Comparison of Long Short Term Memory (LSTM) and LightGBM Algorithms to Improve Inventory Stock Efficiency through Forecasting - George Rivaldo; Siti Maesaroh (Jurnal Inovatif : Inovasi Teknologi Informasi dan Informatika, 2024)
This research aims to address the growing challenges faced by e-commerce businesses in inventory management, particularly the need for accurate forecasting of outgoing goods. The study focuses on comparing the performance of two machine learning algorithms: Long Short Term Memory (LSTM) and LightGBM. Accurate forecasting is crucial to minimize issues such as overstock and stockouts, which can adversely affect profitability. Using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), this study analyzes historical sales data to evaluate the predictive accuracy of both models. The results indicate that the LSTM model provides more accurate predictions compared to LightGBM, demonstrating its effectiveness as a forecasting tool in inventory stock management. These findings highlight the importance of employing advanced machine learning techniques to enhance inventory efficiency, and ultimately, to aid businesses in better decision-making and improved profitability.