|Year : 2020 | Volume
| Issue : 3 | Page : 148-152
Fatigue analysis to evaluate workloads in production area at crumb rubber factories of Padang city, West Sumatra Indonesia
Taufiq Ihsan1, Tivany Edwin1, Yasinta Azwir1, Vioni Derosya2
1 Department of Environmental Engineering, Faculty of Engineering, Universitas Andalas, Padang, West Sumatra, Indonesia
2 Department of Agroindustrial Technology, Faculty of Agriculture Universitas Andalas Kampus Limau Manis, Padang, Indonesia
|Date of Submission||22-Oct-2019|
|Date of Decision||27-Nov-2019|
|Date of Acceptance||24-Apr-2020|
|Date of Web Publication||14-Dec-2020|
Universitas Andalas, Kampus Limau Manis, Padang, West Sumatra
Source of Support: None, Conflict of Interest: None
Background: Work fatigue had the potential to cause work accidents that had an impact on losses to the company. This study aimed to analyze the level of subjective-work fatigue and evaluated the effect of workload on fatigue in the production area at The Crumb Rubber Factories of Padang City, West Sumatra, Indonesia. Methods: The research respondents were all workers in the production area in the three largest crumb rubber factories in Padang, totaling 348 workers. The respondents consisted of 135 workers in the wet division and 213 workers in the dry division. Subjective fatigue analysis uses the Industrial Fatigue Research Committee (IFRC) questionnaire method. Measures workload based on Indonesian Standards No. 7269 of 2009 concerning Workload Assessment based on Calorie Level according to Energy Expenditures. Results: The percentage of work fatigue in the production area was 26.32% light level, 72.63% medium level, and 1.05% heavy level. The workload had a significant effect on work fatigue, with a P value of 0.003. Conclusion: On the basis of the results of multiple regression analysis of all the variable characteristics of the respondents and work area, the most influential factor affecting the occurrence of fatigue in a crumb rubber factory was the workload. All crumb rubber factories in Padang should make improvements related to workload in controlling work fatigue.
Keywords: Fatigue, health occupations, workloads
|How to cite this article:|
Ihsan T, Edwin T, Azwir Y, Derosya V. Fatigue analysis to evaluate workloads in production area at crumb rubber factories of Padang city, West Sumatra Indonesia. Indian J Occup Environ Med 2020;24:148-52
|How to cite this URL:|
Ihsan T, Edwin T, Azwir Y, Derosya V. Fatigue analysis to evaluate workloads in production area at crumb rubber factories of Padang city, West Sumatra Indonesia. Indian J Occup Environ Med [serial online] 2020 [cited 2021 Jan 24];24:148-52. Available from: https://www.ijoem.com/text.asp?2020/24/3/148/302819
| Introduction|| |
Many factors cause work accidents, one of which was fatigue. Work fatigue contributed 50% to work accidents. Recorded in 2018, in Indonesia experienced 173,105 accident cases. Fatigue was a change that occurs in the body due to excessive physical or mental activity. This condition will cause an adverse impact in the form of losses for industry.
The same thing happened to for crumb rubber factories in Padang City, Indonesia’s West Sumatra Province. Rubber was one of the leading agricultural commodities in West Sumatra. Kota Padang, as the provincial capital, had the three largest crumb rubber factories. Although the vision of zero accident has been established, in 2018 in the three factories, 21 cases of accidents occurred, of which 10 cases originated from worker fatigue.
Work fatigue could be measured subjectively. Subjective symptoms were feelings of fatigue felt by workers who experience work fatigue. There were various types of questionnaire methods used in the measurement of subjective fatigue, including the Industrial Fatigue Research Committee (IFRC) questionnaire method. The advantages of the IFRC questionnaire method were grouping questions based on symptoms of work fatigue. The symptoms consist of signs that indicated a weakening of activities, weakened work motivation, and physical fatigue. The IFRC questionnaire method was socialized and published in the proceedings of the Symposium on Methodology of Fatigue Assessment in Kyoto, Japan, in 1969 by K. Hashimoto, K. Kogi, and E. Grandjean. The IFRC questionnaire method was used because the measurement result can be obtained quickly. In addition, there have been many studies on subjective work fatigue measurement using this questionnaire method.
Factors that influence work fatigue include workload. If the workload does not match the ability of workers, it will result in work fatigue that will affect the quality of work results. Calculation of workload refers to Indonesian Standards No. 7269 of 2009 concerning Workload Assessment based on Calorie Level according to Energy Expenditures. In addition to workload, in this study, work fatigue was also associated with age and length of work.
Work fatigue analysis using IFRC questionnaire method was carried out in the production area of the three largest rubber factories in Padang City, West Sumatra Province, Indonesia. The purpose of this study was to analyze the level of work fatigue in the production area of Crumb Rubber Factories at Padang City and investigated the effect of workload, respondent characteristics, and work area on work fatigue in the production area of crumb rubber factories workers at the Padang City.
| Materials and Methods|| |
Research locations in the production area of the three largest crumb rubber factories in Padang City can be seen in [Figure 1]. In the implementation of business activities, the production area of the crumb rubber factory was divided into wet and dry division. All production area workers in the three rubber factories were respondents of the IFRC questionnaire. Distributing questionnaires in the production area to all 348 workers consisted of 135 workers in the wet-division and 213 workers in the dry-division. Next, made direct observations in the three factories. Monitoring the work area of production by observing, workers see the workload and interviews with workers who work in the production area. Then data processing includes calculation of work fatigue score, workload calculation, and analysis of the relationship and influence between two variables (bivariate analysis). Work fatigue scores can be known from the answers to each question on the IFRC questionnaire to facilitate further data processing; the IFRC questionnaire contains 30 items. Questions in the IFRC questionnaire can be seen in [Table 1]. The results of work fatigue data in the production area were displayed descriptively based on research variables, namely, workload, age, and work period.
Next, this study calculates workloads based on Indonesian Standard No. 7269 of 2009 (Workload Assessment based on Calorie Level according to Energy Expenditures). The steps are as follows:
a. Calculate the value of basal metabolism (BM):
BM is the minimum energy needed by the body to maintain basic life processes in units of calories per unit time. BM for men and women, namely:
Male BM = body weight (kg) × 1 Kcal/h (1)
Women BM = body weight (kg) × 0.9 Kcal/h (2)
b. Calculate the value of the workload (Wl):
Wl average = (Wl1 × T1)+(Wl2 × T2)+(Wl3 ×
T3)+. +(Bkn × Tn) × 60 Kcal (3)
T1 + T2 + T3+. + Tn hour
where Wl is the workload per hour; Wl 1, Wl 2, Wl 3, Wl n = workload according to work activities 1, 2, 3, n; T = time in minutes; and T 1, T 2, T 3, T n = time according to work activities 1, 2, 3, n (min)
Examples of estimated workloads according to energy requirements can be seen in [Table 2]
c. Calculate total workload (TW):
TW = MB + Wl (4)
Workload categories were divided based on energy expenditure consisting of light workloads of 100–200, medium workloads of 200–350, and heavy workloads of 350–500 kcal/h.
Data analysis was performed to analyze the relationship and influence between two variables (bivariate analysis). Before the data analysis was performed, a data normality test was performed to determine whether the variables studied were normally distributed or not. This data normality test used the Kolmogorov–Smirnov test. When data were normally distributed, then the bivariate analysis was carried out on two variables. That was thought to be interconnected and influential, namely the relationship of independent variables (workload, age, and work period) to the dependent variable (fatigue).
| Results|| |
The percentage of respondent’s type of work in each work area in the Crumb Rubber factories of Padang can be seen in [Table 3]. As shown in [Table 3], the wet-division had the highest rate, namely milling 31.82% of the total respondents and natural-drying 29.55%. Meanwhile, in the dry-division, the highest rate of work, namely bandela unloading was 23.53%, and packaging was 17.65% of the total respondents.
As shown in [Table 4], the most age in the production area in three factories in the city of Padang were in the category of adult age with an age range of 26–45 years. This age range was a productive age at work so that the number of workers with adult age in the production area can increase work productivity. Furthermore, in [Table 3], the most work periods in the production area of rubber factories in the city of Padang are in the category ten years. These data showed that workers had experience in carrying out work in the production area. Next, workers in the rubber factory production area in Padang City were mostly male. Because men had greater muscle strength than women, so that they were more optimal in carrying out work.
As shown in [Figure 2], the frequency category of light and the medium workloads was 44.21%, and the heavy workload was 11.58%. The highest workloads in the production area at three factories were in the type of light workloads with an energy expenditure of 100–200 kcal/h and medium workloads with an energy expenditure of 200–350 kcal/h. The small number of workers in the production area with the category of heavy workloads will facilitate companies in carrying out efforts to control workloads.
Fatigue measurement results using the IFRC questionnaire method
The most significant frequency distribution of respondents’ answers was “sometimes feel” to questions number 1–10 (56.32%); questions number 11–20 (51.26%); and questions number 21–30 (49.47%). The work fatigue measurement results were obtained based on the calculation of work fatigue scores that can be known from the responses of each respondent. The work fatigue score of each question was summarized, then can be known as the category of work fatigue.
The work fatigue category was divided into light (rating 30–52), medium (rating 53–75), heavy (rating 76–98), and too heavy (rating 99–120). The frequency distribution of respondents’ work fatigue categories can be seen in [Figure 3].
As shown in [Figure 3], the frequency distribution of the category of light fatigue was 26.32%, the medium group was 72.63%, and the heavy group was 1.05%.
| Discussions|| |
Effect of workload on work fatigue
The workloads probability (P value) was 0.003 < 0.05, which means that the workload was significantly related to work fatigue. Workloads caused work fatigue. If the heavier workloads and the number of activities carried out repeatedly in a day by workers in the production area of crumb rubber, then these workers will experience fatigue faster. Workers with heavy workloads will experience increased work fatigue; it was because workers with heavy workloads will spend a lot of energy while working. A heavy workload would be very draining if the work were not balanced with time to rest.
No exception for the association between work fatigue and type of workload. The high workload can lead to an increase in fatigue. The workload following the ability of workers reduced work fatigue and affected the quality of work results. The right workload also affected the quality of worker breaks. If workers can rest well, then productivity increases, and fatigue and work accidents can be minimized. Fatigue levels differ due to several reasons related to work schedules, such as night shifts, periods, irregular shifts, successive working hours, successive working days, and individual workloads.,,,,,
Effect of age on work fatigue
Age P value of 0.00 < 0.05 means that age was significantly related to work fatigue. The age caused work fatigue because, with increasing age, muscle strength, and endurance decreased so that the risk of work fatigue would increase. Crumb rubber factories of Padang City did not differentiate the division of labor based on the age of the worker. All workers work together to achieve production results according to the desired target.
Younger workers need a faster time than older workers when doing work because younger workers had more energy than older workers. If the longer the time required to do the job, then the work fatigue would be increasingly felt. The age factor influenced the occurrence of workers’ fatigue in the production area of crumb rubber, because it was not based on the age of the worker. Workers who were young and old do the same physical activities, both physical activities with light, medium or heavy workloads.,
The function of the body could change due to age. Someone young was able to do heavy work, while someone older the ability to do heavy work would decrease. At different ages, sleep needs and metabolic functions may vary. It is possible that, at younger ages, lipid metabolism may be less susceptible to the effects of an irregular shift schedule or poor sleep quality.
Effect of working period on work fatigue
The value of the probability (P value) of tenure of 0.00 < 0.05, which indicated the working period, was significantly related to work fatigue. Working period caused work fatigue. The longer a person works, there would appear a feeling of boredom due to monotonous and repetitive work so that it affected the level of work fatigue. Workload distribution needs to be done based on a work period. For example, workers with new work periods were placed to work in areas with heavy workloads (such as the bandela unloading process). It was intended that workers with new tenure could be trained and had the motivation to do work processes better. So that productivity increases, whereas workers with long service periods were placed to work in areas with light-workloads (such as the mixing process). This arrangement aimed to reduce work fatigue. Length of service was a factor that affects work fatigue.,,
Multiple regression analysis
Multiple regression analysis was performed using a statistical application. The partial effect or self-influence given the independent variable on the dependent variable can be known by t-test, the results of t-test can be seen in this following equation:
Y = 25.599 + 15.216 X1 + 0.368 X2 + 1.227 X3
The regression equation shows that the workload variable had the most significant influence. That is 15.216 times that of other variables in influencing work fatigue. If the more substantial the workload, the potential for work fatigue will be higher, thus requiring treatment to reduce work fatigue.
Proposed efforts to control fatigue in crumb rubber factories of Padang city
The proposed efforts to control fatigue are as follows:
- Distribution of nutritious food and milk
Indonesian standard No. 7269 of 2009 explains that if more energy were expended while working, the potential for work fatigue would be higher. So companies need to distribute nutritious food and milk to workers to restore power that has been used up while working.
- A break in between work hours
Paying attention to rest periods was important. Every worker must get adequate rest in between work hours. It was better to have a “coffee break” to restore the enthusiasm of workers to be more active when working to reduce work fatigue.,,
- Periodic health checks
Regulation of the Minister of Manpower and Transmigration No. 02 of 1980 explains that companies need to conduct health checks on their workers regularly once every year. This health check aimed to obtain a comparison of the health outcomes of each worker. Periodic health checks were performed to determine whether the workload was within the body’s ability or not.
| Conclusion|| |
The level of work fatigue in the production areas in three crumb rubber factories in Padang was 72.63%. Workloads, age, and work periods were related and had a significant effect on work fatigue. The results of multiple regression analysis showed that the most influential factor on the occurrence of work fatigue in crumb rubber factories of Padang City was the workload. The heavier the workload, the work fatigue would be increased.
This research was conducted under financial support from PNBP funding 2019 of Institute for Research and Community Services, Universitas Andalas, Indonesia.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4]