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All Journal Sistemasi: Jurnal Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science
Hasabi, Rafif
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Computer Science & IT Electrical & Electronics Engineering

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Urban traffic congestion and its association with gas station density: insights from Google Maps data Hasabi, Rafif; Kurniawan, Robert; Sugiarto, Sugiarto; Tri Wahyuni, Ribut Nurul; Nurmawati, Erna
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1618-1626

Abstract

Analyzing air pollution caused by traffic conditions requires appropriate indicators. Currently, air pollution indicators are approximated by the number of vehicles and gas station density. However, this approach cannot provide information at a smaller level. This study aims to identify traffic congestion distribution from Google Maps data as an alternative air pollution indicator at smaller level using map digitization method. In addition, this study examines its relationship with the existing indicator called gas station density. The results show that the digitization method can map the traffic congestion distribution where most areas in West, North, and Central Jakarta are classified as high traffic. In addition, this study found that there is a strong and significant relationship of 0.58277 between traffic congestion distribution and gas station density. Thus, traffic congestion distribution and gas station density data from Google Maps can be used as an indicator of traffic-related air pollution, especially land transportation. Furthermore, this research is expected to serve as a basis for the government in determining mitigation strategies related to traffic congestion and the resulting emissions.
Utilizing Google Trends Data to Examine the Impact of Unemployment Rates on Indonesia's Gross Domestic Product Jane, Giani Jovita; Hasabi, Rafif; Purnatadya, Sinatrya Dwi; Kartiasih, Fitri
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.3603

Abstract

Abstract Data related to the economy have varying frequencies and have delays in publication time. Such as data on the Open Unemployment Rate (OUR) with a semi-annual frequency and Gross Domestic Product at Constant Prices (riil GDP) according to expenditure with a quarterly frequency. So, frequency conversion is required to conduct simple regression modelling using these data. On the other hand, big data such as Google Trends is an additional predictor to estimate OUR and GDP data to overcome delays in publication time. Then the estimated data is modelled to investigate the effect of OUR on GDP. Data conversion uses the Chow-Lin method, while estimation with Google Trends data uses robust regression. The study shows that the estimation results using Google Trends as an additional predictor provide more accurate results than without Google Trends data for OUR and GDP data. Based on the robust regression results, it can be concluded that the OUR has a negative and significant effect on GDP. The findings provide valuable insights for supporting sustainable economic policy and further research on economic analysis.
Co-Authors Fitri Kartiasih Jane, Giani Jovita Nurmawati, Erna Purnatadya, Sinatrya Dwi Ribut Nurul Tri Wahyuni Robert Kurniawan, Robert Sugiarto S