Prediksi Indeks Harga Produsen Pertanian Karet Di Indonesia Menggunakan Metode LSTM

Abstract

Natural rubber is one of the plantation commodities that has a fairly wide market in international trade because it is needed as a raw material for various industries. Rubber producer prices need to be predicted because producer prices are the first price in the lead from other price levels. So that information about price changes at the producer level is very important as an early warning system against price fluctuations at the next price level. The Long Short-Term Memory (LSTM) algorithm was chosen because it is considered capable of accommodating the problem of predicting the price index of producers in the rubber agriculture sector being faced because, LSTM itself is one of the developments of a neural network, which can be used for time series data modeling and is capable of continuous learning. . Parameter analysis carried out in this study is the number of hidden neurons, epochs and batch size. The best combination of parameters produced in this study is 50 hidden neurons, 25 epochs and batch size 10. The best values ​​generated in this study are the RMSE value of training data 384.20 and the value of RMSE testing 306.01 and the value of MAPE training 1.25% and the value of MAPE testing 1.09%  The best MAPE error calculation in this study is "Predicting the Price Index of Agricultural Rubber Producers in Indonesia Using the Long Short Term Memory Method" which produces the best MAPE. These results indicate that the MAPE error can be said to be very good because the best MAPE value produced is below 10%.