Rasyid, Halim
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Analisis Faktor Risiko Kecelakaan Lalu Lintas terhadap Tingkat Keparahan Korban Rasyid, Halim; Ratih Eka Sakti
Kaganga:Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora Vol. 9 No. 3 (2026): Kaganga: Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/wsr16d44

Abstract

This study aimed to analyze the risk factors influencing the severity of road traffic accident victims within the jurisdiction of the Metro Jaya Regional Police. A quantitative approach employing an explanatory and cross-sectional research design was adopted. The study utilized 26,626 road traffic accident records obtained from the Integrated Road Safety Management System (IRSMS) of the Indonesian National Traffic Corps during the 2023–2025 period. Data were analyzed using descriptive statistics, the Chi-Square test, and Ordinal Logistic Regression with IBM SPSS Statistics at a 5% significance level. The findings indicate an increasing trend in road traffic accidents throughout the study period, with motorcycles and individuals in the productive age group representing the highest proportion of accident victims. The Chi-Square analysis revealed that human, vehicle, road, and environmental factors were significantly associated with injury severity (p<0.05). Furthermore, ordinal logistic regression demonstrated that the human factor was the most dominant predictor of injury severity, with an Odds Ratio (OR) of 6.31, followed by vehicle factors (OR=2.51), road factors (OR=1.96), and environmental factors (OR=1.74). The study concludes that improving road safety should prioritize enhancing road user compliance, increasing the use of safety equipment, improving vehicle roadworthiness, and upgrading road infrastructure to support evidence-based road safety policies. Keywords: Injury Severity, Integrated Road Safety Management System (IRSMS), Ordinal Logistic Regression, Risk Factors, Road Traffic Accidents.