Sugar Production Forecasting System in PTPN XI Semboro Jember using Autoregressive Integrated Moving Average (ARIMA) Method

Januar Adi Putra, Saiful Bukhori, Faishal Basbeth

Abstract


There is a lot of entrepreneurial competition in the production of goods or services in the world, especially in Indonesia, especially the production of staple goods, namely sugar. The problem that is often faced at Sugar Factory PTPN XI Semboro Jember is the lack of management that is neatly organized and efficient, which makes this company less working optimally. Often there is a lack and excess of sugar production which makes the sugar does not have the maximum value, the sugar has been damaged, and sales at a reduced price because the sugar is not as efficient as the initial product. From these various problems, it can reduce profits from the company. From these problems it can be concluded that the company needs a system that can organize the management of the company, and is able to forecast production in the future. In this research will make a forecasting system using the method of Autoregressive Integrated Moving Average (ARIMA), where this method is divided into three methods, namely the Autoregressive (AR) method, the Moving Average (MA) method, and the Autoregressive Integrated Moving Average (ARIMA) method, which preceded by checking stationary data, and modeling the Autoregressive Integrated Moving Average (ARIMA) method. Forecasting is done using production data for the previous 12 years from the company. The system is made to facilitate management that is less organized and displays predictions for the next production period. The results of this forecasting system are to determine the amount of production each year needed in this company. From the results of the ARIMA method modeling, the right ARIMA method is obtained by the ARIMA / AR (1,0,0), ARIMA / MA (0,0,1), and ARIMA (1,0,1) methods. The test results found that the average value of Mean Absolute Percentage Error (MAPE) in the Autoregressive (AR) method was 17%, the Moving Average (MA) method was 19%, and the Autoregressive Integrated Moving Average (ARIMA) method was 15%.


Keywords


ARIMA; forecasting; sugar production;

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