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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 1, 2023.
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.
Hendra Kurniawan, Joko Triloka and Yunus Ardhan, “Analysis of the Artificial Neural Network Approach in the Extreme Learning Machine Method for Mining Sales Forecasting Development” International Journal of Advanced Computer Science and Applications(IJACSA), 14(1), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140179
@article{Kurniawan2023,
title = {Analysis of the Artificial Neural Network Approach in the Extreme Learning Machine Method for Mining Sales Forecasting Development},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140179},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140179},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {1},
author = {Hendra Kurniawan and Joko Triloka and Yunus Ardhan}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.