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Journal : Jurnal ULTIMATICS

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.
Peramalan Utilisasi Perangkat Jaringan dan Bandwidth Dengan Metode Holt-Winters dan Multi Layer Perceptron Muhammad Taufiq; 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 (1059.464 KB) | DOI: 10.31937/ti.v12i1.1575

Abstract

Network devices become an important medium for transferring data from one node to another node in the form of switches, routers or network security devices. The reliability of network devices must be maintained both in terms of device resources and bandwidth. The study was conducted by applying the Holt-Winters and Multi Layer Perceptron (MLP) method to network device and bandwidth data utilization. The two methods are compared to assess which accuracy is better when applied to network device and bandwidth utilization data by calculating Root Mean Squared Error (RMSE) and Mean Absolute Percentage (MAPE). The results of the measurement of accuracy in the network device testing data, MLP produces a value of RMSE of 5,67 and MAPE of ​​2.34, and Holt-Winters produces a value of RMSE of ​​14.56 and MAPE of 2.95. For the results of the measurement of accuracy in the bandwidth testing data with MLP produces a value of RMSE of ​​0.13 and MAPE of ​​ 7.27, and Holt-Winters produces RMSE values of ​​2.59 and MAPE of 134.31. Based on the results of these measurements it is concluded that the MLP method has a smaller error value compared to the Holt-Winters method applied to network device and bandwidth utilization data with a span of 3 years historical data.