THE INFLUENCE OF REWARD SYSTEM ON EMPLOYEE JOB SATISFACTION WITH WORK MOTIVATION AS INTERVENING VARIABLES (Case Study on Employees of Medical Department United States)

This study aims to determine“The Influence of the Reward System on Employee Job Satisfaction with Work Motivation as an Intervening Variable (Case Study of Employees of Medical Department United States)”. The results of this study indicate that 1)it can be seen that tcount (10.195) > ttable (1.692), likewise with a significance value of 0.00 <0.05, it can be concluded that the first hypothesis is accepted, meaning that the reward system variable(X) positive and significant effecton work motivation (Y1). 2) it can be seen that tcount (5.737) > ttable (1.692), and the significance value is 0.00 <0.05, it can be concluded that the second hypothesis is accepted, meaning that the reward system(X) significant effecton job satisfaction (Y2). 3) it can be seen that tcount (0.179) < ttable (1.692), and a significance value of 0.00 > 0.05, it can be concluded that the third hypothesis is rejected, meaning that work motivation (Y1) is notsignificant effecton job satisfaction (Y2). 4) the direct effect of variable X on variable Y2 is 0.722. While the indirect effect through the Y1 variable is 0.273 x 0.023 = 0.0627, the results of the calculations show that the indirect effect through the Y1 variable is smaller than the direct effect on the Y2 variable.


INTRODUCTION
Human resources are a fairly important part in achieving organizational goals, both large and small companies, a company has modern equipment with high technology. Human resources are one of the main driving forces for each company's operations, so efforts to develop human resources are the main strategy to uphold global competition.
According to Robbins and Judge (2011: 107) Job satisfaction is a positive feeling about one's work which is the result of an evaluation of its characteristics. Job satisfaction is basically something that is individual. Each individual has a different level of satisfaction according to the value system that applies to him. The higher the assessment of the activities felt in accordance with individual wishes, the higher the satisfaction with these activities. Thus, satisfaction is an evaluation that describes a person's feelings of pleasure or displeasure, satisfaction or dissatisfaction at work.
In this study, not all factors that influence employee performance will be examined one by one, but only factors related to the company's managerial system and leadership factors. The researcher proposes a reward system and promotion that is considered capable of representing the factors that influence employee job satisfaction.
According to Nawawi (2009, p.319), "reward is an effort to foster a feeling of being accepted (recognized) in the work environment, which touches on aspects of compensation and aspects of the relationship between workers with one another". Managers evaluate individual performance results both formally and informally. Because giving rewards and motivation is a driving force within a person that will direct the behavior and work performance of that person, which will play an important role in the success of the company, both output and input from the company, both in terms of quality and quantity. In connection with the above, the company needs to pay special attention to the achievements obtained by employees by giving rewards (gifts, rewards, and awards) and motivation to work enthusiastically, have high responsibility towards their duties, so that a company will be easy to fulfill the planned goals. The work of a person in an organization or company is not only in the form of wages or salaries, but also rewards or rewards intended to meet various needs with various types and forms. For this purpose management is expected to be able to apply an efficient reward. A reward designed by a company must be able to motivate the performance of its employees so that achievement is at a high level. but also rewards or rewards intended to meet various needs with various types and forms. For this purpose management is expected to be able to apply an efficient reward. A reward designed by a company must be able to motivate the performance of its employees so that achievement is at a high level. but also rewards or rewards intended to meet various needs with various types and forms. For this purpose management is expected to be able to apply an efficient reward. A reward designed by a company must be able to motivate the performance of its employees so that achievement is at a high level.
Rewardsor prizes for achieving good work results have been implemented in the Goods and Services Procurement Section of Serdang Bedagai Regency in order to boost employee performance so that they are willing to compete in terms of providing maximum performance for services and work results with enthusiasm. In addition, promotions will also be carried out by the company for employees who are considered worthy of carrying out work duties and responsibilities that are heavier than before by proving themselves to be able to make achievements and achieve maximum work targets.
Based on the explanation above, the reasons in the table below are given regarding jobs that reap rewards from offices or agencies: 1. Checking Provisional Estimated Prices (HPS) according to specifications and market prices in accordance with applicable procedures and conditions so that they comply with the guidelines and working basis Management 1. Examine the specifications for the procurement of goods/construction services/consulting services/other services to be held in accordance with applicable procedures and conditions so that the implementation of tasks runs smoothly. Source: Procurement of goods and services The table above explains how satisfaction is felt by employees by looking at how the performance is carried out by employees. It can be seen that there is a decrease in the level of satisfaction caused by rewards and motivation that do not go well in institutions. Motivation is the impetus for a series of processes of human behavior in achieving goals. While the elements contained in motivation include elements of arousing, directing, maintaining, showing intensity, being continuous and having a purpose. (Wibowo, 2010), with high work motivation from employees, performance will increase which will have a positive impact on providing maximum service Medical Department United States.
However, there are also facts and phenomena that currently exist in these government agencies where a reward system that has not been fully operational is still the basis for employees experiencing dissatisfaction at work where employees need motivational encouragement such as this reward so that achievement and performance are maximized in the services provided.

Rewards System
According to Nawawi (2011, p.319), "reward is an effort to foster a feeling of being accepted (recognized) in the work environment, which touches on aspects of compensation and aspects of the relationship between workers with one another. Efforts to get professional employees in accordance with the demands of the position require a continuous development, namely an effort to plan, organize, use and maintain employee activities so that they are able to carry out their duties effectively and efficiently. As a concrete step in the results of coaching, it is deemed necessary to provide rewards or awards to employees who have shown good work performance. Giving rewards is a leader's effort to provide remuneration for the work of employees, so that it can encourage more active and potential work.
Leaders provide rewards when the work of an employee meets or even exceeds the standards set by the organization. There are also organizations that provide rewards to

Work motivation
Motivation is an encouragement that arises from within a person to carry out a job. Motivation according to (Hariandja, 2002) in (Herdianto, 2010) is defined as factors that direct and encourage behavior or a person's desire to carry out an activity expressed in the form of hard or weak effort. Understanding of motivation is very important in achieving goals, namely productivity and efficiency.
Motivation is a psychological characteristic of human activity to contribute in the form of a person's level of commitment including the factors that cause, channel and maintain human behavior in the direction of a certain determination to achieve a desire. Activities carried out are activities that aim to fulfill individual desires. According to Siagian (2011), defines work motivation as a driving force for a person to make the maximum possible contribution to the success of the organization in achieving its goals, with the understanding that achieving organizational goals means achieving the personal goals of the members of the organization concerned. Temporary Robbins (2010) says work motivation is a willingness to expend a high level of effort towards organizational goals, which is conditioned by the ability of these efforts to fulfill an individual need.

Job satisfaction
High job satisfaction is a sign of a well-managed organization and is basically the result of effective behavior management. Job satisfaction is a measure of the sustainable human climate development process and an organization. Job satisfaction is a set of feelings about whether or not their job is enjoyable. There is an important difference between these feelings and the other two elements of employee attitudes. Job satisfaction is part of life satisfaction. The nature of one's environment outside of work influences feelings at work. Likewise, because work is an important part of life, job satisfaction affects one's life satisfaction.
Job satisfaction is an affective or emotional response from a job (Kreitner & Kinicki, 2010). One can feel satisfaction in one aspect and in another. Robbins and Judge (2007) state that job satisfaction is a positive feeling about a job which is the result of evaluating several characteristics. From the above understanding, the positive and negative feelings experienced by employees cause a person to experience job satisfaction or dissatisfaction which is a complex problem, because it comes from various elements of work, for example towards their own work, salary/wages, promotion, supervision, co-workers, or as a whole.
From the various studies that have been carried out, when employees are asked about the response to the work they have done, the results vary for various work elements. From the research results, in general, employees feel overall satisfaction. In work, there are many

Data Types and Sources a. 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. 1) Qualitative Data Qualitative data according to Sugiyono (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 2) Quantitative Data Quantitative data according to Sugiyono (2015) is data in the form of numbers or qualitative data that is numbered.

b. Data source
According to Sugiyono (2012: 193) the types of data are divided into two, namely: 1) 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 the researcher. 2) 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: 1. 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". which has been filled in by the respondent needs to be scored. The following is the weight of the rating on the Likert scale.

Interview
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 in-depth.

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 using the SPSS version25.00 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 no valid. This test was carried out on 35 respondents, then df = 35-k = 33, with α = 5%, an r table value of 0.333 was obtained (Ghozali, 2016), then the calculated r value would be compared with the r table value as shown in table 4.5 below :  3 (2019) Table 4.5. shows that all statement points, both the reward system variable (X), job satisfaction (Y2) and work motivation (Y1) have a higher r value than the r table value, so that it can be concluded that all statements of 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:  (2019) 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
The testing of the classical assumptions with the SPSS 25.00 program carried out in this study includes: 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:

Figure 4.1 Normal P Plot
Data that is normally distributed will form a straight diagonal line and residual data plotting will be compared with the diagonal line, if the residual data distribution 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 (2019) From the output in table 4.8 it can be seen that the significance value (Monte Carlo Sig.) of all variables is 0.857. 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. 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;138). The results of data processing using SPSS 17.00 show the results in the following table:

Simple Linear Regression Testing
Linear regression testingsimply explains the magnitude of the role of the Reward System (X) on work motivation (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 + b1X + ɛ, so the equation is obtained: Y1 = 10.195 + 0.119 X + ɛ The description of the multiple linear regression equation above is as follows: a. The constant value (a) of 10.195 indicates the magnitude of work motivation (Y1) if the reward system (X) is equal to zero. b. The reward system regression coefficient (X) (b1) is 0.119 indicating the large role of the reward system (X) on work motivation (Y1). This means that if the reward system factor (X) increases by 1 value unit, it is predicted that work motivation (Y1) will increase by 0.119 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 variable X is large on incentives (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:

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 statistical method uses the one sample Kolmogorov Smirnov test. The normality test using the graphical method can be seen in the following figure:

Figure 4.2 Normal P Plot
Data that is normally distributed will form a straight diagonal line and residual data plotting will be compared with the diagonal line, if the residual data distribution is normal then the line that describes the actual data will follow the diagonal line (Ghozali, 2016  Source: Data processed from attachment 4 (2019) From the output in table 4.11 it can be seen that the significance value (Monte Carlo Sig.) of all variables is 0.229. 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 (2019) Based on table 4.12 it can be seen that the tolerance value of the reward system (X) is 0.926, work motivation (Y1) is 0.926 where everything is greater than 0.10 while the VIF value of the reward system (X) is 1.080, work motivation (Y1) of 1.080, all of which are less than 10. Based on the calculation results 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 there is no correlation symptom 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;138). 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 the reward system (X) and work motivation (Z) on job satisfaction (Y). 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 + b2Y1 + ɛ, so the equation is obtained: Y2 = 1.927 + 0.840X + 0.060Y1 + ɛ The description of the multiple linear regression equation above is as follows: a. The constant value (a) of 1.927 indicates the magnitude of job satisfaction (Y2) if the reward system (X) and work motivation (Y1) are equal to zero. b. The regression coefficient value of the reward system (X) (b1) is 0.840 indicating the magnitude of the role of the reward system (X) on job satisfaction (Y2) assuming the variable work motivation (Y1) is constant. This means that if the reward system factor (X) increases by 1 value unit, it is predicted that job satisfaction (Y2) will increase by 0.840 value units assuming constant work motivation (Y1). c. The regression coefficient value of work motivation (Y1) (b2) is 0.060 indicating the large role of work motivation (Y1) on job satisfaction (Y2) assuming the reward system variable (X) is constant. This means that if the work motivation factor (Y1) increases by 1 value unit, it is predicted that job satisfaction (Y2) will increase by 0.060 value units assuming the reward system (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 onwork motivation(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  Source: Data processed from attachment 4 (2019) Hypothesis test the effect of the reward system variable (X) on work motivation variable (Y1). The form of hypothesis testing based on statistics can be described as follows: Decision Making Criteria: a) Accept H0 If tcount < ttable or -tcount> -ttable orSig value. >0.05 b) Reject H0 If tcount ≥ ttable or -tcount ≤ -ttable orSig. < 0.05 From table 4.16, a tcount value of 10.195 is obtained. With α = 5%, ttable (5%; nk = 33) a ttable value of 1.692 is obtained. 0.00 <0.05, it can be concluded that the first hypothesis is accepted, meaning the reward system variable(X) positive and significant effecton work motivation (Y1).  , and its significance value is 0. 00 < 0.05, it can be concluded that the second hypothesis is accepted, meaningrewards system(X) significant effecton job satisfaction (Y2). b. Hypothesis test of the effect of work motivation (Y1) on job satisfaction (Y2) The form of hypothesis testing based on statistics can be described as follows: Decision Making Criteria: a) Accept H0 If tcount < ttable or -tcount> -ttable orSig value. >0.05 b) Reject H0 If tcount ≥ ttable or -tcount ≤ -ttable orSig. < 0.05 From table 4.17, a tcount value of 0.179 is obtained. With α = 5%, ttable (5%; nk = 33) a ttable value of 1.692 is obtained. From this description it can be seen that tcount (0.179) < ttable (1.692), and its significance value is 0. 00 > 0.05, it can be concluded that the third hypothesis is rejected, meaningwork motivation (Y1) nosignificant effecton job satisfaction (Y2).

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:

Figure 4. 4 Path Analysis
The path analysis image shows the direct effect of variable X on variable Y2 of 0.722. While the indirect effect through the Y1 variable is 0.273 x 0.023 = 0.0627, the results of the calculations show that the indirect effect through the Y1 variable is smaller than the direct effect on the Y2 variable. These results can be seen in