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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Made Srinitha Millinia Utami
Department of Information Technology, Udayana University
Computer Science & IT

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The Use of XGBoost Algorithm to Analyse the Severity of Traffic Accident Victims I Made Sukarsa; Ni Kadek Dwi Rusjayanthi; Made Srinitha Millinia Utami; Ni Wayan Wisswani
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 14 No 1 (2023): Vol. 14, No. 1 April 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i01.p04

Abstract

Traffic accidents are still significant contributors to a fairly high death. Denpasar’s resort police record every traffic accident in the form of a daily report. The stored data can generate valuable information to improve policies and propagate better traffic practices. This research utilizes the classification technique with the XGBoost, random forest algorithm, and SMOTE method. The study shows that the SMOTE technique can increase the model's accuracy. Using the classification method with the two algorithms produces factors that affect the severity of traffic accident victims with feature importance. The feature importance obtained using the XGBoost model by counting the weight value for testing using the original dataset, the dataset for the type of two-wheeled vehicle, and the dataset of the kind of vehicle other than two-wheeled indicate that the variables influencing the severity of victims in road accidents are the time of accident between 00.00-06.00, the type of vehicle motorcycle, the type of opponent vehicle truck and pickup car, the age of the driver between 16-25, sub-district road status and front – side type of accident.
Co-Authors Dwi Rusjayanthi, Dwi I Made Sukarsa Ni Wayan Wisswani