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Steven Sen
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Komparasi Metode Multilayer Perceptron (MLP) dan Long Short Term Memory (LSTM) dalam Peramalan Harga Beras Steven Sen; Dedy Sugiarto; Abdul Rochman
Ultimatics : Jurnal Teknik Informatika Vol 12 No 1 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (621.82 KB) | DOI: 10.31937/ti.v12i1.1572

Abstract

Rice is one of the main commodities in Indonesian society. The main problem with rice nationally is inflation of rice prices. Therefore, this research predicts the price of rice using Multilayer Perceptron (MLP) artificial neural network architecture and deep learning: Long Short Term Memory (LSTM) to anticipate these problems. The data used in this study are real data on rice prices during 2016 - 2019 obtained from PT. Food Station. The total dataset is 1307 with the distribution of 1123 as data train and 184 as test data. The final results obtained in this study are LSTM superior to MLP, with the value of Root Mean Square Error (RMSE) training data: 0.49 RMSE loss value of test data is 0.27. The most optimal LSTM model from 3 tests was carried out, namely the number of hidden layers = 16 and epochs = 150 times.
Co-Authors Abdul Rochman Dedy Sugiarto