Pemodelan Regresi Logistik Biner terhadap Tingkat Keparahan Kecelakaan Lalu Lintas di Kabupaten Lamongan
DOI:
10.29303/jm.v8i2.12121Published:
2026-06-13Downloads
Abstract
Abstract
Traffic accidents are a transportation safety issue that have significant social and economic impacts. This study aims to analyze the factors influencing traffic accident severity in Lamongan Regency using binary logistic regression. The data used were secondary data on 1,206 traffic accident cases from 2024 obtained from the Lamongan Police. The response variables were classified into two categories: minor accidents and serious accidents. Predictor variables included the number of fatalities, the number of minor injuries, the time of the accident, the type of accident, the cause of the accident, the road status, the road condition, and the road type. The analysis was conducted using binary logistic regression with a stepwise variable selection method based on the Akaike Information Criterion (AIC). The results showed that the number of fatalities, the number of minor injuries, the time of the accident, the type of accident, the road status, and the road type significantly influenced traffic accident severity. The resulting model achieved a classification accuracy of 78.11%, enabling a reasonably good classification of accident severity.
Keywords:
binary logistic regression traffic accidents accident severity road safety ClassificationReferences
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Copyright (c) 2026 Kurnia Indah Wulandari, Siti Alfiatur Rohmaniah, Mohammad Syaiful Pradana
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