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All Journal Journal of Artificial Intelligence and Digital Business
Solfianti, Maidia
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Implementation of Simple and Weighted Moving Average for Forecasting Tela-Tela Production in MSME X Sariati, Sariati; Pratama, Borneo Satria; Albar, Ferdy; Sitio, Dea Yolanda Putri; Riandry, Dimas Dwika; Antonio, Marcho; Januarti, Sri; Aliansyah, Bayu Rimba; Sutignya, Th. Candra Wasis Agung; Solfianti, Maidia; Erwan, Erwan; Sembiring, Loko Jeremia
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.5205

Abstract

Accurate production forecasting is essential for micro, small, and medium enterprises (MSMEs) to support effective production planning, inventory control, and decision-making. This study evaluates the performance of the Simple Moving Average (SMA) and Weighted Moving Average (WMA) methods in forecasting tela-tela production demand at MSME X using different historical period lengths. Production data from November 2023 to October 2024 were analyzed, and forecasting accuracy was assessed using the Mean Absolute Percentage Error (MAPE). The results indicate that forecasting accuracy varies depending on both the length of the moving average period and the weighting scheme applied. The WMA model with a 4-period window (n = 4) achieved the highest accuracy, producing the lowest MAPE value of 8.36%, which is classified as highly accurate. The SMA model with n = 4 also demonstrated good performance, with a MAPE value of 14.40%. Meanwhile, models employing longer historical periods (n = 8) yielded MAPE values of 16.20% for WMA and 19.82% for SMA, both falling within the good forecasting performance category but exhibiting lower responsiveness to recent demand changes. These findings highlight that shorter historical periods, when combined with appropriate weighting, can more effectively capture recent demand patterns in dynamic production environments. Accordingly, the WMA method with a 4-period window is recommended for MSME X as a reliable and accurate approach to support production planning, optimize resource allocation, and reduce the risk of overproduction or stock shortages.  
Co-Authors Albar, Ferdy Aliansyah, Bayu Rimba Antonio, Marcho Erwan Erwan, Erwan Januarti, Sri Pratama, Borneo Satria Riandry, Dimas Dwika Sariati Sariati, Sariati Sembiring, Loko Jeremia Sitio, Dea Yolanda Putri Th. Candra Wasis Agung Sutignya