Open AccessPeramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network
Author(s) -
Mertha Endah Ervina,
Rini Silvi,
Intaniah Ratur Wisisono
Publication year - 2018
Publication title -
mantik
Language(s) - English
Resource type - Journals
eISSN - 2527-3167
pISSN - 2527-3159
DOI - 10.15642/mantik.2018.4.2.90-99
Subject(s) - rprop , mean absolute percentage error , artificial neural network , computer science , backpropagation , artificial intelligence , recurrent neural network , types of artificial neural networks
Train scheduling affects the level of customer satisfaction and profitability of the train service provider. The prediction method of Back-propagation Neural Network (BPNN) has relatively slow convergence. Therefore, this study uses Resilient Back-propagation (Rprop) because it has a more fast convergence and high accuracy. The model produced is a model for Jabodetabek, Java (non-Jabodetabek), Sumatra, and Indonesia. From the results of data analysis conducted, it can be concluded that the performance of neural network model with Resilient Back-propagation (Rprop) formed from training data gives very accurate prediction accuracy level with mean absolute percentage error (MAPE) less than 10% for each model. Then forecasting for the next 12 months conducted and the results compared with the data testing, Rprop provides a very high forecasting accuracy with MAPE value below 10%. The MAPE value for each forecasting the number of rail passengers is 7.50% for Jabodetabek, 5.89% for Java (non-Jabodetabek), 5.36% for Sumatra and 4.80% for Indonesia. That is, four neural network architectures with Rprop can be used for this case with very accurate forecasting results.