Published: 2025-04-01
Forecasting New Student Admissions at Muhammadiyah Elementary School Metro Using the Weighted Moving Average Method
DOI: 10.35870/ijsecs.v5i1.3541
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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
Keywords
SD Muhammadiyah Metro ; Forecasting ; Weighted Moving Average ; Educational Strategy ; Resource Allocation
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 5 No. 1 (2025)
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Section: Articles
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Published: %750 %e, %2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/ijsecs.v5i1.3541
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Clara Tintan Melati
Informatics Engineering Study Program, Faculty of Business Technology and Science, Universitas Dharma Wacana, Metro City, Lampung Province, Indonesia
Budi Sutomo
Informatics Engineering Study Program, Faculty of Business Technology and Science, Universitas Dharma Wacana, Metro City, Lampung Province, Indonesia
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Silvya, Z., Zakir, A., & Irwan, D. (2020). Penerapan metode Weighted Moving Average untuk peramalan persediaan produk farmasi. JiTEKH, 8(2), 59–64. https://doi.org/10.35447/jitekh.v8i2.220
-
Nasution, A. (2019). Metode Weighted Moving Average dalam M-forecasting. JURTEKSI (Jurnal Teknologi dan Sistem Informasi), 5(2), 119–124. https://doi.org/10.33330/jurteksi.v5i2.355
-
Kartika, I., Sapri, S., & Fredricka, J. (2023). Aplikasi peramalan jumlah pendaftaran siswa di SMA IT IQRA’ Kota Bengkulu menggunakan metode Weight Moving Average (WMA). Jurnal Media Infotama, 19(2), 415–421. https://doi.org/10.37676/jmi.v19i2.4317
-
Dewi, R., & Evi, R. (2018). Sistem forecasting perencanaan produksi dengan metode Single Eksponensial Smoothing pada keripik singkong Srikandi di Kota Langsa. Jensi, 2(1), 10.
-
Forecasting, P. P. (2005). BAB 2 Kajian pustaka 2.1 Pengertian peramalan (Forecasting). [Unpublished manuscript]. [Details incomplete; please provide full source information if available].
-
Hidayanti, D. A., Syafwan, H., & Akmal, A. (2024). Penerapan metode Weighted Moving Average pada sistem peramalan stok bahan laundry. Edumatic: Jurnal Pendidikan Informatika, 8(1), 153–162. https://doi.org/10.29408/edumatic.v8i1.25636
-
Marita, L. S., & Darwati, I. (2022). Prediksi persediaan barang menggunakan metode Weighted Moving Average, Exponential Smoothing dan Simple Moving Average. Jurnal Tekno Kompak, 16(1), 56. https://doi.org/10.33365/jtk.v16i1.1484
-
Rifadli, S., & Sari, R. (2024). Implementasi metode Weighted Moving Average (WMA) pada prediksi penjualan gas elpiji berbasis website. Jurnal Desain dan Analisis Teknologi, 3(2), 88–95. https://doi.org/10.58520/jddat.v3i2.47
-
Ramadani, S., Sembiring, M. A., & Rohminatin, R. (2023). Perbandingan Moving Average dengan Single Exponential Smoothing untuk memprediksi jumlah siswa baru. JUTSI: Jurnal Teknologi dan Sistem Informasi, 3(2), 107–114. https://doi.org/10.33330/jutsi.v3i2.2250
-
Ramadania, R. (2018). Peramalan harga beras bulanan di tingkat penggilingan dengan metode Weighted Moving Average. Bimaster: Buletin Ilmiah Matematika, Statistika dan Terapannya, 7(4), 329–334.
-
Rizaldi, R., Rahmawati, D., & Dermawan, A. (2023). Penerapan metode Weighted Moving Average pada proses peramalan penjualan kue kering Difa. Jurnal Ilmiah Multidisiplin Nusantara (JIMNU), 1(2), 96–102. https://doi.org/10.59435/jimnu.v1i2.127
-
Rizqi, M., Cahya, A., & Maida, N. El. (2021). Implementasi metode Weighted Moving Average untuk sistem peramalan penjualan Markas Coffee. INFORMAL: Informatics Journal, 6(3), 154. https://doi.org/10.19184/isj.v6i3.28467
-
Tarigan, Z. A., & Sagala, J. R. (2021). Peramalan (forecasting) jumlah kunjungan pasien di Klinik Kasih Ibu menggunakan metode Weight Moving Average. Jurnal Media Informatika, 3(1), 38–44. https://doi.org/10.55338/jumin.v3i1%20Desember.217
-
Ustadatin, F., Muqtadir, A., & Arifia, A. (2023). Implementasi metode Weighted Moving Average (WMA) pada prediksi harga bahan pokok. Komputika: Jurnal Sistem Komputer, 12(2), 83–90. https://doi.org/10.34010/komputika.v12i2.10304
-
Winarso, D. (2017). Perbandingan metode regresi linier dan Weighted Moving Average dalam meramalkan jumlah mahasiswa pada periode tertentu. Prosiding Celscitech, 2, tech_70–tech_74.
-
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS ONE, 13(3), Article e0194889. https://doi.org/10.1371/journal.pone.0194889
-
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice. OTexts.
-
Taylor, J. W. (2003). Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting, 19(4), 715–725. https://doi.org/10.1016/S0169-2070(03)00003-5
-
Wang, X., & Chen, Y. (2019). ARIMA model for time series forecasting. Statistical Science, 34(3), 422–436. https://doi.org/10.1214/19-STS688
-
Goodwin, P., & Wright, G. (2014). Decision analysis for management judgment. Wiley.
-
Cuaresma, J. C. (2017). Income projections for climate change research: A framework based on statistical forecasting. Global Environmental Change, 42, 56–68. https://doi.org/10.1016/j.gloenvcha.2016.12.009.
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