Comparison Of Simple Moving Average and Exponential Smoothing Methods To Predict Seaweed Prices

Abstract

Seaweed is one of the main superior commodities in the City of Tarakan. The problem often faced in the field is that the increase in production is not proportional to the price of seaweed that fluctuates every month from year to year, making it difficult for collectors to determine the price of seaweed for sale. Price increases caused by various factors such as the drying process, long marketing chains, and increasing demand from domestic companies. In determining the price of seaweed in the future some approach is needed to predict or predict the price of seaweed in the following month. Forecasting (forecast) is an activity or business that knows the events that will occur at that time will use historical data. Prediction methods used in this study are Simple Moving Average and Exponential Smoothing, these two methods will be guided to find out the most appropriate method used in predicting. Based on the minimum error value calculated using the values of MAD, MSE, MFE, MAPE and CFE, it is found that the Simple moving average forecasting method is considered the best method that can be used to forecast seaweed prices.