Revolutionizing Early Childhood Development Screening with the PINKI App
DOI:
https://doi.org/10.56988/chiprof.v4i1.98Keywords:
Child Development, Development Screening, Digital Tools, Early Childhood Education, PINKI AppAbstract
Early childhood development is a critical phase shaping a child's future, making monitoring their progress essential to identify developmental concerns. However, many early childhood educators (PAUD teachers) face significant barriers to performing accurate and timely screenings due to limited tools and knowledge. This research investigates the effectiveness of the Pinki app, a digital tool designed to streamline and enhance the process of child development screening in early childhood education settings. The study involved a group of PAUD teachers trained to use the Pinki app to assess children's cognitive, motor, and social-emotional development. The research aimed to evaluate the impact of the app on the accuracy, efficiency, and effectiveness of developmental screenings. Data was collected through pre- and post-training surveys, interviews with teachers, and analysis of the screening results generated by the app. The findings significantly improved teachers' ability to conduct systematic, data-driven assessments. 85% of teachers reported enhanced confidence in their ability to identify developmental issues early, and 78% noted that the app helped them save time while increasing the accuracy of their evaluations. Furthermore, the Pinki app enabled early intervention strategies by providing clear, objective data on children's development, contributing to more effective support for children with developmental delays. Despite challenges such as limited resources and occasional connectivity issues, the study demonstrated that integrating digital tools like the Pinki app can transform early childhood education by enhancing the quality of developmental monitoring and fostering better educational outcomes. This research highlights the potential for technology-driven interventions to address gaps in early childhood education and improve long-term developmental outcomes for children.
Downloads
References
A. Ibrahim, A. A. Sudirman, M. Rokani, D. Modjo, and U. M. Gorontalo, “ANALISIS PENGGUNAAN SKRINING KPSP DENGAN DENVER II TERHADAP PERKEMBANGAN ANAK USIA 3-5 TAHUN,” vol. 5, no. September, pp. 9975–9985, 2024.
D. E. Suza, D. K. Ariadni, and D. Rasmita, “Peran Guru TK Dalam Skrining Perkembangan Denver Developmental Screening Test ( DDST ) II Pada Anak Prasekolah,” vol. 5, no. 3, pp. 275–285, 2025, doi: 10.23960/jpkmt.v5i3.191.
E. T. Wijayanti, N. Risnasari, and S. Aizah, “Pengenalan Skrining Tumbuh Kembang Anak Usia Dini Berbasis Guru PAUD di TK Al Fath Desa Gondanglegi Kecamatan Prambon Kabupaten Nganjuk,” J. ABDINUS J. Pengabdi. Nusant., vol. 6, no. 1, pp. 99–105, 2022, doi: 10.29407/ja.v6i1.15897.
D. E. Wati and E. A. Maharani, “Pelatihan Penggunaan Kpsp (Kuesioner Pra Skrining Perkembangan) Sebagai Alat Deteksi Tumbuh Kembang Anak Untuk Meningkatkan Kompetensi Guru Paud Aisyiah Di Wilayah Boyolali Jawa Tengah,” Sniemas Uad 2018, pp. 333–337, 2018, [Online]. Available: https://semnasppm.uad.ac.id/prosiding-2018-3/
V. Maarif, E. Saputro, H. M. Nur, and F. F. Dwi, “Aplikasi Monitoring Perkembangan Anak Pada Pendidikan Anak Usia Dini Berbasis Mobile,” vol. 5, no. 1, pp. 38–46, 2025.
A. Rodiyah and S. Mulyati, “Perancangan Sistem Informasi Monitoring Tumbuh Kembang Anak Pada Sekolah PAUD Tunas Khomsiyah,” Automata, 2021, [Online]. Available: https://journal.uii.ac.id/AUTOMATA/article/view/19446
I. G. De Ayu, M. Prameswari, P. E. Kania, S. N. Putri, A. Latifa, and R. Putri, “Optimalisasi Pengelolaan Data Tumbuh Kembang Anak PAUD dengan Aplikasi Excel : Studi Kasus KB Kenanga Desa Pesantren,” vol. 2, no. 2, pp. 2853–2863, 2024.
“SCIENCE”.
K. B. Suryawan and L. T. Merijanti, “Bermain aplikasi gadget berhubungan dengan keterlambatan
perkembangan bicara dan bahasa pada balita,” J. Biomedika dan Kesehat., vol. 4, no. 4, pp. 157–163, 2021, doi: 10.18051/jbiomedkes.2021.v4.157-163.
J. Fusiak, K. Sarpari, I. Ma, U. Mansmann, and V. S. Hoffmann, “Practical applications of methods to incorporate patient preferences into medical decision models: a scoping review,” BMC Med. Inform. Decis. Mak., vol. 25, no. 1, 2025, doi: 10.1186/s12911-025-02945-5.
F. Asadollahi, S. E. Zagami, S. Eslami, and R. L. Roudsari, “Barriers and facilitators for mHealth utilization in pregnancy care: a qualitative analysis of pregnant women and stakeholder’s perspectives,” BMC Pregnancy Childbirth, vol. 25, no. 1, 2025, doi: 10.1186/s12884-025-07244-5.
A. Alarfaj and M. Alrashidi, “Revolutionizing gifted education: enhancing teachers’ digital competence through fourth industrial revolution training,” Discov. Sustain., vol. 6, no. 1, 2025, doi: 10.1007/s43621-025-00946-y.
Y. Han, “Exploring a digital music teaching model integrated with recurrent neural networks under artificial intelligence,” Sci. Rep., vol. 15, no. 1, pp. 1–13, 2025, doi: 10.1038/s41598-025-92327-8.
N. A. Chua et al., “The Belief of Mandarin Foreign Language Educators in Differentiated Instruction Based on Student Characteristics,” Int. J. Sustain. Dev. Plan., vol. 19, no. 2, pp. 629–635, 2024, doi: 10.18280/ijsdp.190220.
A. Is et al., “D Iscrete M Athematics in D Eaf E Ducation :,” Methodology, vol. 22, pp. 251–259, 2005.
A. S. Alfayiz, “The Role of Communication Strategies in Implementing Serious Creativity Strategies by High School Islamic Studies Teachers,” Prof. la Inf., vol. 33, no. 4, pp. 1–17, 2024, doi: 10.3145/epi.2024.ene.0411.
I. M. Rosenstock, “Historical origins of the health belief model. Health Education Monographs,” Health Educ. Monogr., vol. 2, no. 4, pp. 328–335, 1974, doi: http://dx.doi.org/10.1177/109019817400200403.
Susana Yenny and Imam Jayanto, Teori Belajar dan Pembelajaran, no. April. 2021.
“Insights-Into-How-mHealth-Applications-Could-Be-Introduced-Into-Standard-Hypertension-Care-in-Germany-Qualitative-Study-With-German-Cardiologists-and-General-Practitioners_2025_.pdf.”
Z. Szweda et al., “Testing the Corrosion Rate of Prestressed Concrete Beams Under Variable Temperature and Humidity Conditions,” Materials (Basel)., vol. 18, no. 7, 2025, doi: 10.3390/ma18071553.
N. H. A. Halim, N. A. Zainuddin, F. Aminuddin, N. A. Naserrudin, and N. Z. A. M. Hassan, “Navigating the completing cancer treatment incentive in Malaysia: patient insights and implementation challenges,” BMC Health Serv. Res., vol. 25, no. 1, 2025, doi: 10.1186/s12913-025-12544-9.
C. Entoh, F. Noya, and K. Ramadhan, “Deteksi Perkembangan Anak Usia 3 Bulan – 72 Bulan Menggunakan Kuesioner Pra Skrining Perkembangan (KPSP),” Poltekita J. Pengabdi. Masy., vol. 1, no. 1, pp. 8–14, 2020, doi: 10.33860/pjpm.v1i1.72.
M. Kayser, R. Hurlemann, A. Philipsen, N. Freundlieb, and M. Kiebs, “Machine learning based seizure classification and digital biosignal analysis of ECT seizures,” Sci. Rep., vol. 15, no. 1, pp. 1–12, 2025, doi: 10.1038/s41598-025-88238-3.
I. Santosa, I. Iskandar, and S. Setiadi, “Adapting Language Learning Materials for Digital Native: Infusing CEFR Standards in English Procedural Texts on Microlearning Apps,” World J. English Lang., vol. 15, no. 4, pp. 171–183, 2025, doi: 10.5430/wjel.v15n4p171.
Y. Gao, L. Zhu, and M. Tian, “SWOT analysis of the application of three digital media in OLPE physical education teaching: Edmodo, Zoom, and Google Meet,” BMC Med. Educ., vol. 25, no. 1, p. 243, 2025, doi: 10.1186/s12909-025-06826-3.
M. Boulasikis, F. Gruian, and R. Z. Szász, “Using Machine Learning Hardware to Solve Linear Partial Differential Equations with Finite Difference Methods,” Int. J. Parallel Program., vol. 53, no. 2, pp. 1–22, 2025, doi: 10.1007/s10766-025-00791-6.
J. P. Galve et al., “Applications of UAV Digital Photogrammetry in landslide emergency response and recovery activities: the case study of a slope failure in the A-7 highway (S Spain),” Landslides, vol. 22, no. 5, pp. 1383–1396, 2025, doi: 10.1007/s10346-024-02449-9.
T. Zhou et al., “Digital Lasing Biochip for Tumor-Derived Exosome Analysis,” Anal. Chem., 2025, doi: 10.1021/acs.analchem.4c06172.
P. Sweeney, “Persons, Unique Value and Avatars,” Minds Mach., vol. 35, no. 2, pp. 50–52, 2025, doi: 10.1007/s11023-025-09715-2.
S. Takahashi, Y. Hiruta, and D. Citterio, “Bioluminescence readout lateral flow immunoassay using nanobody targeting aflatoxin B1,” Anal. , pp. 1563–1570, 2025, doi: 10.1039/d5an00030k.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Usman Jayadi, Dian Afrilia, Winda Suryaningtyas, Salma Khodijah
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
