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


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