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, 2026)
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.