ANALYSIS OF LINEAR REGRESSION AND TREND MOMENT METHODS IN PREDICTING SALES USING MAPE

Abstract

ABSTRACK- Sales transaction data stored in the database stores a large number of transaction records, causing the amount of data to continue to increase every day. To explore sales transaction data, data mining techniques are used. One of the goals of data mining is prediction. Prediction is basically an assumption or estimate about the occurrence of an event or event in the future. Through prediction, it is expected to minimize the influence of uncertainty from the future, so that getting results that have the least prediction error is the goal of prediction. This shows that prediction is a very important tool in planning effectively and efficiently. The discussion method used to predict sales is the time series method by using a comparison of two types of prediction methods, namely the Linear Regression method and the Trend Moment method. The use of these two methods will be a better basis for making decisions to determine which method is suitable for predicting future sales. The result of a prediction cannot always be verified in absolute 100%. Therefore, the parameters used to determine the better method are based on the smallest error accuracy rate calculated using MAPE. Based on the results of the comparative prediction analysis of the Linear Regression method and the Trend Moment method, the recommended prediction result is to use the Trend Momnet method because the resulting MAPE error value is smaller, namely 0.439845%. Meanwhile, the MAPE error value with the Linear Regression method is 1.511509%..