THE EFFECT OF JOB DESCRIPTION ON EMPLOYEE PERFORMANCE WITH COMPENSATION AS AN INTERVENING VARIABLE (Case Study on Nurse and Medical Doctor of Chicago District)

This study aims to determine how the influence of job descriptions on employee performance with compensation as an intervening variable for Nurse and Medical Doctor of Chicago District. The research method used is the method of qualitative data and quantitative data. While the data used is primary data. The method of data analysis in this study uses simple linear regression analysis to obtain a comprehensive picture of the effect of job description variables on employee performance with compensation as an intervening variable for Nurse and Medical Doctor of Chicago District using the SPSS 25 for Windows program. To find out whether there is a significant effect of the independent variable on the dependent variable, a simple linear regression model is used. The results of testing the hypothesis using simple regression analysis and t test show that: that the t-table value of the job description variable is 4.181 > the t-table value (df: 38, α: 5%) is 1.685 so it can be concluded that the job description has a positive effect and significant to employee performance. Furthermore, from table 4.17 it can also be seen that the t-table value of the compensation variable is 1.186 < the t-table value (df: 38, α : 5%) is 2.024 so it can be concluded that compensation has no significant effect on employee performance.


INTRODUCTION
The role of HR Management itself is very influential on the performance of employees, because human resources are the most important resource and very decisive in the survival of a company/organization. Basically everyone has extraordinary potential and has not been fully utilized. In this affirmation, it is the manager's duty to utilize these resources in such a way for the benefit of achieving organizational goals, while still providing an appreciation and respect for the HR concerned. It is said that the company's goals can be achieved not only depending on modern equipment, adequate facilities and infrastructure, but more depending on the human resources who carry out the work. The achievement of an organization is strongly influenced by the individual performance of its employees. Performance is the result of work that can be achieved by a person or group of people in an organization, in accordance with their respective authorities and responsibilities in order to achieve the goals of the organization concerned legally, not violating the law, and in accordance with morals and ethics (Hidayah, 2016). One way to spur employee performance in an organization or company is to further improve employee performance optimally, such as providing compensation, holding job training for new employees, getting special attention for employees with achievements such as giving awards, and other forms of attention to all. his employees. The existence of activities will greatly affect the provision of compensation. The following is data or facts on the performance of PT.Karya Hevea Indonesia's harvesting employees with data on production results as of 2019 in the last few months. Job descriptions in an organization, both private and government, are a collection of information about jobs or an outline of what obligations, responsibilities and authorities are held and must be carried out by officials. In addition, job descriptions also explain the procedures for carrying out these tasks in order to achieve organizational goals effectively and efficiently. Job description is a written statement that contains what must be done by workers, how to do it, and under what conditions the work is done (Dessler, 2013). Job description states the duties and responsibilities of a job. It states "what is done, why is it done, and where is it done, and briefly how to do it". The problem of performance is certainly inseparable from the process, results and usability, in this case work performance or achievement is the result of work in quality and quantity achieved by an apparatus in carrying out their duties in accordance with the responsibilities given to them. Job descriptions include those described in this study only for employees who collect or harvest oil palm fruit. They do not discuss other jobs. According to Hasibuan (2017: 119) Compensation is all income in the form of money, direct or indirect goods received by employees as a reward for services provided to the company. Establishing an effective compensation system is an important part of human resource management because it helps attract and retain talented jobs. In addition, the company's compensation system has an impact on strategic performance. Compensation is also the provision of compensation financially or non-financially, directly or indirectly, which is equally distributed to all employees for the achievement of an organization.
This research found several things that are phenomena and facts that are currently happening in the company where it was found that there was a lack of motivation given by the company to all employees in terms of adding forms of compensation needed by employees so that they are better at completing their work.Based on the phenomenon that occurred to Nurse and Medical Doctor of Chicago Districtis still very much found fault with employees who do not comply with the standards set by the company. The fact is that employee job descriptions should be taken into consideration by the company in providing jobs that are clear and in accordance with expertise in order to further improve employee performance within the company

Data Types and Sources 1. Data Type
According to Sugiyono (2015), the types of data are divided into 2, namely qualitative and quantitative. This study uses data types in the form of qualitative and quantitative.  (2015) is data in the form of words, schemes, and pictures. The qualitative data of this research are the names and addresses of the research objects b. Quantitative Data Quantitative data according to Sugiyono (2015) is data in the form of numbers or qualitative data that is numbered.

Data Source
According to Sugiyono (2012: 193) the types of data are divided into two, namely: a. Primary data is a data source that directly provides data to data collectors. In this study, the primary data was in the form of data from questionnaires and interviews conducted by researchers. b. Secondary data is a source that does not directly provide data to data collectors, for example through other people or through documents.

Data collection technique
The data collection technique used is by:

Questionnaire
Questionnaires or questionnaires are a number of questions or written statements about factual data or opinions relating to the respondent, which are considered facts or truths that are known and need to be answered by the respondent. In this questionnaire, a closed question model will be used, namely questions that have been accompanied by alternative answers before so that respondents can choose one of the alternative answers. The processing of data in this study uses a Likert Scale.According to Sugiyono (2013: 132) "Likert scale is used to measure attitudes, opinions and perceptions of a person or group of people about social phenomena". In answering this Likert scale, the respondent only gives a mark, for example a checklist or a cross on the answer chosen according to the statement. The questionnaire that has been filled in by the respondent needs to be scored. The following is the weight of the rating on the Likert scale. According to Sugiyono (2015: 231) interviews are a data collection technique if the researcher wants to conduct a preliminary study to find problems that must be studied, but also if the researcher wants to know things from respondents that are more indepth.

Library Studies
Literature study, according to Nazir (2013) data collection technique by conducting a review study of books, literature, notes, and reports that have to do with the problem being solved.

Validity Test
Validity testing uses SPSS version 25.00 with criteria based on the calculated r value as follows: a) If r count > r table orr count < -r table then the statement is declared valid. b) If r count < r table orr count > -r table then the statement is declared invalid.
This test was carried out on 40 respondents, then df = 40-k = 38, with α = 5%, an r table value of 0.312 was obtained (Ghozali, 2016), then the calculated r value will be compared with the r table value as shown in table 4.5 below :  Table 4.5 shows that all statement points, both the job description (X), employee performance (Y2) and compensation (Y1) variables, have a higher r count than the r table value, so that it can be concluded that all statements for each variable are declared valid.

Reliability Test
Reliability is an index that shows the extent to which a measuring device can be trusted or relied on. According to Sugiyono (2013) A factor is declared reliable if the Cronbach Alpha is greater than 0.6. Based on the results of data processing using SPSS 25.00, the following results are obtained:  (2020) Based on the reliability test using Cronbach Alpha, all research variables are reliable/reliable because Cronbach Alpha is greater than 0.6, the results of this study indicate that the measurement tools in this study have fulfilled the reliability test (reliable and can be used as a measuring tool).

Test the Classical Assumptions of Equation 1
As for testing the classical assumptions with the SPSS program25.00 which was carried out in this study included: a. Normality test The Normality Test aims to test whether in the regression model, the confounding or residual variables have a normal distribution (Ghozali, 2016). Data normality testing can be done using two methods, graphics and statistics. The normality test for the graphical method uses the normal probability plot, while the normality test for the statistical method uses the one sample Kolmogorov Smirnov test. The normality test using the graphical method can be seen in the following figure: 45

Figure 4.1 Normal P Plot
Data that is normally distributed will form a straight diagonal line and plotting the residual data will be compared with the diagonal line, if the distribution of the residual data is normal then the line that describes the actual data will follow the diagonal line (Ghozali, 2016). The test results using SPSS 25.00 are as follows:

b. Heteroscedasticity Test
The heteroscedasticity test aims to test whether from the regression model there is an inequality of variance from the residuals of one observation to another. A good regression model is one that has homoscedasticity or does not have heteroscedasticity. One way to detect the presence or absence of heteroscedasticity is with the Glejser test, in the glejser test, if the independent variable is statistically significant in influencing the dependent variable then there is an indication of heteroscedasticity occurring. Conversely, if the independent variable is not statistically significant in influencing the dependent variable, then there is no indication of heteroscedasticity. This is observed from the significance probability above the 5% confidence level (Ghozali, 2016). The results of data processing using SPSS 17.00 show the results in the following table:

Simple Linear Regression Testing
Multiple linear regression testing explains the role of variablesjob descriptions(X) to the compensation variable (Y1). Data analysis in this study used multiple linear regression analysis using SPSS 25.0 for windows. The analysis of each variable is explained in the following description: Source: Data processed from attachment 4 (2019) Based on these results, the multiple linear regression equation has the formulation:Y1 = a + b X + ɛ, so the equation is obtained: Y1 = 7.017 + 0.590 X + ɛ indicating the large role of the job description variable (X) on the compensation variable (Y1). This means that if the job description variable factor (X) increases by 1 unit value, it is predicted that the compensation variable (Y1) will increase by (0.590) units.

Coefficient of Determination (R2)
The coefficient of determination is used to see how much the independent variable contributes to the dependent variable. The greater the value of the coefficient of determination, the better the ability of the independent variable to explain the dependent variable. If the determination (R 2 ) the greater (closer to 1), it can be said that the influence of the variablejob descriptions(X)is big againstcompensation variable (Y1).
The value used in viewing the coefficient of determination in this study is in the adjusted R square column. This is because the value of the adjusted R square is not susceptible to the addition of independent variables. The value of the coefficient of determination can be seen in Table 4.10 below: Source: Data processed from attachment 4 (2020) Based on table 4.10, it can be seen that the value of the adjusted R square is 0.626 or 62.6%. This shows ifvariablejob descriptions(X) can explain the compensation variable (Y1) of 62.6%, the remaining 37.4% (100% -62.6%) is explained by other variables outside this research model.

Test the Classical Assumptions of Equation 2
As for testing the classical assumptions with the SPSS program25.00 which was carried out in this study included: a. Normality test The Normality Test aims to test whether in the regression model, the confounding or residual variables have a normal distribution (Ghozali, 2016). Data normality testing can be done using two methods, graphics and statistics. The normality test for the graphical method uses the normal probability plot, while the normality test for the

Figure 4.2 Normal P Plot
Data that is normally distributed will form a straight diagonal line and plotting the residual data will be compared with the diagonal line, if the distribution of the residual data is normal then the line that describes the actual data will follow the diagonal line (Ghozali, 2016). The test results using SPSS 25.00 are as follows: Source: Data processed from attachment 4 (2020) From the output in table 4.11 it can be seen that the significance value (Monte Carlo Sig.) of all variables is 0.675. If the significance is more than 0.05, then the residual value is normal, so it can be concluded that all variables are normally distributed.

b. Multicollinearity Test
The multicollinearity test aims to determine whether there is a correlation between the independent variables in the regression model. The multicollinearity test in this study was seen from the tolerance value or variance inflation factor (VIF). The calculation of the tolerance value or VIF with the SPSS 25.00 program for windows can be seen in Table 4.12 below: Source: Data processed from attachment 4 (2020) Based on table 4.12 it can be seen that: The tolerance value of the job description (X) is 0.365, compensation (Y1) is 0.365 where everything is greater than 0.10 while the VIF value of the job description (X) is 2.742 and compensation (Y1) is 2.742 where everything is less than 10. Based on the results of the calculation above, it can be seen that the tolerance value of all independent variables is greater than 0.10 and the VIF value of all independent variables is also less than 5 so that there are no correlation symptoms in the independent variables. So it can be concluded that there are no symptoms of multicollinearity between independent variables in the regression model.

c. Heteroscedasticity Test
The heteroscedasticity test aims to test whether from the regression model there is an inequality of variance from the residuals of one observation to another. A good regression model is one that has homoscedasticity or does not have heteroscedasticity. One way to detect the presence or absence of heteroscedasticity is with the Glejser test, in the glejser test, if the independent variable is statistically significant in influencing the dependent variable then there is an indication of heteroscedasticity occurring. Conversely, if the independent variable is not statistically significant in influencing the dependent variable, then there is no indication of heteroscedasticity. This is observed from the significance probability above the 5% confidence level (Ghozali, 2016). The results of data processing using SPSS 17.00 show the results in the following table:

Multiple Linear Regression Testing
Multiple linear regression testing explains the role of job description (X) and compensation (Y1) on employee performance (Y2). Data analysis in this study used multiple linear regression analysis using SPSS 25.0 for windows. The analysis of each variable is explained in the following description: Source: Data processed from attachment 4 (2019) Based on these results, the multiple linear regression equation has the formulation:Y2 = a + b1X + + b3Y1 + ɛ, so the equation is obtained: Y2 = 3.407 + 0.586X + 0.225Y1 + ɛ The description of the multiple linear regression equation above is as follows: a. The constant value (a) of 3.407 indicates the level of employee performance (Y2) if the job description (X) and compensation (Y1) are equal to zero. b. The regression coefficient value of job description (X) (b1) is 0.586 indicating the magnitude of the role of job description (X) on employee performance (Y2) assuming the compensation variable (Y1) is constant. This means that if the job description factor (X) increases by 1 value unit, it is predicted that employee performance (Y2) will increase by 0.586 value units assuming constant compensation (Y1). c. The value of the compensation regression coefficient (Y1) (b3) of 0.225 indicates the magnitude of the role of compensation (Y1) on employee performance (Y2) assuming the job description variable (X) is constant. This means that if the compensation factor (Y1) increases by 1 value unit, it is predicted that employee performance (Y2) will increase by 0.225 value units assuming the job description (X) is constant.

Coefficient of Determination (R2)
The coefficient of determination is used to see how much the independent variable contributes to the dependent variable. The greater the value of the coefficient of determination, the better the ability of the independent variable to explain the dependent variable. If the determination (R 2 ) the greater (closer to 1), it can be said that the effect of variable X is large oncompensation(Y1).
The value used in viewing the coefficient of determination in this study is in the adjusted R square column. This is because the value of the adjusted R square is not susceptible to the addition of independent variables. The value of the coefficient of determination can be seen in Table 4.15 below:

b. Path Analysis
In order to prove that whether a variable is capable of being a variable that mediates the relationship between the independent variable and the dependent variable, a direct and indirect effect calculation will be carried out between the independent variable and the dependent variable. If the indirect effect of the independent variable on the dependent variable through the intervening variable is greater than the direct effect of the independent variable on the dependent variable, then this variable can be a variable that mediates between the independent variable and the dependent variable (Ghozali, 2016). To carry out direct and indirect calculations, it is carried out from the standardized values of the regression coefficients equations I and II as follows:  Furthermore, the value of standardized coefficients beta will be entered into the path analysis image as follows: 10.

Figure 4.3 Path Analysis
In Figure 4.3 the path analysis shows the direct effect of variable X on variable Y2 of 0.659. While the indirect effect through the Y1 variable is 0.797 x 0.187 = 0.149, the calculation results obtained show that the indirect effect through the Y1 variable is greater than the direct effect on the Y2 variable. These results can be seen in table 4.20 below:

Conclusion
Based on the results of the research and discussion in the previous chapter, it can be concluded as follows: 1. What was submitted stated that:From table 4.16, a tcount value of 8.135 is obtained. With α = 5%, ttable (5%; nk = 38) a ttable value of 2.024 is obtained. From this description it can be seen that tcount (8.135) > ttable (2.024), so does the significance value 0.00 <0.05, it can be concluded that the first hypothesis is accepted, meaning the job description variable(X) positive and significant effectto compensation (Y1).