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The effect of temperature on measurement accuracy

The work environment gives some effects on the employee. One of these is the temperature. Inappropriate working temperatures can interfere with employee work performance. This study aims to proof the effect of temperature on the work efficiency by using the student as a respondent. The student must finish the task in two type temperature condition: with Air conditioner (AC) and no air conditioner (Non-AC). The task was to measure twenty-five workpieces (length and diameter) repeatedly using five types of measuring instruments. Different environmental temperatures are thought to affect student concentration during repeated measurements. The effect of temperature on the measurement results was tested using Two Way ANOVA. Other factors that are taken are the measuring instruments. The results obtained are the temperature conditioned using AC and Non-AC affect the measurement results with p-value<0.05. In addition, the temperature and measuring instruments together influence the measurement results with p-value <0.05.


Introduction
Employee performance is an important factor for the progress of a company. One of the things that supports employee performance is the working environment. A conducive working environment is closely related to how an employee can complete a task or job [1].
In the working environment, temperature becomes an important component because it is related to how the body can survive in different temperatures. Improper temperature can interfere employee concentration in completing their works [2]. In industry, one type of task or job that requires concentration is quality control, one of which is workpiece measurement. In measuring workpieces, especially measurements made repeatedly, an employee must maintain the stability of his concentration so the quality control can be completed properly.
In previous studies, the temperature can affect employee performance. According to [3], inappropriate temperatures make that the results of the work are not optimal. Another study was conducted by [4] who examined the effect of temperature and noise on the workload of the production operator's cardiovascular system. The temperature used in the experiment used two levels, and the results obtained that the temperature significantly affected the cardiovascular system and contributed 17.556%. This paper analyse how temperature affects the results of repeated measurements on a workpiece. These measurements are carried out in the laboratory so that after this analysis can be used as suggestions in laboratory settings to optimize the student practicum.

Literature Study
The literatures used in this paper is as follows.

Ergonomic
Ergonomics is a branch of science that studies human traits, abilities, and limitations that are useful for designing working systems so that humans can work comfortably [5]. The purpose of ergonomics as follows. a. reduce injuries and illnesses that occur at work, b. improve social welfare through improving the quality of social contacts, managing and coordinating work appropriately c. creating a rational balance between aspects, namely technical, economic, anthropological, and cultural aspects of each work system carried out to create high quality work. Another definition of ergonomics according to Manuaba (1999) is the ability to apply information based on human character, capacity, and limitations on the work system so as to create a comfortable working environment.

Working Environment
Humans and the environment are interdependent entities. Humans must be able to adapt to the environment, including the work environment. The condition of the working environment affects the physical and psychological conditions of workers and directly impacts the performance results. In the work system, humans are as controllers, planners, as well as interacting with the system. In creating a comfortable working environment, several aspects must be considered, namely differences in physical, psychological conditions, as well as the strengths and weaknesses of people who are in the work system. According to [6], the strengths and weaknesses of human physical condition are indeed the main things, but not only factors that can affect work productivity.
There are several things that must be considered in the work environment, including noise, light intensity, temperature, and others. Employees can work optimally if the working environment is suitable. Conversely, improper environmental conditions will impact on employees within a certain period of time. A further impact is the occurrence of work accidents [7].
One part of the work environment that affects workers is temperature. High temperatures are dangerous in the work environment [8]. Other research states that high temperatures can reduce performance because it affects the human psychological system [2].

Two-Way Analysis of Variance (Two-Way ANOVA)
The experimental design is used to obtain an appropriate sample for the population in the study conducted, then the corresponding conclusions are obtained [9]. The analysis used in experimental design is Analysis of Variance (ANOVA) which examines the influence or contribution of factors to the response. One type of ANOVA is the Two-Way ANOVA which is used if the experiment involves two factors, and a combination of levels between factors [10] is carried out. The mathematical model used in the design of this experiment is as follows.

Data and Methods
In this study, the experimental unit was a student who measured 25 workpieces. The response variable is the result of measuring the length and diameter of the workpiece parts (six parts) as Figure 1.

Figure 1. Measured workpiece
Part A is the length of workpiece, then part B to F are the diameter of workpiece. The independent variable or factors that influence the response variable are temperature and the measuring instrument used by student in measuring workpieces. The temperature factor has 2 levels and the measuring instruments has 5 levels. The level for each factor as follows. The experimental design used was Two Way Analysis of Variance (Two-Way ANOVA) involving two factor and its interaction. The data structure as follows.  The method carried out in this paper is as follows.
• do data retrieval by measuring 25 workpieces repeatedly using five measuring instruments, • workpiece measurements are carried out in two different conditions, the first condition use Air Conditioner (AC) and the second condition does not use, • the parts of workpiece are measured according to Figure 1, • after the data is collected, an analysis is conducted using Two Way ANOVA to see whether differences in the use of measuring instruments and AC affect the measurement results,

Results and Discussion
In this paper, the statistical method used Two-Way ANOVA. The hypotheses are tested based on factors are thought to influence the measurement results are as follows. H01 : Temperature does not significantly influence the measurement results of the workpiece H02 : Measurement instrument does not significantly influence the measurement results of the workpiece H03 : Interaction between temperature and measurement instrument does not significantly influence the measurement results of the workpiece H11 : Temperature does significantly influence the measurement results of the workpiece H12 : Measurement instrument does significantly influence the measurement results of the workpiece H13 : Interaction between temperature and measurement instrument does significantly influence the measurement results of the workpiece The results of data analysis are shown as follows. The decision to reject or accept H0 is based on F value or the significance of p-value. Hypotheseis H0 is rejected if the > , where the determination of dan is based on degrees of freedom of factor 1 and factor 2. If using the p-value significance, H0 will be rejected when the − < , in this case use 5%. Based on Table 3, all parts of the workpiece measured show temperature, measuring devices, and their interactions significantly affect the measurement results, except for measurements of diameters D and F, because in both parts p-value> α. If you look further, the SS error value in D and F is very large compared to other parts, thus affecting the significance test of these two factors. In addition to factor 1 and factor 2, there are also significant interactions affecting differences in measurement results, then it means that the combination of temperature and measurement instruments used affects simultaneously.
After the ANOVA test, the significant factors in each part of the workpiece are known. Furthermore, the factors calculated contribution value (% contribution) to the measurement results in all parts of the workpiece, except parts D and F. The results of the percentage contribution for each factor are as follows. Based on Table 4, it can be seen each factor and the interaction between factors contribute differently to each part is measured. And the result is the difference factor of the measuring instrument used gives the highest contribution to the difference in measurement results. Temperature contributes an average of 11.83% to differences in measurement results, measuring instruments contribute an average of 33.77% and interactions between temperature and measuring instruments contribute 22.90%. The smaller percentage of temperature contribution from the measuring instrument is caused by the number of treatments used in the experiment. The temperature used only distinguishes measurements in the AC and Non-AC rooms without the room temperature being measured, so the measurement results obtained do not provide a large difference. The second factor, namely measuring instruments contribute above 30% to the measurement results because the types of measuring instruments used are different so that the accuracy provided is also different.
Temperature and type of measuring instrument used have a significant effect on the measurement results, which on a larger scale affect to quality control. This quality control determines the products received or rejected so that high accuracy is needed because it is related to production results. Based on the analysis of the measurement results, the largest R ^ 2 values are part A almost 100%, part B and E are below 75%, and part C is below 50%. If the value of R ^ 2 is small, it means that there are other factors outside of the observation that have a large influence on the measurement results. For example in part C, the value of R ^ 2 is only 36.21%, then it means temperature, measuring instruments, and their interactions only contribute 36.21% to the measurement results, while 63.79% is influenced by factors other than these two factors, for example the physical conditions who take measurements.

Conclusion
The temperature affects the measurement results, but the contribution percentage is smaller than the type of measuring instrument used. It is caused by temperature settings that only use and do not use AC (not specifically regulated at certain temperatures). In the next research can be developed by specifically setting the temperature (using more than two levels) so that the effect given to the measurement results is clearer and reduces errors.