Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Multidisiplin Dehasen (MUDE)
Syifa' Ul Haq
Universitas IBA
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Validasi Panjang dan Durasi Kemacetan Google Traffic dengan Survei Lapangan Pada Kasus Putaran Balik di Jl. Kol. H. Burlian Palembang Syifa' Ul Haq; Ramadhani Ramadhani; Pujiono Pujiono
Jurnal Multidisiplin Dehasen (MUDE) Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/mude.v5i1.9638

Abstract

Traffic congestion is one of the common problems in big cities such as Palembang, particularly at U-turn locations which are prone to bottlenecks. This study aims to validate the congestion length and duration data obtained from Google Traffic with direct field survey observations at the U-turn on Jl. Kol. H. Burlian, Palembang. The research method involved primary data collection through field surveys using a Garmin GPS to record congestion length and a Speed Gun to measure vehicle speed. Secondary data were obtained from Google Traffic, processed through Google Sheets and the Google Distance Matrix API. The results showed that Google Traffic data can generally illustrate the congestion patterns, both in terms of queue length and congestion duration. A paired t-test comparison between Google Traffic data and field survey data indicated no significant difference. Therefore, Google Traffic can be utilized as supporting data in congestion mapping, although field validation is still required for more accurate results.
Co-Authors Pujiono Pujiono Ramadhani Ramadhani