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All Journal REKONSTRUKSI TADULAKO: Civil Engineering Journal on Research and Development
Narendra, A.
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Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Engineering Environmental Science Transportation

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Pemodelan Resiko Kecelakaan Berbasis Kondisi Kendaraan dan Pengemudi Putra, A.; Narendra, A.
REKONSTRUKSI TADULAKO: Civil Engineering Journal on Research and Development Vol. 2, Issue 2 (September 2021)
Publisher : Civil Engineering Department, Tadulako University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/renstra.v2i2.332

Abstract

Traffic accidents are particularly prone to occur mainly caused by vehicle speed, vehicle damage, alcohol influence, and fatigue. The study aims to model the risk of vehicle and driver-based accidents occurring across Queensland, Australia. The data in this study used a dataset of accident factors on Queensland state roads totaling 3412 accidents sourced from the Australian state government of Queensland. Research data period from 2001-2019. This research method uses multinomial logistic regression modeling analysis. The results of this study produced several models, namely; (1) Log odds in the risk level of death vs hospitalization will increase by 1,028 if affected by vehicle damage, increase by 0.731 if affected by fatigue, increase by 0.158 if affected by vehicle speed, increase by 0.151 if influenced by alcohol. (2) Log opportunities in the risk level of death vs. medical care will increase by 0.786 if affected by vehicle damage, increase by 0.375 if affected by fatigue, decrease by 0.003 if affected by vehicle speed, decrease by 0.078 if influenced by alcohol. (3) Log odds in the risk of death vs minor injury will increase by 0.484 if affected by vehicle damage, increase by 0.245 if affected by fatigue, decrease by 0.156 if affected by vehicle speed, decrease by 0.266 if influenced by alcohol. (4) Log odds in the risk of death vs property damage will increase by 1,254 if affected by vehicle damage, increase by 0.828 if affected by fatigue, increase by 0.185 if influenced by vehicle speed, increase by 0.128 if influenced by alcohol. The validation test value with crosstab method explains that the accuracy result of level 1 has an accuracy value of 0.99 and inaccuracy of 0.01 then the result of level 2 to level 5 has an accuracy value of 1.
Pengaruh Infrastruktur Transportasi Terhadap Gross Domestic Product (GDP) Per Capita Pada Negara-Negara Anggota ASEAN Wafa, M.I.; Narendra, A.
REKONSTRUKSI TADULAKO: Civil Engineering Journal on Research and Development Vol. 3 Issue 1 (March 2022)
Publisher : Civil Engineering Department, Tadulako University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/renstra.v3i1.416

Abstract

The purpose of this study is to model the relationship between Gross Domestic Product (GDP) and transportation infrastructure in ASEAN member countries. In addition, this study also compares the data predicted by the model with actual data in Indonesia. Transportation infrastructure includes air transportation, sea transportation, and land transportation (railway). The data used is secondary data from the World Bank (World Development Indicator) 1960-2019. The analytical method used in this research is Statistical Descriptive Analysis and Multivariate Regression Analysis. The results of this study resulted in a multivariate regression model Y = 6,478 – 7,871(X2) + 4,875(X4) – 2,390(X5) – 2,27(X7) + 5,569(X8). This model produces a p-value of 2.619E-13 <0.05, which means that this model has a significance of 2.619E-13 and produces an R2 of 0.95 which means the variables X2 Air transport, passengers carried, X4 Quality of port infrastructure, X5 Container port traffic, X7 Rail lines, X8 Railways, passengers carried affect the Gross Domestic Product (GDP) per capita by 95% and the other 5% is influenced by other factors. This regression model in predicting actual data for the case of GDP in Indonesia is 120.84%, meaning that this model cannot be used to predict actual data in the case of GDP in Indonesia.
Co-Authors A. Putra Wafa, M.I.