Analysis of the Artificial Neural Network Approach in the Extreme Learning Machine Method for Mining Sales Forecasting Development

Abstract

Forecasting is an accurate indicator to support management decisions. This study aimed to mining sales forecasting on Indonesia's consumer goods companies with business warehouses engaged in the dynamic movement of large data using the Artificial Neural Network method. The sales forecasting used traditional method by inputting data and improvising simple patterns by collecting historical sales and remaining stock. Furthermore, several data variables in business warehouses were employed for sales forecasting. The study also used qualitative method to investigate the quality of data that cannot be measured quantitatively. The results showed with Mean Square Error score of 0.02716 in forecasting sales. The average accuracy generated by the Extreme Learning Machine after nine data tests is 111%. The result shows an opportunity for the company to further analyze the sales profit growth potential. The predicted value generated by Extreme Learning Machine for the last three months reaches 132%. The company's improved decision-making enlarge potential production line demonstrates the usefulness of this study.