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Traffic accident is one of the most significant contributors which makes the death number is increasing around the world. With the demographic condition from Indonesia, motorcycle driver is the types of the driver that dominated the traffic, therefore increasing the probability of caught in a traffic accident The existing Vehicle Activity Detection System (VADS) mainly focused on the car driver, with the main problem is that the computational time from the system is too high to be implemented on a real-time condition. To solve this problem, in this research, a classification system for abnormal driving behavior from motorcycle drivers is created, using Light Gradient Boosting Machine (LightGBM) model. The system is designed to be lightweight in computation and very fast in response to the changes of the activities with a high velocity. To train the LightGBM model, the data from Accelerometer and Gyroscope sensor, that has been integrated into a smartphone, will be used to detect the movement from a driver. The accuracy rate from the proposed model is reaching 82% on the test dataset and shows a promising result of around 70% on the real-time detection process. With a computational time of around 10ms, the proposed system is able to work 5 times faster than the existing system.


Traffic Accident LightGBM Computational Time Kecelakaan Lalu Lintas LightGBM Waktu Komputasi

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How to Cite
Rachmadi, R. R., Sudarsono, A. ., & Santoso, T. B. . (2021). Application of LightGBM Method for Abnormal Driving Behavior Classification on Motorcycle Driver Based on Smartphone Sensor. Jurnal Komputer Terapan , 7(2), 218–227.


  1. Jusuf, A., Nurprasetio, I. P., & Prihutama, A. “Macro data analysis of traffic accidents in Indonesia”. Journal of Engineering and Technological Sciences. 2017.
  2. Eraqi, H. M., Abouelnaga, Y., Saad, M. H., & Moustafa, M. N. “Driver distraction identification with an ensemble of convolutional neural networks”. Journal of Advanced Transportation.2019.
  3. Shahverdy, M., Fathy, M., Berangi, R., & Sabokrou, M. "Driver behavior detection and classification using deep convolutional neural networks". Expert Systems with Applications. 2020.
  4. Brombacher, P., Masino, J., Frey, M., & Gauterin, F. "Driving event detection and driving style classification using artificial neural networks". Proceedings of the IEEE International Conference on Industrial Technology. 2017.
  5. Nuswantoro, F. M., Sudarsono, A., & Santoso, T. B. "Abnormal driving detection based on accelerometer and gyroscope sensor on smartphone using artificial neural network (ann) algorithm". IES 2020 - International Electronics Symposium: The Role of Autonomous and Intelligent Systems for Human Life and Comfort. 2020.
  6. Matousek, M., El-Zohairy, M., Al-Momani, A., Kargl, F., & Bosch, C. "Detecting anomalous driving behavior using neural networks". IEEE Intelligent Vehicles Symposium, Proceedings. 2019.
  7. Chen, T., & Guestrin, C. "XGBoost : Reliable Large-scale Tree Boosting System". ArXiv. 2016.
  8. Shi, X., Wong, Y. D., Li, M. Z. F., Palanisamy, C., & Chai, C. “A feature learning approach based on XGBoost for driving assessment and risk prediction”. Accident Analysis and Prevention. 2019.
  9. Lu, Y., Fu, X., Guo, E., & Tang, F. “XGBoost Algorithm-Based Monitoring Model for Urban Driving Stress: Combining Driving Behaviour, Driving Environment, and Route Familiarity”. IEEE Access. 2021.
  10. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. “LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems”. 2017
  11. Zeng, H., Yang, C., Zhang, H., Wu, Z., Zhang, J., Dai, G., Babiloni, F., Kong, W., & Chuang, L. “A LightGBM-Based EEG Analysis Method for Driver Mental States Classification”. Computational Intelligence and Neuroscience. 2019.
  12. Gao, X., Luo, H., Wang, Q., Zhao, F., Ye, L., & Zhang, Y. “A human activity recognition algorithm based on stacking denoising autoencoder and lightGBM”. Sensors (Switzerland). 2019.
  13. Choi, S., & Hur, J. “An ensemble learner-based bagging model using past output data for photovoltaic forecasting”. Energies. 2020.
  14. Demir-Kavuk, O., Kamada, M., Akutsu, T., & Knapp, E. W. “Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features”. BMC Bioinformatics. 2011.

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