Sales Forecasting pada Dealer Motor X Dengan LSTM, ARIMA dan Holt-Winters Exponential Smoothing

Jennifer Soeryawinata, Henry Novianus Palit, Leo Willyanto Santoso


In the world of commerce, inventory is an important issue. Occasionally, motorcycle dealer X experience lost revenue due to a lack of motorcycle inventory as well as lost storage space due to under-selling motorcycles being stocked in large quantities. If the restock process is easy to do, it will answer the problem. Inventory of motorcycles at the motorcycle dealer X was sent from Jakarta to Central Sulawesi. If the motorcycle dealer X wants to do a restock, it will take a long time and expensive shipping costs. To overcome the problems at the motorcycle dealer X, a prediction or forecasting of motorcycle sales is needed. With this forecast, it is hoped that the owner of the motorcycle dealer X can determine the number and type of motorbikes that must be sent from Jakarta each month. In this study, we will use Long-Short Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and HoltWinters Exponential Smoothing to forecast motorcycle sales and then compare their performance using evaluation metrics, such as the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). From this third model, the best model for forecasting is ARIMA with the lowest RMSE (1.1339-5.8936) value for all types of motors and has the lowest MAPE values for three types of motors. If the LSTM model is compared with the HoltWinters model, the LSTM model is better at forecasting with smaller RMSE and MAPE values for most types of motors.


forecasting; sales forecasting; LSTM; ARIMA; HoltWinters; exponential smoothing

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