The Comparison of Performance Double Exponential Smoothing and Double Moving Average Methods in Seasonal Meat Stock Demand

Abstract

The demand for beef supplies depended on seasonal patterns because it depends on feed supplies, especially in rural areas that still rely on natural feed. Beef supplies were part of government regulations because they were highly demanded commodities. They are livestock products that contain nutritional value to meet the protein needs of the community. Beef stocks were influenced by factors such as beef production, beef consumption, and people's income levels. In anticipating the increasing demand for beef, it is necessary to carry out forecasting to estimate the demand for meat in the future. In forecasting, various methods were used to choose the method with the lowest error rate. This research will compare the Double Exponential Smoothing (DES) with the Double Moving Average (DMA) based on the Mean Absolute Percentage Error (MAPE) measurement, one method for finding the minor error value. Based on the test results with beef supplies in Madura and conducting analysis, it can be concluded that the method with the smallest MAPE value is the Double Exponential Smoothing method, with the smallest MAPE value of 9.50% at an alpha parameter of 0.5. In testing with the Double Moving Average method, by determining the best MAPE value, the best time order in the Double Moving Average method is at time order parameter 2 with a MAPE value of 29.8408%. After finding the parameter with the smallest MAPE value, that parameter is used for data testing. In the measurement, data testing for data of 1 year, two years, three years, and four years. Each method has a level of error value that increases the same; a lot of data entered can affect the size of the MAPE value. So, the more data entered, the smaller the resulting error value.