Forecasting Using the Time Series Forecasting Model at SMEs Pempek Dang Tirta, Banyumas, Central Java
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Abstract
Erratic product demand makes manufacturers create strategies to minimize unsold products. Products that are stored for a long time can cause losses, so forecasting is needed to calculate future product demand. The method used in this study uses a time series forecasting model at SMEs Pempek Dang Tirta. The quantitative data used covers the period January 2022-August 2023. The results of the calculation of the naïve bayes, exponential smoothing, Simple Moving Average (SMA), and Least Square models are presented in the table. The MAD (Mean Absolute Deviation) value indicates that the average deviation of the yb data from its mean value is about 1060.82 and The least square value obtained is 306.44 indicating that it could be the constant or intercept value of the resulting regression model.
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