Influence of Capital Adequacy Ratio (CAR) and Operational Expenses to
Operating Income (BOPO) towards Return on Assets (ROA)
Andi Silvan
STIE Manajemen Bisnis Indonesia
|
Keywords |
Abstract |
|
CAR, BOPO, ROA |
The reason for this ponder is to get data within
the shape of a more in-depth clarification of the relationship and impact
between the CAR and BOPO proportions on ROA and comes about from handled
information concerning how much the relationship and impact between the CAR
and BOPO proportions on ROA. This research aims to obtain deeper information about the
relationship and influence of the Capital Adequacy Ratio (CAR) on Return on
Assets (ROA). The strategy utilized in this considers employment
the impact strategy. This sort of investigation is graphic confirmation with
a quantitative approach. Information collection procedures utilizing
auxiliary information. At that point the information examination procedure
employments a computer application program, specifically the Factual Item and
Benefit Arrangement (SPSS) adaptation 26. The comes about of examination of
different straight relapse conditions delivers a condition, to specific Y =
0.002 + 0.004X1 + 0.000X2 + e. It can be concluded that on the off chance
that all free factors are zero, the esteem of the subordinate variable is
0.002. The coefficient esteem of the CAR variable (X1) is 0.004 and the
coefficient esteem of the BOPO variable (X2) is 0.000. |
Corresponding Author: Andi
Silvan
Email: [email protected]
INTRODUCTION
Agreeing to PT Law No. 40 of 2007 Article 1
Definition of a Constrained Risk Company may be a legitimate substance that
could be a capital association, built up based on an assertion, carrying out
commerce exercises with authorized capital which is partitioned into offers,
and meets the prerequisites stipulated in this law and executing controls (Lubis, 2018) .
The Indonesian Stock Exchange commonly abbreviated
as BEI is regulated in Law No. 8 of 1995 as a capital market (Lubis, 2018) . The Indonesian Stock Exchange
has a broad definition, namely the party that organizes and provides a system
or means for bringing together offers to sell and buy securities of other
parties to trade securities between them, including debt recognition letters,
shares, bonds, proof of debt, and securities. commercial (Kanawa, 2017) . The Indonesian Stock Exchange
acts as a securities trading facilitator by disseminating stock exchange
information and as an authority that controls the course of securities
transactions. The Indonesian Stock Exchange is the official exchange in
Indonesia, so companies that want to go public in Indonesia must go through the
Indonesian Stock Exchange (BEI).
Keeping money may be a financial institution that
incorporates a part of the budgetary framework in Indonesia. The presence of
the banking segment is a critical part, where most people's lives involve
services from managing an account division. This is often since the keeping
money division is an institution that carries out the most work as a money-related
middle person between parties who have stores (overflow stores) and parties who
require stores (shortfall stores) as well as an institution whose work is to
encourage the stream of activity installment.
In Indonesia, the keeping money framework utilized
could be a double managing an account framework where two sorts of managing an
account businesses work, to be specific sharia banks and routine banks. In this
way, the approaches taken by the government through Bank Indonesia are
certainly distinctive for the two sorts of banks. Sharia banks don't recognize
the intrigued framework, so benefits can be sourced from benefit sharing with trade-performing
artists who utilize reserves from Sharia banks as well as ventures from Sharia
banks themselves.
Initially, there were only three Islamic banks in
Indonesia, but now their growth is increasing. One of the indicators of a
company's performance is profitability. Profitability is management's ability
to generate profits. One of the appropriate proxies for measuring the
profitability of a bank is to look at the size of the Return On Assets (ROA),
this shows the bank's ability to generate income from managing the assets it
owns (Niode & Chabachib, 2016) .
One way to decide the well-being level of a bank's
money-related execution is by measuring the bank's benefit execution. Return on
Resources (ROA) may be a proportion utilized to the degree of bank management's
capacity to get benefits and oversee the general level of bank commerce
effectiveness. The more noteworthy the esteem of this proportion demonstrates
the bank's commerce productivity level is getting way better or more
advantageous. The reason for choosing ROA as the dependent variable is
that ROA is used to measure the company's effectiveness in generating profits.
The ROA obtained in 2017 � 2021 at Bank Muamalat is shown in Table 1.
Table 1 ROA Growth Data PT. Bank Muamalat, Tbk.
2017-2021
|
Year |
ROA |
ROA Growth |
|
2017 |
0.11% |
0 |
|
2018 |
0.08% |
-0.92% |
|
2019 |
0.05% |
-0.95% |
|
2020 |
0.03% |
-0.97% |
|
2021 |
0.02% |
-0.98% |
|
Average |
0.29% |
-0.955% |
Source:
PT. Bank Muamalat, Tbk.
Based on Table 1 at PT. Bank Muamalat, Tbk, the
level of Return on Assets for the period 2017 to 2021 has decreased where the
ROA is 0.11%, 0.08%, 0.05%, 0.03%, and 0.02%. With an average value of negative
ROA growth, namely (-)0.955. This value indicates a decrease in ROA, this
decrease indicates that the company management's ability has decreased in manage
its assets to generate profits. For example, from 2017 to 2018, based on the
annual financial report of PT. Bank Muamalat Tbk, in 2017 the profit earned was
IDR 26,115,563 (in thousands of Rupiah), and in 2018 it was IDR 46,002,044 (in
thousands of Rupiah) while the total assets in 2017 were IDR 61,696,919,644 (in
thousands of Rupiah) and in 2018 amounting to IDR 57,227,276,046 (in thousands
of Rupiah). This shows that the level of bank efficiency in carrying out its
operations influences the level of income generated by the bank.
Profitability is the most appropriate indicator
for measuring the success of a bank. The level of benefit is measured utilizing
ROA (Return on Resources) which is utilized to determine a company's viability
in creating benefits by utilizing the resources it possesses. ROA could be a
comparison between benefits that are sometimes recently charged and normally
add up to resources, which appears the capacity of all resources utilized to produce
benefits. The budgetary proportions that impact ROA are Capital Ampleness
Proportion (CAR), Working Costs and Working Wage (BOPO), Financing to Store
Proportion (FDR), Net Intrigued Edge (NIM), Non-Performing Credits (NPL) (Nanda, Hasan, & Aristyanto, 2019) .
Capital Adequacy Ratio (CAR) is capital ampleness
which appears as the bank's capacity to preserve adequate capital and the
capacity of bank administration to distinguish, degree, screen, and control
dangers that emerge which can impact the sum of bank capital (Dedi Gunawan Putra & Raymond, 2019) .
Working Costs and Working Salary (BOPO) may be a
proportion that appears in the execution comparison between operational costs
brought about by the bank and operational wage that the bank can create (Budianto & Dewi, 2023) . This operating income ratio is
also called the efficiency ratio which is used to measure the ability of bank
management to control the operational costs incurred against the operational
income it obtains (Asri, 2018) .
Net Interest Margin (NIM) is a comparison of net
interest income to average productive assets (Purba & Triaryati, 2018) . This proportion demonstrates
the bank's capacity to create net intrigued pay by putting in profitable
resources. The more prominent this proportion, the superior the bank's
execution in creating intrigued wages. Be that as it may, it must be guaranteed
that typically not due to tall intermediation costs, the suspicion is that
intrigued pay must be reinvested to reinforce bank capital (Purwanti, 2020) .
Non-performing credits (NPL) could be a proportion
utilized to the degree of a bank's capacity to cover the chance of
disappointment in repaying credit by indebted individuals. Banks must watch out
in disseminating credit to maintain a strategic distance from tall NPLs (Fernos & Itra, 2022) .
Financing to Store Proportion (FDR) could be a
proportion that measures a bank's capacity to reimburse commitments to clients
who have contributed to the stores they have with the credit they have given to
their indebted individuals (Nanda et al., 2019) .
Financing to Store Extent (FDR) may well be an
extent that measures a bank's capacity to repay commitments to clients who have
contributed to the stores they have with the credit they have given to their
obliged individuals (Ferdinan Eka Putra Eka Putra & Kindangen, 2016) .
Based on the research background, it is suspected
that the Capital Adequacy Ratio (CAR) and Operating Expenses and Operating
Income (BOPO) play a significant role in contributing to Return On Assets
(ROA), so this research is entitled "The Effect of Capital Adequacy Ratio
(CAR) and Operating Expenses on Operating Income (BOPO ) on Return On Assets
(ROA) at PT. Bank Muamalat Tbk, Jakarta� (Sari, nd) .
This research aims to obtain deeper information
about the relationship and influence of the Capital Adequacy Ratio (CAR) on
Return on Assets (ROA). CAR is an indicator used to measure the level of bank
capital adequacy in facing risk. This research aims to identify whether there
is a relationship between CAR and ROA, which can provide an understanding of
the extent to which the level of bank capital adequacy affects the bank's
financial performance.
Apart from that, this research also aims to obtain
deeper information about the relationship and influence of Operating Expenses
and Operating Income (BOPO) on Return on Assets (ROA). BOPO is an indicator
that describes the comparison between operational expenses and operational
income of a bank. By analyzing the relationship between BOPO and ROA, this
research aims to understand the extent to which bank operational efficiency can
impact the bank's financial performance. Furthermore, this research also aims
to obtain information from processed data on how big the relationship and
influence of Capital Adequacy Ratio (CAR) and Operating Expenses and Operating
Income (BOPO) are on Return on Assets (ROA). By examining these two factors
simultaneously, this research will provide a more comprehensive understanding
of the factors that contribute to bank financial performance, as well as the
extent of their simultaneous influence on ROA.
RESEARCH
METHODS
The type of research used is quantitative research
with descriptive methods. The population presented in this company research is
the Financial Report of PT. Bank Muamalat, Tbk which has been registered and is
still listed on the Indonesia Stock Exchange (BEI). The sample in this research
is the CAR, BOPO, and ROA ratios in PT's financial statements. Bank Muamalat
with 32 samples or 32 quarters for 8 years. The subject of this research is PT.
Bank Muamalat, Tbk. The objects that will be studied by researchers are the
independent variable and the dependent variable. The independent variables in
this research are CAR (Capital Adequacy Ratio) as X1 and BOPO (Operating
Expenses and Operating Income) as X2. The dependent variable in this research
is ROA (Return on Assets) as Y. In this research, the data used is PT's
quarterly published financial report data. Bank Muamalat, Tbk. 2014-2021
period. The type of data analysis used is descriptive statistics. This data
analysis technique will be tested using the SPSS version 26 system. SPSS or the
abbreviation for Statistical Package for the Social Sciences is a software that
can be used to help with statistical data processing, calculations, and
analysis.
RESULTS AND
DISCUSSION
Normality test
Source: processed with SPSS 26.0
Based on the results in Table 2 using the one-sample
Kolmogorov-Smirnov test method, a probability number or Asymp is obtained. Sig.
(2-tailed) of Unstandardized Residual is 0.000, which means that the
significance value for the CAR variable (X1), BOPO variable (X2), and ROA
variable (Y) is 0.000. Because probability numbers or Asymp. Sig. (2-tailed) is
less than 0.05, which means that the data is not normally distributed (Tala & Karamoy, 2017) .
Classic Test (Multicollinearity,
Heteroscedasticity, Autocorrelation)
1. Multicollinearity
Test
Table 3 Multicollinearity Test Results
Source: processed with SPSS 26.0
Table 3 shows the tolerance value for the CAR
variable (X1) of 0.987 > 0.1 and the BOPO variable (X2) of 0.987 > 0.1.
Meanwhile, the VIF value of the CAR variable (X1) is 1.013 < 10 and the BOPO
variable (X2) is 1.013 < 10, so it can be concluded that the
multicollinearity assumption has been fulfilled or that there are no symptoms
of multicollinearity.
2. Heteroscedasticity Test
Figure 1 Scatter plot
Source: processed with SPSS 26.0
By looking at
Figure 1, you can see that the points are spread randomly, both above and below
the number 0 (zero) on the Y axis. Thus it can be explained that the regression
model does not have symptoms of heteroscedasticity (Lawendatu, Kekenusa, & Hatidja, 2014) .
3. Autocorrelation Test
Table 4 Autocorrelation Test Results
Source:
processed with SPSS 26.0
Based on Table 4, the Durbin Watson value is 1.887
and the du value is obtained from K (2) = N (32) with a significance of 5%
which can be seen in the Durbin Watson table, namely 1.5736, which can be
calculated as du (1.5736) < Durbin Watson ( 1.887) < 4-du (2.4264) and
the results show no symptoms of autocorrelation.
Test Method
A.
Correlation
coefficient
1. Partial Correlation Coefficient Test
Table 5 Partial Correlation Coefficient
Test Results
Source: processed with SPSS 26.0
Table 5 shows the results of the correlation
coefficient value between the CAR variable (X1) and the ROA variable (Y),
producing a figure of 0.237 with positive results. The correlation coefficient
value between the BOPO variable (X2) and the ROA variable (Y) is (-0.129) with
a negative value (Susanto & Kholis, 2016) .
2.
Simultaneous
Correlation Coefficient Test
Table 6 Simultaneous Correlation Coefficient Test Results
Source: processed with SPSS 26.0
B.
Coefficient
of Determination
1. Partial Coefficient of Determination Test
Table 7 Results of CAR Partial Determination Coefficient
Test on ROA
Source:
processed with SPSS 26.0
Based on Table 7, shows that the partial
coefficient of determination (R Square) between the CAR variable (X1) and the
ROA variable (Y) is 0.056.
Table 8 BOPO Partial Determination Coefficient Test Results
on ROA
Source: processed with SPSS 26.0
Based on Table 8, shows that the partial
coefficient of determination (R Square) between the BOPO variable (X2) and the
ROA variable (Y) is 0.017.
2. Simultaneous Coefficient of Determination Test
Table 9 Simultaneous Coefficient of Determination Test
Results
Source: processed with SPSS 26.0
Based on Table 9, shows the results of the
simultaneous Adjusted R Square (R2) coefficient of determination test between
the CAR variable (X1) and the BOPO variable (X2) on the ROA variable (Y) of
0.225. This value means the ability of the combination of the CAR variable (X1)
and the BOPO variable (X2) to simultaneously influence the ROA variable (Y).
C. Multiple Regression Equation
Table 10 Multiple Linear Regression Analysis
Source: processed with SPSS 26.0
Based on Table 1 0, the results of the multiple
linear regression equation in this study are:
Y = a + b1X1 + b2X2 + e
Y = 0.002 + 0.004X1 + 0.000X2 + e
Where:
Y = ROA
a = constant = 0.002
b1 = regression coefficient X1 = 0.004
b2 = regression coefficient X2 = 0.000
X1 = CAR
X2 = BOPO
e
= error
The Multiple Linear Regression equation obtained
from the SPSS processing results is Y = 0.002 + 0.004X1 + 0.000X2 + e. Thus,
the constant value obtained is 0.002, which means that if the CAR variable (X1)
and the BOPO variable (X2) have a value of 0, the value of the ROA (Y) variable
is 0.002. The regression coefficient value for variable X1 is 0.004 and the
regression coefficient value for variable X2 is 0.000. So it can be interpreted
that if there is an increase in variable X1 then the value of variable Y will
increase assuming the values of other variables X are fixed or constant.
Meanwhile, for variable X2, if there is an increase of 1 point, the value of
variable Y will increase assuming the values of another variable
Hypothesis
testing
1. T Statistical Test
Table 11 T-Test Results
Source: processed with SPSS 26.0
From Table 11 it can be clarified that most
testing the theory of the relationship and impact of the Capital Ampleness
Proportion (CAR) variable on the Return on Resources (ROA) variable got a
t-count esteem of 1.249 with a noteworthiness level of 0.222. The t-count
esteem is compared with the t-table at a noteworthiness level (α) = 5% or
0.05 with df = 29, the comes about gotten are t-count = 1.249 < t
xss=removed> 0.05, this implies that for testing the CAR variable (X1)
against the ROA variable (Y) H0 is acknowledged and H1 is rejected. This
implies that it isn't proven that there's a noteworthy relationship between the
CAR variable (X1) in part and the ROA variable (Y).
The next hypothesis test, partially the
relationship between the Operating Expenses and Operating Income (BOPO)
variable with the Return on Assets (ROA) variable, obtained a t-value of
(-0.577) with a significance level of 0.568. And the t-count value is compared
with the t-table at a significance level (α) = 5% or 0.05 with df = 29,
the result obtained is t-count = (-0.577) < t-table = 2.045, so H2 is
rejected. So it can be concluded that there is no significant relationship
between the BOPO variable (X2) and the ROA variable (Y). For a significance
level of 0.568 > 0.05, it means that H0 is accepted and H2 is rejected,
meaning that it is not proven that there is a significant relationship between
the BOPO variable (X2) partially and the ROA variable (Y).
2.
F test
Table 12 Significant
F Test Results
Source: processed with SPSS 26.0
Based on Table 12, it can be seen that testing the
hypothesis of the influence of the independent variables CAR (X1) and BOPO (X2)
simultaneously on ROA (Y) obtained an F-count value of 1.040 with a sig of
0.366. The F-calculated value is compared with the F-Table at a significance
level (α) = 5% or 0.05 with df numerator = 2 and df = 29, the results
obtained are F-calculated = 1.040 < F-Table = 3.33 with the level
significance 0.366 > 0.05. So it can be stated that H3 is rejected and H0 is
accepted, thus the results of hypothesis testing are that simultaneously there
is no significant influence between the CAR variable (X1) and the BOPO variable
(X2) on the ROA variable (Y).
Discussion
A.
Method Test
Results
Correlation
coefficient
1.
Partial Correlation Coefficient
Based on Table 5, the interpretation of the
correlation coefficient value obtained between the CAR variable (X1) and the
ROA variable (Y) produces a figure of 0.237. And this value is between the
interval 0.200 � 0.399 with a low interpretation. Meanwhile, the results of the
correlation coefficient test for the BOPO variable (X2) with the ROA variable
(Y) are (-0.129) with a negative value and this value is between the interval
0.000 - 0.199 with a very low interpretation.
Thus, the results of the correlation coefficient
analysis value obtained partially can be interpreted as meaning that the
relationship between the CAR variable (X1) and the ROA variable (Y) is positive
with a low level of relationship strength, and the relationship between the BOPO
variable (X2) and the ROA variable (Y). is negative with a very low level of
relationship strength. A positive relationship can be interpreted as the
relationship between the independent variable running in the same direction as
the dependent variable and vice versa if it is negative. Or it can be
interpreted that if the CAR variable (X1) increases, the ROA (Y) variable will
increase, whereas if BOPO (X2) increases, ROA (Y) will decrease.
2.
Simultaneous Correlation Coefficient
Based on Table 6, shows the results of the
correlation coefficient test (R) simultaneously between the CAR variable (X1)
and the BOPO variable (X2) on the ROA variable (Y) of 0.524 with a positive
value. Based on Table 3.2, this value is in the interval 0.40 � 0.599 with a
moderate level of relationship strength.
Thus, the results of the correlation coefficient
analysis obtained simultaneously can be interpreted as meaning that the
relationship between the CAR variable (X1) and the BOPO variable (X2)
simultaneously with the ROA variable (Y) is positive with a moderate level of
strength.
Coefficient
of Determination
1.
Partial Coefficient of Determination
Based on Table 7 and Table 8, the partial value (R
square) between the CAR variable (X1) and the ROA
variable (Y) is 0.056. The partial value (R square) between the BOPO
variable (X2) and the ROA variable (Y) is 0.017.
Thus, the value of the coefficient of
determination (KD) or how large the percentage of the ability of the CAR
variable (X1) to partially influence the ROA variable (Y) is 5.6 %. The value
of the coefficient of determination (KD) or how large the percentage of the
BOPO variable (X2) can partially influence the ROA variable (Y) is 1.7 %.
2.
Simultaneous Coefficient of Determination
Based on Table 9, the Adjusted R Square value is
0.225. The coefficient of determination (KD) value or how large the percentage
of ability between the CAR variable (X1) and the BOPO variable (X2)
simultaneously influences the ROA variable (Y) requires the following
calculation:
KD �������������� = Adjusted R 2 X
100%
���������������������� = 0.225 X 100%
���������������������� = 22 .5 %
This means that the coefficient of determination
(KD) value for the percentage of ability provided by the combination of the CAR
variable (X1) and the BOPO variable (X2) in influencing the ROA variable (Y) is
22.5% and the remaining 77.5% is influenced by other variables that are not
examined in this research.
Multiple
Linear Regression Equations
The results in Table 10 show that the equation
obtained is Y = 0.002 + 0.004X1 + 0.000X2 + e.
In this equation, it can be interpreted that for
every one unit increase in the CAR variable (X1), the ROA variable (Y) will
increase by 0.004 with the assumption that the other independent variables from
the regression model remain constant. Every time the BOPO variable (X2)
increases by one unit, the ROA (Y) variable will increase by 0.000, assuming
that the other independent variables from the regression model remain constant.
B. Hypothesis Test Results
T-test
Within the T-Test, there are two ways to get data
on whether the autonomous factors CAR and BOPO have a halfway impact on the ROA
(Y) variable, specifically by looking at the comes about of the t column and
the comes about of the critical column in Table 11 within the variable CAR
(X1). The about of the t column gets the t esteem. -number = 1.249 < t
xss=removed> 0.05, meaning that for testing the CAR (X1) variable on the ROA
(Y) variable, H0 is acknowledged and H1 is rejected. This means that it isn't
demonstrated that there's a partially significant relationship between the CAR
variable (X1) and the ROA variable (Y).
Within the BOPO variable (X2), the comes about of
the t column appears that the t-value = (-0.577) < t xss=removed> 0.05,
meaning that H0 is acknowledged and H2 is rejected, meaning that it isn't
demonstrated that there's an in part critical relationship between the BOPO
(X2) variable and the variable ROA (Y).
F test
In Table 13, it is gotten that F-count = 1.040
< F xss=removed> 0.05. This implies that for testing the CAR variable
(X1) and the BOPO variable (X2) on the ROA variable (Y) is that H0 is
acknowledged and H3 is rejected, meaning that there's no noteworthy impact of
the CAR variable (X1) and the BOPO variable (X2) at the same time on the ROA
variable (Y).
H0 Acceptance and H0 Rejection Curves
Figure 2 Areas of Acceptance of H0 and
Rejection of H0
Source: processed, 2023
The results of the first hypothesis test (H1) are
that H01 is accepted and Ha1 is rejected, which means that partially the CAR
variable (X1) does not have a significant relationship with ROA (Y). The
results of the second hypothesis test (H2) are that H02 is accepted and Ha2 is
rejected, which means that partially the BOPO variable (X2)
does not have a significant relationship with ROA (Y). The results of the third
hypothesis test (H3) are that H03 is accepted and Ha3 is rejected, which means
that simultaneously the CAR variable (X1) and the BOPO
variable (X2) have no significant influence on ROA (Y).
CONCLUSION
Based on the inquiry about what comes about displayed, it can be
concluded that this inquiry has succeeded in creating a show to analyze the
relationship between the factors CAR (X1), BOPO (X2), and ROA (Y). The comes
about of impact test shows that somewhat, the CAR variable (X1) has an impact
of 5.6% on the ROA variable (Y), whereas the BOPO variable (X2) has an impact
of 1.7%. Be that as it may, at the same time, the CAR variable (X1) and the
BOPO variable (X2) as it were have an impact of 22.5 % on the ROA variable (Y).
The numerous direct relapse conditions appear that the CAR variable (X1)
encompasses a relationship within the opposite direction to the ROA variable
(Y), whereas the BOPO variable (X2) features a relationship within the inverse
heading as well. However, there's no noteworthy concurrent impact between these
two factors on the ROA (Y) variable. The comes about of the relationship test shows
that there's a moo and positive relationship between the CAR variable (X1) and
the ROA variable (Y), whereas the relationship between the BOPO variable (X2)
and the ROA variable (Y) is moo and negative. By and large, this inquiry
bolsters the discoveries of past experimental thinks, somewhat affirming the
presence of a relationship between the factors CAR (X1), BOPO (X2), and ROA
(Y). �
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