Robusta London Coffee Price Forecasting Analysis Using Recurrent Neural Network – Long Short Term Memory (RNN – LSTM)

Ferzy Tryanda Nosa, Dian Kurniasari, Amanto Amanto, Warsono Warsono

Abstract


Coffee price forecasting has a significant role in preventing price fluctuations at a time. Therefore, a method is needed that can be used to forecast the price of coffee. This study discusses the analysis of coffee price forecasting using the Recurrent Neural Network – Long Short-Term Memory (RNN – LSTM) method. This study will be determined the best LSTM model that aims to get the results of forecasting the price of London robusta coffee with the smallest  Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. Using the LSTM model with units of 128 and dropouts of 0.1, forecasting the price of London robusta coffee has an RMSE value of 1,303 and MAPE of 3.53%. Therefore, the LSTM model can indicate the cost of London robusta coffee with an accuracy rate of 96.47%.

 


Keywords


Coffee price forecasting, LSTM, Units, Dropout, RMSE, MAP.

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DOI: http://dx.doi.org/10.26623/transformatika.v20i2.5482

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