PERBANDINGAN KINERJA MODEL LSTM DAN RNN PADA REDIKSI HARGA MINYAK GORENG DI KOTA YOGYAKARTA
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Abstract
This study aims to compare the performance of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) algorithms in predicting cooking oil prices using various unit layer configurations. The experimental results show that RNN, with a 64,32,16-unit layer configuration for predicting the prices of branded cooking oil Brand 2, achieved the best prediction performance, with a Mean Absolute Error (MAE) of 190.043, Root Mean Squared Error (RMSE) of 573.453, and Mean Absolute Percentage Error (MAPE) of 0.859%. Meanwhile, LSTM demonstrated optimal performance in predicting the prices of branded cooking oil Brand 1 with a 32-unit layer configuration, achieving an MAE of 168.233, RMSE of 546.887, and MAPE of 0.778%. Both algorithms have their respective advantages depending on the configuration and data characteristics. This study shows that LSTM is more suitable for highly accurate predictions, while RNN is more effective in capturing specific data patterns. This study provides significant contributions to selecting the optimal algorithm and configuration for commodity price prediction models.
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