Modeling Of Hyperparameter Tuned RNN-LSTM and Deep Learning For Garlic Price Forecasting In Indonesia

Abstract

In the Indonesian garlic industry, the unpredictability of garlic prices poses a substantial challenge, impacting the sector's stability and growth. This research aims to address this issue by developing a highly accurate predictive model using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The study employs a dataset spanning 782 days, meticulously divided with 80% dedicated to training and 20% to testing. The model, equipped with 50 LSTM units, undergoes intensive training over 100 epochs, with a batch size of 5. Its effectiveness is evaluated using the Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), revealing exceptional predictive capabilities. The model achieves a low RMSE and MAPE in both training and testing phases, underscoring its accuracy and reliability in forecasting garlic prices. These results indicate not only the success of the RNN-LSTM model in capturing the complex patterns of price fluctuations but also highlight the potential of machine learning in enhancing time series analysis. This breakthrough offers significant implications for stakeholders in the garlic industry, providing a powerful tool for informed decision-making and strategic market planning, thereby contributing to the sector's sustainable development and stability