Abstract

Fitness for duty (FFD) is an individual's ability to perform tasks in relation to the physical and mental health of each member and individual readiness to work.The development of a bus driver's work readiness model based on physical, mental, and work aspects is very important so that bus drivers are in a fit condition or ready to work. This study aims to develop a model of Agra Mas Bus driver's fitness for duty using measuring instruments in the form of the Visual Analogue Scale (VAS), Karolinska Sleepiness Scale (KSS) and Quantitative Analysis of Situational Awareness (QUASA). Thirty Agra Mas Bus drivers on the Wonogiri – Kendal – Karawang route participated in the study with independent variables such as age, weight, height, sleep duration, sleep quality, cigarette consumption, caffeine consumption, eye health, shift work, attention level, fatigue, and sleepiness, as well as the dependent variable in the form of FFD results of physical test screening. The results of this study indicate that the study and evaluation of the driver's job readiness level are quite good. That is, 100% of the drivers can be declared ready to be seen from the model that has been made. A total of 96.7% of drivers were correctly predicted, meaning that the predictive power or the accuracy of the model in classifying its observations was 96.7%. The ability of the model to predict the observation value is high, which is 75.8%. The adjusted R2 value of the model is 66.67%. VAS and KSS can be used to evaluate the work readiness of drivers, while QUASA cannot be used. Fatigue and sleepiness affect the driver's work readiness, while attention does not. Fatigue affects the driver's level of sleepiness. The more tired a driver is, the higher the level of sleepiness felt. A shift has no significant effect on attention, fatigue, and sleepiness. Shifts can affect the duration and quality of a driver's sleep. The expected results of this study can be a reference for predicting the fitness for duty of bus drivers so that the risk of road accidents is reduced.

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