-evaluating-and-eliciting-highquality-houde-gonzalez/27e1a9aea63b5f449e986ece4ce6116b/

H. Kazemi-Arpanahi, M. Shafiee, Z. Nassari, and M. Shanbehzadeh, “Development and evaluation of an electronic nursing documentation system,” BMC Nurs., vol. 21, 2021, doi: 10.1186/s12912-021-00790-1.

P. Rachmadi, D. A. Wp, and P. K. Ayuningtyas, “Performance And Functional Testing With The Black Box Testing Method,” Int. J. Progress. Sci. Technol., 2023, doi: 10.52155/ijpsat.v39.2.5471.

M. F. Fiandhika, A. Riskinanto, B. Kelana, and M. Nasyiah, “Validation of System Usability Scale From Expert and Ordinary User Perspective,” Proceeding Int. Conf. Sci. Heal. Technol., 2024, doi: 10.47701/icohetech.v5i1.4109.