Optimization of Backpropagation Algorithms for Enhancing Market Prediction Accuracy in Emerging Industries
DOI:
https://doi.org/10.37859/coscitech.v7i1.11122
Abstract
This research aims to systematically review and analyze the application of backpropagation algorithms in data-driven business market prediction, focusing on emerging industries. Using the Systematic Literature Review (SLR) method, this study examined research articles from the Dimensions and Scopus databases published over the last 10 years. The analysis synthesizes findings related to the effectiveness, challenges, and potential advancements of Backpropagation in improving market prediction accuracy. The results reveal that backpropagation models, particularly LSTM and MLP have shown significant promise in various sectors, including financial forecasting, customer behavior analysis, and sales prediction. However, challenges such as overfitting, high computational costs, and the integration of real-world market complexities remain. The study emphasizes the need for continuous optimization of these models, as well as improvements in data quality and computational efficiency. This research contributes valuable insights for enhancing predictive models in business market forecasting and offers directions for future studies to further refine the use of backpropagation in addressing market prediction challenges.
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