Klasifikasi Sifat Cidera Kecelakaan berdasarkan Karakteristik Masyarakat di Jakarta

Penulis

  • Sarinah Sarinah Institut Transportasi dan Logistik Trisakti
  • Fida Fathiyah Addini Sekolah Tinggi Manajemen Asuransi Trisakti
  • Lira Agusinta Institut Transportasi dan Logistik Trisakti
  • Dwi Haryanto Sekolah Tinggi Manajemen Asuransi Trisakti
  • Rukaesih Achmad Maolani Sekolah Tinggi Manajemen Asuransi Trisakti
  • Edi Abdurachman Institut Transportasi dan Logistik Trisakti

DOI:

https://doi.org/10.54324/j.mtl.v11i3.1450

Kata Kunci:

accidents, chi square test, community characteristics, ordinal logistic regression, risk classification

Abstrak

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.

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Unduhan

Diterbitkan

2024-11-30

Terbitan

Vol 11 No 3 (2024): NOVEMBER

Bagian

Articles

Lisensi

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