Analysis of Meeting Early Childhood Education Resource Needs Through Monte Carlo Simulation
Accurate resource planning is a critical challenge in managing Early Childhood Education (ECE) institutions, especially in semi-rural areas facing budget constraints and fluctuating student enrollment. This study aims to apply the Monte Carlo Simulation method as a quantitative approach to predict...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
STAI Publisistik Thawalib Jakarta
2025-05-01
|
| Series: | Al Tahdzib |
| Subjects: | |
| Online Access: | https://jurnal.staithawalib.ac.id/index.php/altahdzib/article/view/625 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849314001022877696 |
|---|---|
| author | Citra Aulia Uzliva Philips Ratu Bunga Yenik Wahyuningsih |
| author_facet | Citra Aulia Uzliva Philips Ratu Bunga Yenik Wahyuningsih |
| author_sort | Citra Aulia Uzliva |
| collection | DOAJ |
| description |
Accurate resource planning is a critical challenge in managing Early Childhood Education (ECE) institutions, especially in semi-rural areas facing budget constraints and fluctuating student enrollment. This study aims to apply the Monte Carlo Simulation method as a quantitative approach to predict resource needs at PAUD Ceria Mojokerto over one academic year. The research focuses on three main components: teaching staff, stationery supplies, and classroom space. Historical data from the past five years were used to build probability distributions and model student enrollment and attendance rate uncertainties. PAUD Ceria Mojokerto was chosen as a case study due to its representativeness of typical conditions in semi-rural ECE settings. The simulation results indicate that the required number of teaching staff ranges from 2 to 5, stationery supplies range from 103 to 496 units, and classrooms range from 2 to 4 per academic year. These findings highlight that the approach offers more flexible and realistic estimates than conventional deterministic methods. Therefore, ECE administrators are encouraged to integrate quantitative methods such as Monte Carlo Simulation into their operational planning to enhance efficiency, resilience to student population dynamics, and data-driven decision-making. This research contributes to developing data-driven educational planning practices, particularly in resource-limited settings.
|
| format | Article |
| id | doaj-art-d844953235d84d7f9ddc4c21536d00b1 |
| institution | Kabale University |
| issn | 2962-5440 2962-4630 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | STAI Publisistik Thawalib Jakarta |
| record_format | Article |
| series | Al Tahdzib |
| spelling | doaj-art-d844953235d84d7f9ddc4c21536d00b12025-08-20T03:52:37ZengSTAI Publisistik Thawalib JakartaAl Tahdzib2962-54402962-46302025-05-013210.54150/altahdzib.v3i2.625Analysis of Meeting Early Childhood Education Resource Needs Through Monte Carlo SimulationCitra Aulia Uzliva0Philips Ratu Bunga1Yenik Wahyuningsih2Sekolah Tinggi Agama Islam Publisistik Thawalib Jakarta, IndonesiaSekolah Tinggi Teologi Global Glow, IndonesiaSekolah Tinggi Agama Islam Publisistik Thawalib Jakarta, Indonesia Accurate resource planning is a critical challenge in managing Early Childhood Education (ECE) institutions, especially in semi-rural areas facing budget constraints and fluctuating student enrollment. This study aims to apply the Monte Carlo Simulation method as a quantitative approach to predict resource needs at PAUD Ceria Mojokerto over one academic year. The research focuses on three main components: teaching staff, stationery supplies, and classroom space. Historical data from the past five years were used to build probability distributions and model student enrollment and attendance rate uncertainties. PAUD Ceria Mojokerto was chosen as a case study due to its representativeness of typical conditions in semi-rural ECE settings. The simulation results indicate that the required number of teaching staff ranges from 2 to 5, stationery supplies range from 103 to 496 units, and classrooms range from 2 to 4 per academic year. These findings highlight that the approach offers more flexible and realistic estimates than conventional deterministic methods. Therefore, ECE administrators are encouraged to integrate quantitative methods such as Monte Carlo Simulation into their operational planning to enhance efficiency, resilience to student population dynamics, and data-driven decision-making. This research contributes to developing data-driven educational planning practices, particularly in resource-limited settings. https://jurnal.staithawalib.ac.id/index.php/altahdzib/article/view/625Monte CarloResourcesEarly Childhood Education |
| spellingShingle | Citra Aulia Uzliva Philips Ratu Bunga Yenik Wahyuningsih Analysis of Meeting Early Childhood Education Resource Needs Through Monte Carlo Simulation Al Tahdzib Monte Carlo Resources Early Childhood Education |
| title | Analysis of Meeting Early Childhood Education Resource Needs Through Monte Carlo Simulation |
| title_full | Analysis of Meeting Early Childhood Education Resource Needs Through Monte Carlo Simulation |
| title_fullStr | Analysis of Meeting Early Childhood Education Resource Needs Through Monte Carlo Simulation |
| title_full_unstemmed | Analysis of Meeting Early Childhood Education Resource Needs Through Monte Carlo Simulation |
| title_short | Analysis of Meeting Early Childhood Education Resource Needs Through Monte Carlo Simulation |
| title_sort | analysis of meeting early childhood education resource needs through monte carlo simulation |
| topic | Monte Carlo Resources Early Childhood Education |
| url | https://jurnal.staithawalib.ac.id/index.php/altahdzib/article/view/625 |
| work_keys_str_mv | AT citraauliauzliva analysisofmeetingearlychildhoodeducationresourceneedsthroughmontecarlosimulation AT philipsratubunga analysisofmeetingearlychildhoodeducationresourceneedsthroughmontecarlosimulation AT yenikwahyuningsih analysisofmeetingearlychildhoodeducationresourceneedsthroughmontecarlosimulation |