<![CDATA[In this study, researchers used respondents' data, such as gender, age and duration of work of respondents to be able to provide information about the characteristics of respondents. Where from the questionnaire distributed questionnaire as many as 80 respondents using census techniques. Data analysis with parametric and non-parametric statistics using SEM-PLS ( Structural Equation Modeling-Partial Least Square) concerning research variables, instrument test, normality test, hypothesis test, and discussion of the results of hypothesis testing and Path Path Analysis. This study uses path analysis (path analysis) to test the pattern of relationships that reveal the influence of variables or a set of variables on other variables, both direct influence and indirect influence. The results of the study are as follows: The effect of the X3 variable on X4 has a P-Values value of 0.008 <0.05, so it can be stated that the influence between X3 and X4 is significant. The effect of the X3 variable on Y has a P-Values value of 0.002> 0.05, so it can be stated that the influence between X3 and Y is significant. The effect of the X4 variable on Y has a P-Values value of 0.005> 0.05, so it can be stated that the effect between X4 on Y is significant. The influence of X1 on X4 has a P-Values value of 0.009 <0.05, so it can be stated that the influence between X1 to X4 is significant. The influence of X1 on Y has a P-Values value of 0.021> 0.05, so it can be stated that the influence between X1 and Y is significant. The effect of the X2 variable on X4 has a P-Values value of 0,000 <0,05, so it can be stated that the effect between X2 on X4 is significant. The effect of the X2 variable on Y has a P-Values value of 0.008 <0.05, so it can be stated that the effect between X2 on Y is significant.]]>
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