Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network

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 conve...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mertha Endah Ervina, Rini Silvi, Intaniah Ratna Nur Wisisono
Formato:	article
Lenguaje:	EN
Publicado:	Department of Mathematics, UIN Sunan Ampel Surabaya 2018
Materias:	Resilient Back-propagation (Rprop) Peramalan Penumpang Kereta Api nnfor Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/b652028a1e394deb90516ab38c84fc0c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b652028a1e394deb90516ab38c84fc0c
record_format	dspace
spelling	oai:doaj.org-article:b652028a1e394deb90516ab38c84fc0c2021-12-02T17:37:38ZPeramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network2527-31592527-316710.15642/mantik.2018.4.2.90-99https://doaj.org/article/b652028a1e394deb90516ab38c84fc0c2018-10-01T00:00:00Zhttp://jurnalsaintek.uinsby.ac.id/index.php/mantik/article/view/310https://doaj.org/toc/2527-3159https://doaj.org/toc/2527-3167Train 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.Mertha Endah ErvinaRini SilviIntaniah Ratna Nur WisisonoDepartment of Mathematics, UIN Sunan Ampel SurabayaarticleResilient Back-propagation (Rprop)PeramalanPenumpang Kereta ApinnforMathematicsQA1-939ENMantik: Jurnal Matematika, Vol 4, Iss 2, Pp 90-99 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Resilient Back-propagation (Rprop) Peramalan Penumpang Kereta Api nnfor Mathematics QA1-939
spellingShingle	Resilient Back-propagation (Rprop) Peramalan Penumpang Kereta Api nnfor Mathematics QA1-939 Mertha Endah Ervina Rini Silvi Intaniah Ratna Nur Wisisono Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network
description	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.
format	article
author	Mertha Endah Ervina Rini Silvi Intaniah Ratna Nur Wisisono
author_facet	Mertha Endah Ervina Rini Silvi Intaniah Ratna Nur Wisisono
author_sort	Mertha Endah Ervina
title	Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network
title_short	Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network
title_full	Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network
title_fullStr	Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network
title_full_unstemmed	Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network
title_sort	peramalan jumlah penumpang kereta api di indonesia dengan resilient back-propagation (rprop) neural network
publisher	Department of Mathematics, UIN Sunan Ampel Surabaya
publishDate	2018
url	https://doaj.org/article/b652028a1e394deb90516ab38c84fc0c
work_keys_str_mv	AT merthaendahervina peramalanjumlahpenumpangkeretaapidiindonesiadenganresilientbackpropagationrpropneuralnetwork AT rinisilvi peramalanjumlahpenumpangkeretaapidiindonesiadenganresilientbackpropagationrpropneuralnetwork AT intaniahratnanurwisisono peramalanjumlahpenumpangkeretaapidiindonesiadenganresilientbackpropagationrpropneuralnetwork
_version_	1718379904933822464

Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network

Ejemplares similares