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All Journal Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG)
Maolani, Rukaesih Achmad
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Other

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Klasifikasi Sifat Cidera Kecelakaan berdasarkan Karakteristik Masyarakat di Jakarta Sarinah, Sarinah; Addini, Fida Fathiyah; Agusinta, Lira; Haryanto, Dwi; Maolani, Rukaesih Achmad; Abdurachman, Edi
Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) Vol 11, No 3 (2024): NOVEMBER
Publisher : Institut Transportasi dan Logistik Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54324/j.mtl.v11i3.1450

Abstract

Traffic accidents are one of the biggest causes of death in Indonesia. Traffic accidents could occur due to various factors such as human factors, facilities, infrastructure, and the environment. These factors are seen as causes of traffic accidents. Furthermore, the impacts experienced due to traffic accidents are not always the same. Someone could be injured or even die. This impact is seen as the nature of an accident injury. This research analyzes and models the relationship between the nature of traffic accident injury variables and the variables causing traffic accidents. The data used in this research is 12,353 traffic accident data in DKI Jakarta from 2020 to 2021. The analysis was carried out using two main methods, namely the chi-square test to determine the existence of a relationship between variables and an ordinal logistics regression model to model the influence of variables causing traffic accidents on variables of the accident injuries nature. Based on the chi-square test, there is a significant relationship between the level of traffic injury and the factors of age, gender, type of work, time and location of the incident. However, there is no significant relationship between the level of the traffic injury and the day of the incident. Variables that are significantly related are then created as an ordinal logistics regression model equation. The results show that the injury level could be predicted based on the causal factors with 92.45% accuracy in classifying the level of the injury.
Co-Authors Addini, Fida Fathiyah Agusinta, Lira Dwi Haryanto Edi Abdurachman Sarinah Sarinah