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...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Department of Mathematics, UIN Sunan Ampel Surabaya
2018
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/b652028a1e394deb90516ab38c84fc0c |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | 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. |
|---|