THE EFFECT OF ONLINE ADVERTISING ON CONSUMER BUYING INTEREST IN ONLINE SELLING APPLICATIONS WITH CUSTOMER SATISFACTION AS AN INTERVENING VARIABLE (Study From Member of United Kingdom Medical Doctor Department)

The results of this study show. (1) It can be seen that the value of the adjusted R square is 0.758 or 75.8%. This shows that consumer satisfaction (Y1) and advertising (X) can explain purchase intention (Y2) of 75.8%, the remaining 24.2% (100% - 75.8%) is explained by other variables outside the research model. This. Such as service, price and interest in buying. (2) The results of the (Partial) t test show that tcount (7.413) > ttable (1.685), likewise with a significance value of 0.00 <0.05, it can be concluded that the first hypothesis is accepted, meaning that the advertising variable(X) positive and significant effecton consumer satisfaction (Y1).(3) The results of the t test (Partial) can be seen that the value of tcount (3.059) > ttable (1.685), and the significance value is 0.00 <0.05, it can be concluded that the second hypothesis is accepted, meaningadvertisement(X) positive and significant effecton buying interest (Y2). (4) The results of the path analysis test show that the direct effect of variable X on variable Y2 is 0.377. While the indirect effect through the Y1 variable is 0.769 x 0.554 = 0.426, the calculation results obtained show that the indirect effect through the Y1 variable is greater than the direct effect on the Y2 variable.


INTRODUCTION
Consumer behavior in deciding to buy a product is a special study for every company before releasing its product to the market. The development of the digital age is increasingly inevitable that every company must adjust its marketing strategy by incorporating an online system to sell its products. Online shopping has become a habit for some people because of the convenience it provides, many people think that online shopping is a means to find the items they need. The research method used is to compare the results of research and journals that examine online shopping in Indonesia. Then review and review existing consumer behavior theories so that it can be concluded that consumer considerations shop online at an online storeShopping decisions usually require considerations that really support and can benefit buyers such as location and price factors. Buyers tend to choose to shop at supermarkets that have strategic locations. Schnaars (Harbani Pasolong, 2010: 221) states that: The creation of customer or community satisfaction can provide benefits, including: the relationship between customers and agencies becomes harmonious, provides a good basis for repeat buyers (use), creates customer loyalty and forms recommendations word of mouth, all of which benefit the company. When reviewed further, the achievement of community satisfaction through service quality can be improved by several approaches. from other brands. One way to build differentiation is to create personality for the brand Therefore it is very important for a brand to have a competitive advantage that can differentiate a brand from other brands. One way to build differentiation is to create personality for the brand Therefore it is very important for a brand to have a competitive advantage that can differentiate a brand from other brands. One way to build differentiation is to create personality for the brand Online advertising is an online marketing effort by displaying a website in search engine search results in a paid way. Online advertising can also be described as the activity of placing advertisements to offer products or services via cyberspace, the purpose of which is to make a profit from sales activities. The advantage of online advertising is that it can target consumers based on consumer interests or also according to the targets the company wants to achieve. In fact, payment for online advertising is only paid for those that are successful or in other words the company can pay according to the total clicks from those that enter the website. Because every consumer has the right to comfort, security, correct and honest information and correct treatment or service for what is purchased, This fact can be seen, that there are several things that can increase consumer buying interest, namely the total customer value consisting of product value, service value, personal value, image or image value, and total customer cost consisting of monetary costs, time costs, effort, and cost of thought. At this time the use of e-commerce in the student environment is said to be quite rapid in its use and development.

LITERATURE REVIEWS
According to Keegan and Green in Rahman, (2012: 21) advertising is messages that contain elements of art, text/writing, titles, photographs, tagelines, other elements that have been developed for their suitability. A good advertisement must be able to convey the contents of the message clearly. Focused on the segment, attractive and in accordance with advertising ethics. A good advertising strategy will contribute to the value of competition in the world of marketing which has led to wars between brands. Durianto (2013: 58), reveals that "Buying interest is the desire to own a product, buying interest will arise if a consumer has been affected by the quality and quality of a product, information about the product, ex: price, how to buy and the weaknesses and advantages of the product compared to other brands. Buying intention is the selection of two or more alternative choices, which means that a person can make a decision, there must be a variety of alternative choices. The decision to buy can affect how the decision-making process is carried out.
Schnaars (Harbani Pasolong, 2010: 221) states that: The creation of customer satisfaction can provide benefits, including: the relationship between customers and agencies becomes harmonious, provides a good basis for repeat buyers (use), creates customer loyalty and forms word of mouth recommendations word of mouth, all of which benefit the company. Based on this understanding of customer satisfaction, it can be concluded that customer satisfaction is the level of one's feelings after consuming a product or service towards the needs, wants, and expectations he wants.

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. a. 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 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
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) "the 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 30 respondents, then df = 30-k = 28, with α = 5%, an r table value of 0.361 was obtained (Ghozali, 2016), then the calculated r value would be compared with the r table value as shown in table 4.5 below :  Table 4.5 shows that all statement points, both the advertising variable (X), purchase intention (Y) and consumer satisfaction (Z) have a higher r value 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
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 infollowing picture:

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: Source: Data processed from attachment 4 (2020) From the output in table 4.7 it can be seen that the significance value (Monte Carlo Sig.) of all variables is 0.850. 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). The results of data processing using SPSS 25.00 show the results in the following table: Based on the test above, the significance value is greater than 0.05 (5%), namely 0.532, so there is no indication of heteroscedasticity.

Simple Linear Regression Testing
Multiple linear regression testing explains the role of advertising (X) on consumer satisfaction (Z). 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 (2020) Based on these results, the multiple linear regression equation has the formulationZ = a + b1X + ɛ, so the equation is obtained: Z = 2.041 + 0.906X + ɛ The description of the multiple linear regression equation above is as follows: a. The constant value (a) of 2.041 indicates the magnitude of consumer satisfaction (Z) if advertising (X) is equal to zero. b. The value of the advertising regression coefficient (X) (b1) is 0.906 indicating the large role of advertising (X) on consumer satisfaction (Z). This means that if the advertising factor (X) increases by 1 value unit, it is predicted that consumer satisfaction (Z) will increase by 0.906 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 consumer satisfaction (Z). 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:  Based on table 4.10 it can be seen that the value of the adjusted R square is 0.580 or 58.0%. This shows if the ad (X) can explainconsumer satisfaction(Z) of 58.0%, the remaining 42.0% (100% -58.0%) is explained by other variables outside this research model. Such as service, price and interest in buying.

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 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  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 0.625. 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:  Based on table 4.12 it can be seen that the tolerance value of advertising (X) is 0.409, consumer satisfaction (Z) is 0.409 where everything is greater than 0.10 while the VIF value of advertising (X) is 2.446, consumer satisfaction (Z) is 2.446 where all are 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 25.00 show the results in the following table: Based on the test above, the significance value of advertising is greater than 0.05 (5%), namely 0.952, and testing the significance value of consumer satisfaction is greater than 0.05 (5%), namely 0.648, so there is no indication of heteroscedasticity.

Multiple Linear Regression Testing
Multiple linear regression testing explains the role of advertising (X) and consumer satisfaction (Z) on buying interest (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 (2020) Based on these results, the multiple linear regression equation has the formulation:Y = a + b1X + b2Z + ɛ, so the equation is obtained: Y = 0.612 + 0.426X + 0.531Z + ɛ The description of the multiple linear regression equation above is as follows: a. The constant value (a) of 0.612 indicates the amount of buying interest (Y) if advertising (X) and consumer satisfaction (Z) are equal to zero. b. The value of the advertising regression coefficient (X) (b1) of 0.426 indicates the large role of advertising (X) on purchase intention (Y) assuming the variable consumer satisfaction (Z) is constant. This means that if the advertising factor (X) increases by 1 value unit, it is predicted that buying interest (Y) will increase by 0.426 value units assuming constant customer satisfaction (Z). c. The value of the regression coefficient of consumer satisfaction (Z) (b2) of 0.531 indicates the large role of consumer satisfaction (Z) on purchase intention (Y) assuming the advertising variable (X) is constant. This means that if the consumer satisfaction factor (Z) increases by 1 unit value, it is predicted that buying interest (Y) will increase by 0.531 value units assuming advertising (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 onconsumer satisfaction(Z).
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 (2020) Based on table 4.15, it can be seen that the value of the adjusted R square is 0.758 or 75.8%. This shows that consumer satisfaction (Z) and advertising (X) can explain purchase intention (Y) of 75.8%, the remaining 24.2% (100% -75.8%) is explained by other variables outside the research model. This. Such as service, price and interest in buying.

Hypothesis testing a. t test (Partial)
The t statistical test is also known as the individual significance test. This test shows how far the influence of the independent variables partially on the dependent variable.
In this study, partial hypothesis testing was carried out on each independent variable as shown in Table 4.16 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:   In Figure 4.3 the path analysis shows the direct effect of variable X on variable Y of 0.377. While the indirect effect through variable Z is 0.769 x 0.554 = 0.426, the results of the calculations show that the indirect effect through variable Z is greater than the direct effect on variable Y. These results can be seen in table 4.20 below:  (2020) In the path analysis test it can be seen that the direct effect of advertising (X)on buying interest (Y) is greater than the indirect effect through advertising variable (X), on buying interest (Y) through consumer satisfaction (Z). This means that advertising is the independent variable on the relationship between consumer satisfaction and purchase intention. And consumer satisfaction is an intervening variable on the relationship between the influence of advertising and purchase intention.

Conclusion
Based on the results of the research and discussion in the previous chapter, it can be concluded as follows: 1) Based on the results of the research, the researcher concludes that advertisements are descriptively in the high classification or in the good category. This can be seen from the ad variable(X)influential significanton consumer satisfaction (Y). In theory Advertising can increase good consumer satisfaction, with advertising having a big influence on buying interest. 2) Based on the results of the study, the researchers concluded that descriptive advertising is in a high classification or has a positive effect on purchase intention. Where is the ad (X2)significant effecton buying interest (Y).Improving advertising can be done by paying attention to and meeting consumer needs properly so that it can significantly influence advertising on buying interest. 3) Based on the results of the study, the researchers concluded that descriptively consumer satisfaction is in the high classification or has a positive effect on purchase intention. Where is consumer satisfaction (X3)significant effecton buying interest (Y).Consumer satisfaction can