TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING
Trains as a means of public transportation have an important role in connecting various regions of Jabodetabek. Therefore, it is necessary to have a deep understanding of the trend of train passenger movements and predict the number of train passengers in the next period in order to optimize the man...
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| Format: | Article |
| Language: | English |
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Universitas Pattimura
2025-04-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13985 |
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| author | Bashir Ammar Hakim Billy Billy Khairil Anwar Notodiputro Yenni Angraini Laily Nissa Atul Mualifah |
| author_facet | Bashir Ammar Hakim Billy Billy Khairil Anwar Notodiputro Yenni Angraini Laily Nissa Atul Mualifah |
| author_sort | Bashir Ammar Hakim |
| collection | DOAJ |
| description | Trains as a means of public transportation have an important role in connecting various regions of Jabodetabek. Therefore, it is necessary to have a deep understanding of the trend of train passenger movements and predict the number of train passengers in the next period in order to optimize the management and service of train passengers properly. In this study, we examine two methods that can be used as forecasting methods for train passenger data sourced from the Central Statistics Agency (BPS), namely ARIMA and Prophet. This study demonstrates that the optimal ARIMA model is ARIMA (0,2,1), achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and a Root Mean Square Error (RMSE) of 1754.970. In addition, the Prophet model, which is an additive regression model designed by Facebook for time series forecasting was also obtained with a MAPE of 0.04% and an RMSE of 1170.59. Considering the MAPE and RMSE values of the two models, the Prophet model emerges as the most suitable for forecasting the number of train passengers in the Jabodetabek region. |
| format | Article |
| id | doaj-art-c52a5cec539849d9a1881deb6aed07d7 |
| institution | DOAJ |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-c52a5cec539849d9a1881deb6aed07d72025-08-20T03:05:39ZengUniversitas PattimuraBarekeng1978-72272615-30172025-04-0119275576610.30598/barekengvol19iss2pp755-76613985TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTINGBashir Ammar Hakim0Billy Billy1Khairil Anwar Notodiputro2Yenni Angraini3Laily Nissa Atul Mualifah4Department of Statistics, School of Data Science, Mathematics, and Informatics, IPB University, IndonesiaDepartment of Statistics, School of Data Science, Mathematics, and Informatics, IPB University, IndonesiaDepartment of Statistics, School of Data Science, Mathematics, and Informatics, IPB University, IndonesiaDepartment of Statistics, School of Data Science, Mathematics, and Informatics, IPB University, IndonesiaDepartment of Statistics, School of Data Science, Mathematics, and Informatics, IPB University, IndonesiaTrains as a means of public transportation have an important role in connecting various regions of Jabodetabek. Therefore, it is necessary to have a deep understanding of the trend of train passenger movements and predict the number of train passengers in the next period in order to optimize the management and service of train passengers properly. In this study, we examine two methods that can be used as forecasting methods for train passenger data sourced from the Central Statistics Agency (BPS), namely ARIMA and Prophet. This study demonstrates that the optimal ARIMA model is ARIMA (0,2,1), achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and a Root Mean Square Error (RMSE) of 1754.970. In addition, the Prophet model, which is an additive regression model designed by Facebook for time series forecasting was also obtained with a MAPE of 0.04% and an RMSE of 1170.59. Considering the MAPE and RMSE values of the two models, the Prophet model emerges as the most suitable for forecasting the number of train passengers in the Jabodetabek region.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13985arimapassengerprophettime seriestrain |
| spellingShingle | Bashir Ammar Hakim Billy Billy Khairil Anwar Notodiputro Yenni Angraini Laily Nissa Atul Mualifah TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING Barekeng arima passenger prophet time series train |
| title | TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING |
| title_full | TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING |
| title_fullStr | TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING |
| title_full_unstemmed | TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING |
| title_short | TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING |
| title_sort | time series model for train passenger forecasting |
| topic | arima passenger prophet time series train |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13985 |
| work_keys_str_mv | AT bashirammarhakim timeseriesmodelfortrainpassengerforecasting AT billybilly timeseriesmodelfortrainpassengerforecasting AT khairilanwarnotodiputro timeseriesmodelfortrainpassengerforecasting AT yenniangraini timeseriesmodelfortrainpassengerforecasting AT lailynissaatulmualifah timeseriesmodelfortrainpassengerforecasting |