<![CDATA[The research design used in this study is a descriptive correlational research design, which outlines and provides an explanation of the relationship between the variable X (independent) as the independent variable that influences and the variable Y (dependent) as the dependent variable that is influenced, where the independent variable is the Level Service and dependent variable is Number of Visitors. Considering the total population under 100, the authors take the entire population of 30 (thirty) visitors as a sample. Calculation of the positive correlation coefficient between variables X and Y is with a value of 0.959, meaning that there is a relationship between the level of service (variable X) with the number of visitors (variable Y) and the relationship is classified as a very high correlation. Based on R Squre = 0.920, this shows how far the model's ability to explain the variation of independent variables, means that the influence between the level of service to the number of visitors is 92% which can be categorized as very high while the remaining 8% is explained by other variables or factors outside the research. The results of regression analysis with the equation Y = 5.876 + 0.87 X. can be explained by a constant of 5.876 meaning that if (variable X) = 0, then the number of variables Y = 5.876 and X = 1, then the variable Y = 6.746. From the results of the calculation of the t test above where 17.885> 1.701. Because the value of t is greater than the table value, there is an effect of the level of service on the number of visitors.]]>
