Double exponential smoothing brown method towards sales forecasting system with a linear and non-stationary data trend

Abstract UD Parama Store is a trading company engaged in selling retail goods that sell various types of daily necessities retail. The major problems occurred are the difficulty predicting sales due to maturity level experience, customer demand changes, and owner’s limited memory. Therefore, there should be an increase merchandise stock prevent any sudden decrease overcome shortage when sales. In this current research, forecasting web-based system was designed built assist owner number next period. As result, decisions can made determining provided. method used double exponential smoothing brown by improving forecasting, averaging (smoothing) past value time coherent data, decreasing (exponential), which requires one parameter only. It linear non-stationary data. calculation accuracy using MAPE (Mean Absolute Percentage Error) produces smallest error rate ranging from 7.99% 32.42% for 10 different items.