Forecasting New Student Admissions at Muhammadiyah Elementary School Metro Using the Weighted Moving Average Method

SD Muhammadiyah Metro Lampung was established in 1968 with the Decree of the Muhammadiyah Education, Teaching, and Culture Council Number 664/I-057/LP-68/1977. Since then, this institution has emphasized the importance of providing quality education and creating an environment that supports the development of students. The purpose of this study is to predict the acceptance of new students in the coming period, so that it can be the basis for compiling a more appropriate educational planning strategy that is in accordance with real needs. To realize all of this, the main analysis tool is the Weighted Moving Average (WMA). This method is different from other modeling methods such as exponential smoothing and ARIMA because this method provides greater weight based on current data, so that estimates are more sensitive to current trends and more credible as a decision-making tool. The results of the WMA forecast provide schools with the opportunity to estimate the need for resources needed (including teaching staff, supporting facilities, and classroom allocation) to ensure that the education process is running well and correctly. In addition, this technique is a way to assess developing or abolishing admission policies. However, forecasts are only as good as historical data and cannot predict the presence of external factors that affect outcomes

Authors

Publication Details

Journal
International Journal Software Engineering and Computer Science (IJSECS)
Published
2025-04-01
DOI
https://doi.org/10.35870/ijsecs.v5i1.3541
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Forecasting New Student Admissions at Muhammadiyah Elementary School Metro Using the Weighted Moving Average Method

Budi Sutomo, Clara Tintan Melati
International Journal Software Engineering and Computer Science (IJSECS)
Data Mining and Machine Learning Applications
article

Forecasting New Student Admissions at Muhammadiyah Elementary School Metro Using the Weighted Moving Average Method

Budi Sutomo, Clara Tintan Melati
article en

Abstract

SD Muhammadiyah Metro Lampung was established in 1968 with the Decree of the Muhammadiyah Education, Teaching, and Culture Council Number 664/I-057/LP-68/1977. Since then, this institution has emphasized the importance of providing quality education and creating an environment that supports the development of students. The purpose of this study is to predict the acceptance of new students in the coming period, so that it can be the basis for compiling a more appropriate educational planning strategy that is in accordance with real needs. To realize all of this, the main analysis tool is the Weighted Moving Average (WMA). This method is different from other modeling methods such as exponential smoothing and ARIMA because this method provides greater weight based on current data, so that estimates are more sensitive to current trends and more credible as a decision-making tool. The results of the WMA forecast provide schools with the opportunity to estimate the need for resources needed (including teaching staff, supporting facilities, and classroom allocation) to ensure that the education process is running well and correctly. In addition, this technique is a way to assess developing or abolishing admission policies. However, forecasts are only as good as historical data and cannot predict the presence of external factors that affect outcomes

International Journal Software Engineering and Computer Science (IJSECS)Vol. 5(1)
Openalex Percentile: Top 82%
Data Mining and Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.