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p-Index From 2021 - 2026
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All Journal International Journal on Advanced Technology, Engineering, and Information System (IJATEIS)
Martha Leni Siregar
Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Analysis of Human Error Factors in Traffic Accident (Case Study: Tangerang-Merak Toll Road) Dika Ruslaninur Yadi; Martha Leni Siregar; Sutanto Soehodho
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 5 No. 2 (2026): MAY
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v5i2.2230

Abstract

Traffic accidents carry significant social, economic, and public safety consequences. In Indonesia, human error accounts for over 60% of accident cases, based on data from the Indonesian National Police Traffic Corps. The Tangerang-Merak Toll Road, a vital corridor connecting industrial areas and major ports in western Java, has seen a notable increase in accidents in recent years, underscoring the need for a deeper understanding of human error contributions. This study aims to analyze the influence of human error-related factors, including driving behavior, risk perception, environmental factors, and individual characteristics, on traffic accidents on the Tangerang-Merak Toll Road. A quantitative approach using Structural Equation Modeling Partial Least Squares (SEM-PLS) was employed to examine these relationships. Data were collected through observation, literature review, and questionnaires. The model demonstrates strong explanatory power, with an R-square value of 0.606, indicating that 60.6% of the variation in traffic accidents is explained by the included variables. Driving behavior is the most dominant factor, with a path coefficient of 0.669, followed by risk perception (0.248). Environmental factors show a negative relationship (-0.478), suggesting that certain environmental conditions may reduce accident risk. Individual characteristics exhibit smaller and mixed effects, including driving experience (0.134), frequency of toll road usage (-0.149), vehicle type (-0.073), and gender (-0.144).
Driving License Testing Quality and Driver Competence in Urban Traffic Safety Johannes Johannes; Sutanto Soehodho; Martha Leni Siregar
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 5 No. 2 (2026): MAY
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v5i2.2233

Abstract

Road traffic accidents remain a pressing public safety concern in rapidly urbanizing cities. In Bekasi City, accident cases rose from 683 in 2022 to 1,146 in 2025, with a growing proportion of perpetrators found to be licensed drivers, raising questions about whether the driving license (SIM) testing system adequately ensures driver competence. This study examines the relationship between the quality of SIM testing and driver competence, focusing on hazard perception, safety knowledge, and driving behavior in Bekasi City. A quantitative approach was employed using Analysis of Variance (ANOVA) within the General Linear Model (GLM) framework, involving 120 respondents: 60 SIM A (car drivers) and 60 SIM C (motorcycle riders). Data were collected through questionnaires based on official SIM training modules. The results show that for SIM A drivers, the model significantly affects hazard perception and safety knowledge (Sig. = 0.000), but not driving behavior (Sig. = 0.102), with R² values of 0.818, 0.568, and 0.197, respectively. For SIM C riders, the model is significant across all variables (Sig. = 0.000), with R² values of 0.696, 0.633, and 0.562. Age and driving experience significantly influence cognitive aspects, while license ownership shows a consistently strong effect (Sig. = 0.000). Questionnaire accuracy ranged from 78% to 92% across all categories. Despite high cognitive scores, a gap persists between knowledge and actual driving behavior, particularly among SIM A drivers. It is recommended that the system incorporate behavior-based evaluations and real-world driving assessments to improve road safety outcomes.
Co-Authors Dika Ruslaninur Yadi Johannes Johannes Sutanto Soehodho