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All Journal JARES (Journal of Academic Research and Sciences) Advance Sustainable Science, Engineering and Technology (ASSET)
E. Gunawan, Fergyanto
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Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Social Sciences

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ANALYSIS OF FACTORS CONTRIBUTING TO WORK STRESS AMONG EXPATRIATE AIRLINES CREW IN SAUDI ARABIA Molina, Prima; Teresa; V.V Satvika, Maharani; E. Gunawan, Fergyanto
JARES (Journal of Academic Research and Sciences) Vol 9 No 1 (2024): March 2024
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/jares.v9i1.3312

Abstract

Work stress can significantly impact the performance of cabin crew in the airline industry, potentially leading to job errors with critical implications for safety, a paramount concern in the industry. The research focuses on factors influencing work stress among cabin crew, providing a valuable reference for improving both service quality and safety in the industry. The potential key factors that affect work stress, including workload, cross-cultural adjustment, rotating work schedules, interpersonal relationships, organizational relationships, and physical demands, are established through extensive literature reviews. The relevant data were collected from a sample of cabin crews in Saudi Arabia representing various nationalities. The most pertinent factors for the problem setting are determined by applying a multivariate regression analysis, where the theoretical understanding is statistically verified. The analysis suggests that the theory is supported by the sample data with a fitness level of 0.85 in terms of the determination coefficient, implying strong materialization of the theory. Furthermore, the agreement between theory and reality is supported by the ANOVA and t-tests. The study concludes that workload is the most dominant factor affecting work stress, followed by organizational relationships and rotating work schedules. Statistically less significant factors include cross-cultural adjustment, interpersonal relationships, and physical demands.
High-Resolution Smart Card-Based OD Matrix for Optimizing Jakarta’s LRT Operations Fadillah, Ikhsan Rahmat; E. Gunawan, Fergyanto
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2254

Abstract

Efficient urban mobility is essential to support transportation planning and policy. However, traditional methods are often limited in data resolution, lacking the ability to describe passenger movement dynamics in detail. This study aims to analyze passenger mobility patterns using high-resolution tap-in/tap-out data from the closed-loop LRT system in Jakarta during January-February 2025. The methods used include constructing an origin-destination (OD) matrix based on 185,512 trip records, as well as temporal and spatial analysis of passenger flows. The results showed the existence of peak hour patterns on weekdays (07.00-09.00 and 17.00-19.00), trip spikes on weekends and holidays (14.00-18.00), and high flow concentrations at interchange stations such as Velodrome and North Boulevard. While data from the closed system allows for accurate trip tracking, potential data gaps due to technical errors or user behavior remain a concern for long-term analysis. The findings suggest that high-resolution smart card data can provide operationally relevant insights for short-term decision-making, such as schedule adjustments or fleet allocation. However, for long-term strategic planning, integration with predictive models and other planning tools remains necessary. This research fills a gap in the literature by showing that even limited-duration datasets can be leveraged to effectively support data-driven transportation management.
Co-Authors Fadillah, Ikhsan Rahmat Molina, Prima Teresa V.V Satvika, Maharani