Clothing Sales Prediction Information System Using Web-Based Double Exponential Smoothing Method
(1) Department of Information System, Universitas Islam Negeri Sumatera Utara, Indonesia
(2) Department of Information System, Universitas Islam Negeri Sumatera Utara, Indonesia
(3) Department of Electronics and Communications Engineering, Technology and Maritime Transport, Egypt
Abstract
Purpose: The purpose of this research is to determine the smallest error value so that the resulting prediction data is more accurate. This prediction data is used to help Raja Fashion Medan in processing goods data and help predict the amount of goods that must be provided to meet customer needs in the next period.
Methods: This research uses the Double Exponential Smoothing method because it is used on data that is more stable and has a trend pattern. To test the accuracy of the prediction results with the Double Exponential Smoothing method, the Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) data testing methods are used by finding the smallest error value.
Result: This test is carried out by determining the smallest error value on 118 data types of goods with error results, namely the average Root Mean Square Error (RMSE) of 26.5, Mean Absolute Deviation (MAD) 1.2, Mean Squared Error (MSE) 37.8 and Mean Absolute Percent Error (MAPE) of 10%, it can be concluded that the accuracy of theprediction is very good.
Novelty: Testing on prediction results uses 4 methods to determine more accurate results, namely with Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percent Error (MAPE) which are used to find values smallest error.
Keywords
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