PERAMALAN DATA PENUMPANG KERETA API JANUARI 2013-NOVEMBER 2018 DENGAN MENGGUNAKAN MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM- RECURRENT NEURAL NETWORK (MODWT-RNN)

*Mira Andriyani  -  Departemen Matematika, FMIPA, Universitas Gadjah Mada, Indonesia
Subanar Subanar  -  Departemen Matematika, FMIPA, Universitas Gadjah Mada, Indonesia
Received: 26 Jun 2019; Published: 30 Dec 2019.
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Abstract
The train is one of the public transportation that is very popular because it is affordable and free of congestion. There is often a buildup of train passengers at the station so that it sometimes causes an accumulation of passengers at the station and makes the situation at the station to be not conducive. In order to avoid a buildup of passengers, forecasting the number of passengers can be done. Forecasting is determined based on data in previous times. Data of train passengers in Java (excluding Jabodetabek) forms a non-stationary and contains nonlinear relationships between the lags. One of the nonlinear models that can be used is Recurrent Neural Network (RNN). Before RNN modeling, Maximal Overlap Wavelet Transform (MODWT) was used to make data more stationary. Forecasting model of train passengers in Java excluding Jabodetabek, Indonesia using MODWT-RNN results forecasting with RMSE is 252.85, while RMSE of SARIMA and RNN are 434.97 and 320.48. These results indicate that the MODWT-RNN model gives a more accurate result than SARIMA and RNN.
Keywords
MODWT; RNN; Train Passengers

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