TYPES AND SOURCES OF DATA RESEARCH POPULATION AND SAMPLE Conclusion

28

D. TYPES AND SOURCES OF DATA RESEARCH

This study will use a type of ordinal data that is then converted into interval data. To obtain these data, used questionnaire data collection tool. Here decomposition types and sources of data used in this study: Table 3.2 Types and Sources of Data Variable Types of Data Source of Data Scale Current Ratio Ratio Primer Ratio Activity Ratio Ratio Primer Ratio Debt Ratio Ratio Primer Ratio Inflation Ratio Primer Ratio Profitability Ratio Ratio Primer Ratio Source: Data Processing, 2014

E. POPULATION AND SAMPLE

Population is a group of object which has characteristic similarity in one or more things and creates main problem in a specific research. Hence, sample is a part of population which its characteristic will be observed and considered as representative of all population Sugiyono, 2011:39. Based on the criteria set out in this study to determine the study sample, there were eight samples for the selection of these companies is because the company is engaged in the same industry, all of these companies have go public, as well as data from companies that provide complete data since 2010 until 2013. The last reason of 29 the selection of these companies because these companies have similarities in their assets, thus making the data obtained will be more specific, because we can assume that all companies have the same grade. The population in this study is the financial data’s of Property Company which are: Table 3.3 List of Companies No Name and Code of Company 2010 2011 2012 2013 1. Alam Sutera Realty Tbk ASRI √ √ √ √ 2. Bekasi Asri Pemula Tbk BAPA √ √ √ √ 3. Bumi Citra Permai Tbk BCIP √ √ √ √ 4. Sentul City Tbk BKSL √ √ √ √ 5. Ciputra Development Tbk CTRA √ √ √ √ 6. Bakrieland Development Tbk ELTY √ √ √ √ 7. Lippo Karawaci Tbk LPKR √ √ √ √ 8. Agung Podomoro Land Tbk APLN √ √ √ √ Source: Data Process, 2014

F. DATA COLLECTION METHODS

The type of data in this research is quantitative data which is the numbers that have units of count and can be calculated mathematically. The data in this research is gotten by using two kinds of method, those are:

1. Literature method; conducted by doing exploration and literature review.

The researcher assesses information through books, journals, literature, and others that related to support this research.

2. Documentation method; done by collecting, noting, and assessing secondary

data which is in the form of ready-made and have been collected and processed by other party. The secondary data in this research is annual financial report of property and real estate companies listed in Indonesia 30 Stock Exchange which is included in Indonesian Capital Market Directory ICMD from year 2010 to 2013. Hence, the data of exchange rate, interest rate, and inflation are obtained from Indonesian Financial Statistics issued by Indonesia Bank and issuance of publications such as: Financial Statements of Bank Indonesia, Central Bureau of Statistics, and other sources.

G. DATA PROCESSING METHOD

1. Normality Test

Normality test is performed to determine whether the data are normally distributed population. Normality test for each variable is done by looking at the distribution of data points on the graph QQ plot. Data - Data from a variable can be said to be normal, if the distribution of the data are spread on a straight line plot point. According Sarjono and Julianita 2011:64 states that the normality test if the researcher has the respondent 50, then Sig. Kolmogorov-Smirnov compared with alpha, whereas if the researcher has the following respondents 50, then Sig. Shapiro-Wilk compared with Alpha to test the normality of the data obtained by the researcher. Because the respondent in this research are more than 50, then Sig. Kolmogorov-Smirnov compared with alpha will be used. Basis for a decision on the normality test is as follows:  If the numeric significance Kolmogorov- Smirnov Test Sig ≥ 0, 05 then the data are normally distributed. 31  If the numeric significance Kolmogorov-Smirnov Test Sig 0, 05 then the data distribution is not normal. The value of Sig. or significance can be obtained by calculating a test of normality or plot through SPSS tools with confidence level of 95 or 5 error rate. Also in the figure of Q-Q plot is straight line from left to right. The line was derived from the value of Z. if the data is normal distribution, then the data will be scattered around the line.

2. Multicollinearity Test

Multicolinierity test aims to test whether the regression model found a correlation between the independent variables independent Ghozali, 2006. Results are expected in testing is not the correlation between the independent variables. There are several ways to test whether or not multicoloniarity in the regression model. In this test, the researchers used the analysis of the correlation matrix between the independent variables by looking at the value of Tolerance and Variance Inflation Factor VIF. If the tolerance value is greater than or equal to 0.10 VIF value of less than 10, it means not occur multicoloniarity in the regression model.

3. Heteroscedastity Test

Heteroscedasticity test aims to test whether the regression model occurred inequality variance of residuals of the observations to other observations Ghozali, 2006. If the variance of the residuals of the observations to other observations remained, then called homoskedasticity, and if different is called heteroscedasticity. A good regression model is that does not produce heteroskedasticity. In this study, researchers used a scatterplot graph to test whether there is heteroscedasticity. If 32 there is no obvious anyway, as well as the points spread above and below the number 0 on the Y axis, it does not happen heteroscedasticity homoscedasticity

4. Multiple Linear Regressions

Based on the opinion Engkos, Kuncoro Ahmad and Riduwan 2007:83 regression is a process of systematically estimate of what is most likely to happen in the future based on past and present information that is held so that mistakes can be minimized. Multiple regression aims to quantify the effect of two or more independent variables on a dependent variable and the dependent variable predicted by using two or more independent variables. Criteria that must be met for the regression is independent variable and the dependent variable must be interval scale. The general formula of the regression is: Y = a + b2 x2 + b1x1 +b x3 Decryption: Y = independent variable independent variable X = independent variable independent variable a = regression constant. b = slope of the regression line. The method of analysis used in this study is the Multiple Linear Regression method with the help of SPSS 21 program. The following will be described further research methods used in this study: 33

5. Hypothesis Test a.

Test for the Significance of the Partial Multiple Regression Model t-test This test used to know whether independent variables partially influence toward dependent variable or not, by assuming other independent variables are constant Engkos and Riduwan 2007. 1 H : β i = 0 It means the independent variables are not significant explanatory toward dependent variable. 2 H a : β i ≠ 0 It means the independent variables are significant explanatory toward dependent variable. Testing criteria which is used in this test is by comparing significance value reached by level significance defined that is 0.05. If the significance value 0.05, thus the independent variables can influence dependent variable significantly. In other words, the hypothesis is accepted.

b. Test for the Significance of the Overall Multiple Regression

Model F-test Overall F test used to determine whether there is a significant relationship between the dependent variable and the entire set of independent variables the overall multiple regression. Since there is more than one independent variable, this test uses the following null and alternative hypothesis Engkos and Riduwan 2007 34 1 H : β 1 = β 2 = …….= β 7 = 0 There is no linear relationship between the dependent variable and the independent variables. It means all independent variables are not significant explanatory toward dependent variable. 2 H a : At least one β 1 ≠ 0, j = 1,2, ….. , k There is linear relationship between the dependent variable and at least one of the independent variables. It means all independent variables are simultaneously significant explanatory toward dependent. 35 CHAPTER IV FINDING AND ANALYSES

A. GENERAL DESCRIPTION OF RESEARCH OBJECT

This chapter presents and discusses the findings of the research conducted. This study was a descriptive study on the influence of internal factors such as current ratio, liquidity ratio and debt ratio of the company as well as external factors such as inflation on profit ratio. The financial data of the selected companies compiled from data from the annual reports of listed companies in Indonesia Stock Exchange in 2010-2013. The period 2010-2013 was chosen to meet the requirements of research methods. That hypothesis was tested with multiple regression method. The sample selection criteria chosen by the company which has been described in research methodology in the previous chapter. 1. Overview of selected companies In the 2010-2013, the company is listed on the Indonesia Stock Exchange on subsectors Property Real Estate are 19 companies. The purposive sampling was used to select the sample. Of the 19 companies listed, only eight 8 companies that meet criteria in four years’ timeseries. Therefore, a total of 32 annual reports of companies. Report taken from the companys website. Eight 8 companies presenting their report are as follows: 36 Table 4.1 Eight 8 Selected Companies No. Name and Code of Company 1. Alam Sutera Realty Tbk ASRI 2. Bekasi Asri Pemula Tbk BAPA 3. Bumi Citra Permai Tbk BCIP 4. Sentul City Tbk BKSL 5. Ciputra Development Tbk CTRA 6. Bakrieland Development Tbk ELTY 7. Lippo Karawaci Tbk LPKR 8. Agung Podomoro Land Tbk APLN Source: Indonesia Stock Exchange

2. Overview of business development 8 selected companies

Nine 9 companies selected comprise establishments Property and Real Estate sectors. The reason that choosing companies in the sector because of what Im doing and I undertook the work on the field. Therefore, have a great responsibility for the disclosure of sustainability reports.

B. ANALYSIS AND DISCUSSION

1. Descriptive Analysis

The independent variable in this research is profitability ratio. From the indicators in the sustainability report, there are 3 internal indicators and 1 external indicator is used in this research result. 37 Table 4.2 Descriptive Statistics Source: Data Process 2015 a. Independent Variable Table 4.3 Profitability Ratio 2010-2013 in percentage No. Name and Code of Company 2010 2011 2012 2013 1. Alam Sutera Realty Tbk ASRI 16.26 33.68 61.19 44.62 2. Bekasi Asri Pemula Tbk BAPA 19.61 3.08 6.78 7.59 3. Bumi Citra Permai Tbk BCIP 15.5 1.93 6.7 22.91 4. Sentul City Tbk BKSL 2.61 4.58 7.04 20.07 5. Ciputra Development Tbk CTRA 20.58 25.21 20.6 11.65 6. Bakrieland Development Tbk ELTY 7.76 7.23 18.16 26.85 7. Lippo Karawaci Tbk LPKR 30.3 31.56 46.48 53.94 8. Agung Podomoro Land Tbk APLN 19.45 28.34 39.6 41.53 Source: Data Process, 2015 Based on the statistic calculation which is included in table above, the results can be explained as follows: For the variable profitability ratio, the value of the average profitability of all companies surveyed is 22.64. Furthermore, the highest value of the entire period is at the point of 61.18 and the N Minimum Maximum Mean Std. Deviation Skewness Kurtosis Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statist ic Std. Error CR 32 .50 4.89 2.0313 1.08056 .914 .414 .802 .809 AR 32 .20 3.18 1.3944 .68277 .631 .414 .344 .809 DR 32 .10 .76 .4328 .17554 .493 .414 .289 .809 PR 32

1.93 61.19 22.6416

16.83661 .627 .414 .576 .809 INFLATION 32 .03 .09 .0489 .01057 1.348 .414 3.557 .809 Valid N listwise 32 38 lowest value of all periods contained in point 1.93. Standard deviation indicates the fluctuation of data where the data are fluctuations in the value of 16.83. With an average value greater than the standard deviation, it indicates that there is no movement of the extreme value of the average profitability ratio in the whole object of research. b. Dependent Variable 1 Current Ratio Table 4.4 Current Ratio 2010-2013 in decimal No. Name and Code of Company 2010 2011 2012 2013 1. Alam Sutera Realty Tbk ASRI 0.97 0.97 1.23 0.73 2. Bekasi Asri Pemula Tbk BAPA 2.21 3.02 2.54 2.69 3. Bumi Citra Permai Tbk BCIP 2.33 2.63 0.76 0.61 4. Sentul City Tbk BKSL 2.81 3.16 3.18 4.57 5. Ciputra Development Tbk CTRA 4.89 2.36 1.55 1.35 6. Bakrieland Development Tbk ELTY 2.38 1.34 0.85 0.54 7. Lippo Karawaci Tbk LPKR 2.02 2.06 1.4 1.4 8. Agung Podomoro Land Tbk APLN 3 1.82 1.56 1.67 Source: Data Process, 2015 Based on the statistic calculation which is included in table above, the results can be explained as follows: For variable current ratio, the value of the average current ratio of all companies surveyed is 2.03. Furthermore, the highest score of all periods contained in point 4.89 and the lowest value of the entire period are at the point 0.50. Standard deviation indicates the 39 fluctuation of data where the data are fluctuations in the value of 1.080. With an average value greater than the standard deviation, it indicates that there is no movement of the extreme value of the average current ratio on the whole object of research. 2 Activity Ratio Table 4.5 Activity Ratio 2010-2013 in decimal No. Name and Code of Company 2010 2011 2012 2013 1. Alam Sutera Realty Tbk ASRI 1.73 1.86 3.18 1.21 2. Bekasi Asri Pemula Tbk BAPA 1.54 1.87 2.01 2.32 3. Bumi Citra Permai Tbk BCIP 2.31 2.53 2.82 4.91 4. Sentul City Tbk BKSL 1.87 2.01 2.39 2.43 5. Ciputra Development Tbk CTRA 0.64 0.72 2.17 2.38 6. Bakrieland Development Tbk ELTY 0.3 0.4 0.7 1.3 7. Lippo Karawaci Tbk LPKR 1.26 1.21 0.69 0.69 8. Agung Podomoro Land Tbk APLN 0.87 1.03 3.1 1.21 Source : Data Process, 2015 Based on the statistic calculation which is included in table above, the results can be explained as follows: For variable activity ratio, the average value of the entire company activity ratio studied is 3.18. Furthermore, the highest score of all periods contained in the 3.18 point and the lowest value of all periods contained in point 0.20. Standard deviation indicates the fluctuation of data where the data are fluctuations in the value of 0.68277. With an average value greater than the standard deviation, it indicates that 40 there is no movement of the extreme value of the average activity ratio in the whole object of research. 3 Debt Ratio Table 4.6 Debt Ratio 2010-2013 in decimal No. Name and Code of Company 2010 2011 2012 2013 1. Alam Sutera Realty Tbk ASRI 0.51 1.86 3.18 1.21 2. Bekasi Asri Pemula Tbk BAPA 0.45 0.45 0.45 0.47 3. Bumi Citra Permai Tbk BCIP 0.19 0.22 0.43 0.47 4. Sentul City Tbk BKSL 0.14 0.13 0.21 0.35 5. Ciputra Development Tbk CTRA 0.22 0.33 0.43 0.51 6. Bakrieland Development Tbk ELTY 0.38 0.38 0.39 0.41 7. Lippo Karawaci Tbk LPKR 0.49 0.48 0.53 0.54 8. Agung Podomoro Land Tbk APLN 0.45 0.53 0.58 0.63 Source : Data Process, 2015 Based on the statistic calculation which is included in table above, the results can be explained as follows: For variable debt ratio, the value of the average debt ratio of all companies surveyed is 0.4328. Furthermore, the highest score of all periods contained in point 0.76 and the lowest value of all periods contained in point 0.10. Standard deviation indicates the fluctuation of data where the data are fluctuations in the value of 0.17554. With an average value greater than the standard deviation, it indicates that there is no movement of the extreme value of the average debt ratio in the whole object of research. 41 4 Inflation Table 4.7 Inflation 2010-2013 in decimal No. Month 2010 2011 2012 2013 1. January - February 0.086 0.043 0.040 0.046 2. March - April 0.059 0.046 0.036 0.055 3. May – June 0.063 0.043 0.035 0.050 4. July - August 0.056 0.046 0.038 0.048 5. September 0.054 0.046 0.042 0.063 6. October 0.053 0.045 0.041 0.057 7. November 0.055 0.045 0.40 0.058 8. December 0.043 0.045 0.032 0.056 Source : Data Process, 2015 Based on the statistic calculation which is included in table above, the results can be explained as follows: For variable inflation, the average value of inflation of all companies surveyed is 0.489. Furthermore, the highest score of all periods contained in the 0.09 point and the lowest value of the entire period are at the point 0:03. Standard deviation indicates the fluctuation of data where the data are fluctuations in the value of 0.1057. With an average value greater than the standard deviation, it indicates that there is no extreme movement of the average value of inflation on the whole object of research. 42

2. Data Processing

a. Classic Assumption Test

1 Normality Normality test is used to ensure that all the data is still at the normal level or not there is an extreme value that interfere with research results. Here are the results of the testing of normality Figure 4.1 Normality Test To see the normality test, it appears that the whole dots still are at about the linearity means the whole of the data used still meet the assumption of normality. 2 Multicolinearity Test Multicollinearity test has purpose to test whether the regression model found a correlation between the independent variables. A good regression models should have no correlation between independent variables. If the independent variables are correlated, then this variable is not orthogonal. Orthogonal variable is the independent variable in which the correlation value between the 43 members of independent variables is equal to zero 0. Multicollinearity can be known from the value of tolerance and Variance Inflation Factor VIF. If the tolerance value is greater than 0.1 and VIF values smaller than 10, then the regression is free from multicollinearity. VIF and Tolerance value from independent variables on regression model can be seen in table below: Table 4.8 The Result of Multicolinearity Test Coefficients a Model Collinearity Statistics Conclusion Tolerance VIF 1 Constant CR ,947 1,056 No Multicollinearity AR ,702 1,425 No Multicollinearity DR ,558 1,791 No Multicollinearity INFLATION ,545 1,835 No Multicollinearity a. Dependent Variable: PR The result shows that the VIF values of all independent variables having a value smaller than 10 1.056, 1.425, 1.791 and 1.835, It means that the variables of the study did not show any multicollinearity in the regression model. 3 Heteroscedacticity Test Aims to test whether in the regression model have the variance inequality from one residual observation to the order. If the variance of residual is fixed, then it is called homoscedasticity and if different called heteroscedasticity. A good regression mo del is homoscedasticity or doesn’t have heteroscedasticity. 44 The method used to detect the presence of absence of heteroscedasticity is to look at the heteroscedasticity graph between the predicted values of the dependent variable with independent variables. From the scatterplot below show the dots spread as randomly and spread above and below 0 and Y axis, it means that there is no heteroscedasticity on the regression model, so the regression model is proper to use on the test. To be more can be seen on figure 4.11 below: Figure 4.2 Scatterplot Dependent Variable 4 Autocorrelation Autocorrelation is used to determine the presence or absence of classic assumption deviation autocorrelation is the correlation between the residuals on the observation by other observations in the regression model. 45 Table 4.9 Autocorrelation Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 .901 a .811 .783 7.84257 1.333 a. Predictors: Constant, Inflation, Current_Ratio, Activity_Ratio, Debt_Ratio b. Dependent Variable: Profitability_Ratio From the results obtained above output value of DW generated from the regression model is 1.333. While the DW table by 0.05 and the number of data n = 32, k = 4 k is the number of independent variables obtained value dU dL at 1.24 and 1.65. Because the value of DW 1,333 are in the area between dL and dU, it does not produce definitive conclusions.

c. Hypothesis Test 1

F-test F test measures the significance of independent variable CR, AR, DR, INFLATION in affecting dependent variables PR. The output of F test can be show below: Table 4.11 F-test ANOVA a Model Sum of Squares df Mean Square F Sig. Regression 7126.950 4 1781.738 28.969 .000 b Residual 1660.659 27 61.506 Total 8787.610 31 a. Dependent Variable: PR b. Predictors: Constant, INFLATION, CR, AR, DR From the table above, can be formulated hypotheses for his fifth goal as follows: 46 Ho: CR, AR, DR, INFLATION does not have a significant effect on PR Ha: CR, AR, DR, INFLATION has a significant effect on PR Basic Decision Sig Alpha, then Ho is accepted Sig Alpha, then Ha accepted Decision Sig = 0.000 Alpha = 0.05 Then, the Sig Alpha, Ho accepted Result Through testing the significance of the above, it can be concluded that CR, AR, DR, INFLATION has a significant effect on PR 2 t-Test Table 4.12 t-Test Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics B Std. Error Beta Zero- order Parti al Part Tolerance VIF 1 Constant -47,047 9,652 4,87 4 ,000 CR ,494 1,339 ,032 ,369 ,715 -,168 ,071 ,031 ,947 1,056 AR 2,859 2,463 ,116 1,16 1 ,256 ,077 ,218 ,097 ,702 1,425 DR 42,457 10,738 ,443 3,95 4 ,000 ,773 ,606 ,331 ,558 1,791 INFLATION 947,176 180,568 ,594 5,24 6 ,000 ,783 ,710 ,439 ,545 1,835 a. Dependent Variable: PR From the above table, it can be concluded hypothesis testing as follows: a For the purpose of CR Ho: CR does not have a significant effect on PR Ha: CR has a significant effect on PR 47 Basic Decision Sig Alpha, then Ho is accepted Sig Alpha, then Ha accepted Decision Sig = 0.715 Alpha = 0:05 Then, the Sig Alpha, Ho accepted Result Through testing the significance of the above, it can be concluded that the CR does not have a significant effect on PR. b For the purpose of AR Ho: AR does not have a significant effect on PR Ha: AR has a significant effect on PR Basic Decision Sig Alpha, then Ho is accepted Sig Alpha, then Ha accepted Decision Sig = 0.256 Alpha = 0:05 Then, the Sig Alpha, Ho accepted Result Through testing the significance of the above, it can be concluded that the AR did not have a significant effect on PR. c For the purpose of DR Ho: DR does not have a significant effect on PR 48 Ha: DR has a significant effect on PR Basic Decision Sig Alpha, then Ho is accepted Sig Alpha, then Ha accepted Decision Sig = 0.000 Alpha = 0:05 So, Sig Alpha, Ha accepted Result Through testing the significance of the above, it can be concluded that the DR has a significant effect on PR. d For the purpose of Inflation Ho: Inflation does not have a significant effect on PR Ha: Inflation has a significant effect on PR Basic Decision Sig Alpha, then Ho is accepted Sig Alpha, then Ha accepted Decision Sig = 0.000 Alpha = 0:05 So, Sig Alpha, Ho accepted Result Through testing the significance of the above, it can be concluded that Inflation has a significant effect on PR. 49

d. Regression Equation

Furthermore, based on table 4.26, the SPSS result of coefficient regression formed a multiple linear regression equation as follow Y=-47.047+0.494CR+2.859AR+42.457DR+947.176INFLATION+e This means that: Y value when the variable CR, AR, DR and Inflation = 0 is -47 047 which means that all changes in Y has a high dependence on the value of CR, AR, DR, and Inflation. If the DR increased by 1 point while CR, AR, and inflation has a fixed point it will affect Y or PR by 42.457 If the Inflation increased by 1 point while CR, AR, and DR has a fixed point it will affect Y or PR by 947.176 This study fits with previous research that has been run by Saleem and Rehman 2011, which explained that the liquidity ratio effect on profitability ratios, further research Yanuardi, Alindra 2011 explained that the solvency ratio to Affect profitability ratio, and the latter according to research results Kabajeh et al 2012 described that the activity ratios with a profitability ratio. Other than that according to Heikal et al 2014 and Fengju et al 2013 which is also explained that the debt ratio may affect the profitability ratio has been proven in this study. 50

e. Coefficient of Determination R Square

Coefficient determination measures the ability of independent variable CR, AR, DR, INFLATION elaborate dependent variables PR. Coefficient determination value shows how much variation in the data can be explained by the regression model built. Table 4.13 Coefficient of Determination R Square Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 .901 a .811 .783 7.84257 1.333 a. Predictors: Constant, Inflation, Current_Ratio, Activity_Ratio, Debt_Ratio b. Dependent Variable: Profitability_Ratio From the table above, it appears that the value of R square of 0.811 which means CR, AR, DR, inflation simultaneously affect PR by 0811 or 81 variation were formed on the PR generated by the CR, AR, DR, inflation simultaneously and there are still 19 factors beyond the actual research also affect PR. Some other factors that could affect the PR in addition to the above four factors such as accounts receivable, long-term debt, as well as the amount of dividends. 51 CHAPTER V CONCLUSSION

A. Conclusion

From the research that has been carried out, it can be concluded some of the following: 1. Current ratio does not have a significant effect on the profitability ratio on the real estate company in the Indonesian Stock Exchange from 2010 to 2013 Activity ratio does not have a significant effect on the profitability ratio on the real estate company in the Indonesian Stock Exchange 2010-2013 where any increase in the value of the variable current ratio, the ratio of the value of profitability will also increase. 2. Debt ratio has a significant effect on the profitability ratio on the real estate company in the Indonesian Stock Exchange 2010-2013 where any increase in the value of the variable debt ratio, the ratio of the value of profitability will also increase. 3. Inflation has a significant effect on the profitability ratio on the real estate company in the Indonesian Stock Exchange 2010-2013 where any increase in the value of the variable inflation; the profitability ratio value will also increase. 4. Simultaneously, current ratio, activity ratio, debt ratio and inflation has a significant effect on the profitability ratio on the real estate company in the Indonesian Stock Exchange from 2010 to 2013. 52

B. Implication Recommendation