Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction

Muhammad Alfathan Harriz, Nurhaliza Vania Akbariani, Harlis Setiyowati, Handri Santoso


This study is based on a machine learning algorithm known as XGBoost. We used the XGBoost algorithm to forecast the capacity of Jakarta's mass transit system. Using preprocessed raw data obtained from the Jakarta Open Data website for the period 2020-2021 as a training medium, we achieved a mean absolute percentage error of 69. However, after the model was fine-tuned, the MAPE was significantly reduced by 28.99% to 49.97. The XGBoost algorithm was found to be effective in detecting patterns and trends in the data, which can be used to improve routes and plan future studies by providing valuable insights. It is possible that additional data points, such as holidays and weather conditions, will further enhance the accuracy of the model in future research. As a result of implementing XGBoost, Jakarta's transportation system can optimize resource utilization and improve customer service in order to improve passenger satisfaction. Future studies may benefit from additional data points, such as holidays and weather conditions, in order to improve XGBoost's efficiency.


Jakarta; Mass Rapid Transit; XGBoost

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Abdulrazzaq, L. R., Abdulkareem, M. N., Mat Yazid, M. R., Borhan, M. N., & Mahdi, M. S. (2020). Traffic congestion: the shift from private cars to public transportation. Civil Engineering Journal, 6(8), 1547–1554. doi: 10.28991/cej-2020-03091566

Azhar, H. N., Fatima, H. H. P., & Tamas, I. N. (2020). Preliminary study of Indonesia capital city relocation based on disaster mitigation principle with mental model approach. E3S Web of Conferences, 148, 06002. doi: 10.1051/e3sconf/202014806002

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623. doi: 10.7717/peerj-cs.623

Data Penumpang MRT 2021 di Provinsi DKI Jakarta—Open Data Jakarta. (n.d.). Retrieved March 20, 2023, from https://data.jakarta.go.id/dataset/data-penumpang-mrt-2021-di-provinsi-dki-jakarta

Data Penumpang MRT di Provinsi DKI Jakarta—Open Data Jakarta. (n.d.). Retrieved March 20, 2023, from https://data.jakarta.go.id/dataset/data-penumpang-mrt-di-provinsi-dki-jakarta

Deng, A., Zhang, H., Wang, W., Zhang, J., Fan, D., Chen, P., & Wang, B. (2020). Developing computational model to predict protein-protein interaction sites based on the XGBoost algorithm. International Journal of Molecular Sciences, 21(7), doi: 10.3390/ijms21072274

Deng, X., Ye, A., Zhong, J., Xu, D., Yang, W., Song, Z., Zhang, Z., Guo, J., Wang, T., Tian, Y., Pan, H., Zhang, Z., Wang, H., Wu, C., Shao, J., & Chen, X. (2022). Bagging–XGBoost algorithm-based extreme weather identification and short-term load forecasting model. Energy Reports, 8, 8661–8674. doi: 10.1016/j.egyr.2022.06.072

Fan, C., Chen, M., Wang, X., Wang, J., & Huang, B. (2021). A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Frontiers in Energy Research, 9, 652801. doi: 10.3389/fenrg.2021.652801

Febriani, D., Mega Olivia, C., Aniisah Sholilah, S., & Hidajat, M. (2020). Analysis of modal shift to support MRT-based urban transportation in Jakarta. Journal of Physics: Conference Series, 1573(1), 012015. doi: 10.1088/1742-6596/1573/1/012015

Majid, R. A., Said, R., Mohamad, N., Abdullah, J., & Ngah, R. (2020). The impact of time attribute on mass rapid transport (MRT) ridership in Malaysia. International Journal of Social Science Research, 2(4).

Paleczek, A., Grochala, D., & Rydosz, A. (2021). Artificial breath classification using XGBoost algorithm for diabetes detection. Sensors, 21(12), 4187. doi: 10.3390/s21124187

Pan, S., Zheng, Z., Guo, Z., & Luo, H. (2022). An optimized XGBoost method for predicting reservoir porosity using petrophysical logs. Journal of Petroleum Science and Engineering, 208, 109520. doi: 10.1016/j.petrol.2021.109520

Prakash, T. N., & Aloysius, A. (2019). Data preprocessing in sentiment analysis using twitter data. International Educational Applied Research Journal (IEAJR). 3 (7), 89–92.

Rachman, F. F., Nooraeni, R., & Yuliana, L. (2021). Public opinion of transportation integrated (Jak Lingko), in DKI Jakarta, Indonesia. Procedia Computer Science, 179, 696–703. doi: 10.1016/j.procs.2021.01.057

Ramana, A. V. (2022). Taxi demand prediction using ML. International Journal for Research in Applied Science and Engineering Technology, 10(6), 3811–3815. doi: 10.22214/ijraset.2022.43912

Ranganathan, G. (2021). A study to find facts behind preprocessing on deep learning algorithms. Journal of Innovative Image Processing, 3(1), 66–74. doi: 10.36548/jiip.2021.1.006

Shen, E. Z. (2022). Short-time cab speed prediction model based on XGBoost [Preprint]. In Review. doi: 10.21203/rs.3.rs-2200855/v1

Sinaga, S. M., Hamdi, M., Wasistiono, S., & Lukman, S. (2019). Model of implementing bus rapid transit (BRT) mass public transport policy in DKI Jakarta province, Indonesia. International Journal of Science and Society, 1(3). doi: 10.54783/ijsoc.v1i3.51

Solihati, K. D., & Indriyani, D. (2021). Managing artificial intelligence on public transportation (case study Jakarta city, Indonesia). IOP Conference Series: Earth and Environmental Science, 717(1), 012021. doi: 10.1088/1755-1315/717/1/012021

Tiong, K. Y., Ma, Z., & Palmqvist, C.-W. (2023). A review of data-driven approaches to predict train delays. Transportation Research Part C: Emerging Technologies, 148, 104027. doi: 10.1016/j.trc.2023.104027

Utami, D. L., Putri, A. L., Sutomo, A. H., & Ismara, K. I. (2022). Design of ergonomic emergency car toilet seats as a solution to severe traffic in Jakarta, Indonesia. 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). doi: 10.54941/ahfe1002004

Zhang, R., Li, B., & Jiao, B. (2019). Application of XGboost algorithm in bearing fault diagnosis. IOP Conference Series: Materials Science and Engineering, 490, 072062. doi: 10.1088/1757-899X/490/7/072062

Zhu, L., Shu, S., & Zou, L. (2022). XGBoost-based travel time prediction between bus stations and analysis of influencing factors. Wireless Communications and Mobile Computing, 2022, 1–25. doi: 10.1155/2022/3504704

DOI: https://doi.org/10.37905/jji.v5i1.18814


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