Penggunaan Pendekatan Cardiovascular Load (CVL) dan Subjective Workload Assessment Technique (SWAT) Dalam Menganalisis Beban Kerja Driver Online

Semnasti, Akmal Suryadi; Semnasti, Mega Cattleya PA Islami; Semnasti, Romy Trizki Suwardana · 2023 · Crossref

DOI: 10.33005/wj.v16i1.63

archive: archived pipeline: cataloged verified

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Summary

This study investigates the physical and mental workload of online motorcycle taxi drivers, specifically those using the Gojek application in the Lidah Kulon sub-district. The research is motivated by the complex nature of this profession, which combines physical driving tasks with mental demands such as navigation, customer interaction, and app management. Drivers face significant stressors including traffic congestion, extreme weather, long working hours exceeding eight days, and technical issues with the application. These factors contribute to physical fatigue and psychological strain, potentially impacting performance and health. The primary objective was to quantify these workloads to provide data-driven recommendations for improving working conditions and minimizing excessive burden on drivers. The researchers employed a quantitative approach involving 26 active Gojek drivers, selected from a population of 35 using Slovin’s formula with a 10% margin of error. Physical workload was assessed using the Cardiovascular Load (CVL) method, which compares working heart rate against maximum heart rate to determine physiological strain. Mental workload was evaluated using the Subjective Workload Assessment Technique (SWAT), a multidimensional model assessing time load, mental effort, and psychological stress. Data collection involved measuring heart rates during work and rest periods and administering SWAT questionnaires. The validity and reliability of the questionnaire were confirmed using SPSS, with a Cronbach’s Alpha of 0.840. SWAT data were processed using DosBox software to calculate Kendall’s Coefficient of Concordance and perform event scoring for specific job activities. The results indicated that the average physical workload, measured by CVL, was 31%, suggesting a moderate physical burden influenced by traffic, distance, weather, and overtime. However, the mental workload analysis revealed that time load was the dominant factor, contributing 64.43% to the overall workload, followed by mental effort at 23.69% and stress at 11.89%. The Kendall’s Coefficient of Concordance was 0.6713, indicating heterogeneous responses among drivers, which necessitated the use of Individual Scaling Solution rather than a group scale. Event scoring identified that communicating with passengers resulted in the highest mental workload, with 69% of drivers experiencing high load during this activity. Other high-load activities included delivering food and beverages (54%) and delivering goods (50%). The study concludes that while physical strain is present, the temporal pressure and communication demands significantly dominate the workload profile of Gojek drivers. The findings highlight the need for ergonomic and operational interventions. Recommendations include improving the accuracy and stability of the Gojek application to reduce errors that negatively impact driver ratings and income. Additionally, the authors suggest implementing maximum time limits for waiting periods—whether for passengers, food pickup, or deliveries—to streamline workflows and reduce time-related stress. Enhancing transparency in policy changes is also advised to ensure driver acceptance and maintain service quality, ultimately balancing the needs of workers and the company.

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Topics

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• workload measurement
• stress driving
• mental demand

Information type

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• Empirical Findings: physiological data, self report data
• Theoretical Contribution: theory or model