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Wirosari Bus Terminal Performance Analysis Based on Facility Availability and Optimization Through Internet of Things (IoT) Technology Irawan, Arie; Mudiyono, Rachmat; Pratikso, Pratikso
Journal of Engineering Science and Technology Management (JES-TM) Vol. 5 No. 2 (2025): September 2025
Publisher : Journal of Engineering Science and Technology Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jestm.v5i2.316

Abstract

Wirosari Bus Terminal, a Type-C terminal located in Grobogan Regency, plays a crucial role in supporting rural mobility. However, underutilization and non-compliance with Indonesia's Minimum Service Standards (SPM) have significantly hampered its performance. This study evaluates the availability and quality of terminal facilities and proposes a technological optimization model based on the Internet of Things (IoT). Using a quantitative research design, 92 respondents including users, operators, and terminal staff were surveyed. The data were analyzed through multiple linear regression to determine the influence of facility availability (X₁) and technology perception (X₂) on terminal performance (Y). Results indicate that perceived facility availability is low (mean = 2.70), while technology acceptance is high (mean = 4.68). The regression model reveals that both variables significantly affect terminal performance (R² = 0.284, p < 0.05). This study concludes that enhancing physical infrastructure combined with IoT-based digital innovations such as real-time scheduling displays, electronic queuing, and sensor-based monitoring can significantly improve terminal functionality, user satisfaction, and operational transparency, especially in rural contexts.
Modeling Causal Analysis of Crash Severity on Indonesian Toll Road Using Integrated Z-Score and Bayesian Network Framework Istiyanto, Bambang; Pratikso, Pratikso; Mudiyono, Rachmat; Nurrohman, Hafidz
Automotive Experiences Vol 9 No 1 (2026): Issue in Progress
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.15445

Abstract

Traffic crashes remain a critical safety challenge, with Indonesia experiencing 73,446 fatalities annually. This study develops an integrated Z-Score and Bayesian Network framework to analyze causal interactions between human and environmental factors influencing crash severity on toll roads. Z-Score analysis of 450 crash records (2022–2025) identified five statistically significant blackspot segments, with KM 430–431 exhibiting the highest concentration (Z = 4.036, n = 91). A Bayesian Network model constructed using K2 structure learning and Expectation-Maximization parameter estimation achieved 86.2% classification accuracy, surpassing previous international applications (78–82%). Conditional probability analysis revealed that straight-downhill segments exhibited 3.3-fold higher fatal crash probability than straight-level segments (0.083 vs. 0.025), while night-time conditions increased fatal risk by 57%. Sensitivity analysis demonstrated that crash type (weighted index = 0.282) and accident cause (0.214) exerted strongest influence on severity outcomes. Human error constituted 83% of crashes but showed moderate sensitivity, indicating that severe outcomes emerge from interactions between human factors and adverse conditions rather than isolated factors. Findings support prioritizing enhanced lighting and speed management on curved-downhill segments during night-time, alongside rear-end collision prevention strategies. This validated framework enables evidence based, proactive crash management and intervention prioritization for toll road safety in developing countries.