FORECASTING PRICES OF FERTILIZER RAW MATERIALS USING LONG SHORT TERM MEMORY

Abstract

This study uses long short term memory (LSTM) modeling to predict time series data on the price of fertilizer raw materials, namely prilled urea, granular urea, ammonium sulphate((NH4)2SO4), ammonia (NH3), diammonium phosphate((NH4)2HPO4 ), phosphoric acid (H3PO4), phosphate rock (P2O5), NPK 16-16-16, potash, sulfur, and sulfuric acid (H2SO4). Predictions are made based on data that existed in the past using the long short term memory method, which is a derivative of the recurrent neural network. Carry out the evaluation process by looking at the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the model that has been created. The results obtained are quite good, as seen from the root mean square error (RMSE) and mean absolute percentage error (MAPE) which are close to 0 and not too high. Sulfur raw material got the smallest root mean square error (RMSE) with a score of 0.053 and diammonium phosphate raw material got the smallest mean absolute percentage error (MAPE) evaluation value with 2.3%, while the largest value was for the root mean square error (RMSE) of raw materials. Phosphoric acid fertilizer raw material with a value of 22,979 and the largest mean absolute percentage error (MAPE) comes from sulfuric acid fertilizer raw material with a value of 9.180%.