THE DETERMINANT OF MSME FINANCING

DISTRIBUTION OF INDONESIA SYARIAH BANKS

By

Ulfah Noor Hasanah

ID No. 014201300152

A skripsi presented to the

Faculty of Business President University

In partial fulfillment of the requirements for

Bachelor Degree in Economics Major in Management

January 2017

PANEL OF EXAMINERS

APPROVAL SHEET

The panel of Examiners declare that skripsi entitled “THE DETERMINANT OF

MSME FINANCING DISTRIBUTION OF INDONESIA SYARIAH

BANKS” that was submitted by Ulfah Noor Hasanah majoring in Management

from Faculty of Business was assessed and approved to have passed the Oral

Examination on January 13th

2017.

___________________________

Chair – Panel of Examiners

___________________________

Examiner 1

___________________________

Examiner 2

ii

SKRIPSI ADVISER

RECOMMENDATION LETTER

The skripsi entitled “THE DETERMINANT OF MSME FINANCING

DISTRIBUTION OF INDONESIA SYARIAH BANKS” prepared and

submitted by Ulfah Noor Hasanah in partial fulfillment of the requirements for the

degree of Bachelor in the Faculty of Business has been reviewed and found to

have satisfied requirement for a skripsi fit to be examined. I therefore recommend

this skripsi for Oral Defense.

Cikarang, Indonesia, December 18th

2016

Acknowledged by, Recommended by,

Dr. Dra. Genoveva, M. M Dr. Drs. Chandra Setiawan, M. M., Ph. D

Head of Management Study Program Skripsi Adviser

iii

DECLARATION OF ORIGINALITY

I declared that this skripsi, entitled “THE DETERMINANT OF MSME

FINANCING DISTRIBUTION OF INDONESIA SYARIAH BANKS” is, to

the best of my knowledge and belief, and original piece of work that has not been

submitted, either in a whole part, to another university to achieve a degree.

Cikarang, Indonesia, January 18th

2017

Ulfah Noor Hasanah

iv

ABSTRACT

This study aimed to determine the factors that influence the MSME Financing

Distribution of Indonesia Syariah banks period 2011-2016. This research has

applied fixed effect model, used data taken from the bank’s financial reports.

Syariah banks have responsibility in creating social welfare and can be done

through the allocation of funding to MSME. The finding of this research, the

MSME Financing Distribution conducted Syariah bank are partially and

simultaneously affected simultaneously by the Inflation Rate, Gross Domestic

Product (GDP) Rate, Non-Performing Financing (NPF) and Return On Asset

(ROA). The variation of those four independent variables can explain 85.9% of

the variation influence MSME Financing Distribution variable.

Keywords: MSME Financing Distribution, Inflation Rate, Gross Domestic

Product (GDP) Rate, Non-Performing Financing (NPF) and

Return On Asset (ROA).

v

ACKNOWLEDGEMENT

Gratefulness to Allah SWT who never stops giving His mercy and bless to the

researcher. With the ease and help of Allah SWT, researcher can finished the

skripsi entitled "The Determinant of MSME Financing Distribution of

Indonesia Syariah Banks". In the preparation of this skripsi, the researcher

aware of the limitations, capabilities, and knowledge of the researcher in the

preparation. But the difficulties can be assisted by several parties. Therefore the

researcher would like to thank the various parties that have provided assistance in

the form of energy and mind. A big thank you goes to the honorable:

1. Researcher’s family, mom, dad, and sister who are always sending

prayers, as well as provide assistance both moral and material in the

preparation of this skripsi.

2. Mr. Chandra Setiawan, as the researcher’s advisor, who has provided

advice, help, and guidance during this process until finish this thesis.

Thank you for your time and thought has been given all this time.

3. Researcher’s lecturers and seniors, Mr. Purwanto, Mrs. Isfandayani, Mr.

Dece Kurniadi, and Kak Bagas Bhirawa who have provided very useful

feedback from skripsi proposal until the researcher was able to finish

writing this skripsi.

4. Shafira Yustianita, Shabrina Ulfah, Wike Andrianti, and Trianisa Zulianti,

extraordinary friends in the college life who always provides

encouragement. Thank you for all the suggestions and inspiration that has

been given.

5. Sara Amalia Sadendra, Izazi Nur Shabrina, and Lolita Nataya, researcher’s

best friend who is always listening to the story and the complaint, a friend

who is always waiting for her to come home.

6. Selly Magdalenna Sherwin, Onie Insany Kodratillah, Sophia Dwi Ratna,

Agatha Risdani, Jonathan Arifin, Zerlinda Thariah Ulfa, and Rahmi Zuha

Emdi, friends who has always been a place to share stories and dreams.

All Banking and Finance Management 2013 and friends from other major

vi

and batch who cannot be mentioned here one by one, thank you for the

kindness and the friendship in this journey.

7. Other parties which cannot be mentioned one by one, which has provided

assistance to the researcher, so that the skripsi can be done well.

The author is fully aware that this skripsi still have many shortcomings,

although the researcher has sought the best possible way. Therefore, the

researcher expects the criticism and constructive suggestions to improved.

The author hopes that this skripsi is useful and can expand the knowledge

for us all.

Regards,

Ulfah Noor Hasanah

vii

TABLE OF CONTENT

PANEL OF EXAMINERS ...................................................................................... i

SKRIPSI ADVISER ............................................................................................... ii

DECLARATION OF ORIGINALITY .................................................................. iii

ABSTRACT ........................................................................................................... iv

ACKNOWLEDGEMENT ...................................................................................... v

TABLE OF CONTENT ........................................................................................ vii

CHAPTER 1 - INTRODUCTION .......................................................................... 1

1.1. Background .............................................................................................. 1

1.1.1. Problem Identification ....................................................................... 4

1.1.2. Research Question ............................................................................. 4

1.1.3. Research Objectives .......................................................................... 5

1.2. Significance of Study ............................................................................... 5

1.3. Limitation ................................................................................................. 6

1.4. Research Outline ...................................................................................... 6

CHAPTER 2 - LITERATURE REVIEW ............................................................... 8

2.1. Introduction .............................................................................................. 8

2.2 Micro, Small and Medium Enterprises ..................................................... 8

2.3. Inflation Rate ............................................................................................ 9

2.4. Growth Domestic Product (GDP) Rate .................................................. 10

2.5. Non-Performing Financing (NPF) .......................................................... 10

2.6. Return on Assets (ROA) ......................................................................... 10

2.7. Previous Research .................................................................................. 11

2.8. Research Gaps ........................................................................................ 12

2.8.1. Inflation Rate ................................................................................... 12

2.8.2. Gross Domestic Product (GDP) Rate .............................................. 12

viii

2.8.3. Non-Performing Financing (NPF) .................................................. 13

CHAPTER 3 - METHODOLOGY ....................................................................... 14

3.1. Research Methods .................................................................................. 14

3.2. Theoretical Framework .......................................................................... 14

3.3. Hypotheses ............................................................................................. 15

3.4. Operational Definition of Variables ....................................................... 16

3.5. Instrument ............................................................................................... 17

3.6. Sampling ................................................................................................. 18

CHAPTER 4 - RESULT AND DISCUSSSION ................................................... 20

4.1. Descriptive Analysis ............................................................................... 20

4.2. Inferential Analysis ................................................................................ 21

4.2.1. Panel Data Regression..................................................................... 21

4.2.2. Classical Assumption Test .............................................................. 22

1. Normality Test ........................................................................................ 22

2. Autocorrelation Test ............................................................................... 23

3. Multicollinearity Test ............................................................................. 23

4. Heteroscedasticity Test ........................................................................... 24

4.2.3. Multiple Regression Analysis................................................................. 25

4.2.4. Hypotheses Testing ......................................................................... 27

4.2.4.1. Coefficient of Determination ........................................................ 27

4.2.4.2. F-test ............................................................................................. 27

4.4.3 T-test ................................................................................................ 28

4.3. Discussion .............................................................................................. 29

CHAPTER 5 - CONCLUSION ............................................................................ 32

5.1. Hypotheses Answer ................................................................................ 32

5.2. Recommendations .................................................................................. 33

REFFERENCE ...................................................................................................... 34

ix

APPENDICES........................................................................................................38

CHAPTER 1

INTRODUCTION

1.1. Background

Micro, Small and Medium Enterprises (MSME) have an important role and

strategic for the country's economic growth, both developing and developed

countries. The strategic position of the small business sector and the informal

sector has several advantages over large enterprises. MSME is very productive in

generating new employment and also can increase the amount of new business

units that support household income of the business. SMEs also tend to have more

flexibility when compared with large-capacity business (Citra, 2013). Various

strategic role owned by MSME sectors, but this sectors are also faced various

problems. Among other obstacles and problems are capital, business management

skills, and the quality of human resource managers. Obstacles and problems of

small and informal businesses were also due to the difficulty of access to

information and productive resources such as capital and technology, which

resulted in the limited ability of small businesses to thrive.

Attempts of progression and reinforcing of MSME today got critical thought from

the government. This is induced by the measure of basic and some key portion of

MSME in national monetary related progression. Other than a section in monetary

advancement and businesses, MSME also accepts a section in dispersing change

comes about. MSME sector has proved resilient, when the Economic Crisis of

1998, only the MSME sector that survived the collapse of the economy, while the

larger sector actually fell by crisis. It also happened when the second crisis in

2008, MSME proved resistant to the crisis and be able to survive because, first,

they do not have foreign debt. Secondly, not a lot of debt to banks because they

are considered unbankable. Third, the use of local inputs. Fourth, export-oriented

(Kuncoro, 2008).

2

Central Statistics Agency data shows, after the economic crisis of 1997-1998 the

number of MSME is not reduced, it increases steadily, even capable of reaching

51.3 million units, 90.9 million workers, and accounted for GDP amounted to

2,609 trillion IDR (55.6%). As well as providing foreign exchange contribution

amounted to 183.8 trillion (20%) (Bank Indonesia. 2015). The data proved,

MSME is a very potential market for the financial services industry, especially

banks to channel financing due to the fact, approximately 60-70% MSME do not

have access to bank financing.

Unfortunately, the huge potential of MSME are not in line with the Syariah bank

for the distribution of the MSME Financing Distribution. Can be seen from the

data below, the MSME Financing, Distribution of the three governments, banks,

Bank Negara Indonesia (BNI) Syariah, Bank Rakyat Indonesia (BRI) Syariah and

Bank Syariah Mandiri (BSM) experience the decreasing.

Figure 1.1 MSME Financing Distribution

Source: Financial Reports

According to Law No. 21 of 2008 concerning Syariah banking is anything that

concerns about the Syariah banks and Syariah business units, covering

institutional, business activities, as well as the manner and process of carrying out

its business activities. The regulation allows the development of Syariah banking

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BSM BNI Syariah BRI Syariah

3

very quickly. The growing number of branches of Syariah banks both from a

syariah bank and syariah division of conventional commercial banks (Karim,

2010).

Syariah banks ought to have the capacity to enable MSME for the most part

oversaw as per Syariah banks. In this way, the essential mission of Syariah banks

is to move the genuine area, particularly MSME, if the bank can do as such then

industry Syariah keeping money will decidedly affect the future improvement of

the national economy. Combined with the way that the most of the Indonesian

population is Muslim which reflects the state of the lion's share populace in the

MSME segment, properly Syariah keeping money framework could make a huge

commitment to the part. In addition, it is trusting the act of Syariah Managing an

account and its items are as per the business atmosphere in the MSME area.

Essential standards of theory for Syariah improvement bank in the advancement

of Small and Medium in the public eye is, help allowed without insurance or

underwriter, the objective gathering is a little group of poor underprivileged that

can possibly build up the business economy. Regarding loaning, Syariah banks

will offer need to the gatherings of individual who truly require the assets to

bolster its business achievement. In addition, help advances conceded by the

Syariah bank don't require any security or assurance individuals. Significantly,

Syariah bank's strategy is to keeping in mind that the end goal is to give monetary

help for little groups that identified with the administration of credit help itself

ought to be directed transparently and professionally with the rule of, by and for

the individuals. Furthermore, in the execution of the program, attempting to utilize

the gatherings that as of now exist in the group as a method for diverting credit

help.

Based on these descriptions, it is necessary to study how the influence of internal

and external variables, especially Inflation Rate, Growth Domestic Product (GDP)

Rate, Non-Performing Financing (NPF) and Return on Assets (ROA) to the

MSME Financing Distribution of Syariah bank. Therefore, the researcher takes

the title: The Determinant of MSME Financing Distribution of Indonesia

Syariah Banks.

4

1.1.1. Problem Identification

Funding and lending is the main activity of Syariah banks in providing facilities

for provision of funds to meet the needs of the community in need of funds

derived from the collection of funds that have been collected from people who

have excess funds. Because financing is the most important function of the use of

funds for Syariah banks, the bank needs to consider a variety of factors and what

aspects should be considered in making decisions on financing or distribution

problems there in the community (Siswati, 2013).

According to the background, the decreased of MSME Financing Distribution in

Indonesia Syariah Bank is regretting since how the MSME can survive from the

crisis that happened in Indonesia and got a small impact rather than the bigger

enterprises. The factors that implies some effects that divided by external factors

and internal factors should be reflect the MSME Financing Distribution.

1.1.2. Research Question

Based on the background described above, then the researcher formulate the

problem as follows:

1. Is there any significant partial influence of:

a. Inflation Rate towards MSME Financing Distribution in Indonesia

Syariah banks?

b. GDP Rate towards MSME Financing Distribution in Indonesia Syariah

banks?

c. NPF towards MSME Financing Distribution in Indonesia Syariah

banks?

d. ROA towards MSME Financing Distribution in Indonesia Syariah

banks?

2. Is there any simultaneous significant influence of Inflation Rate, GDP

Rate, NPF, and ROA toward the MSME Financing Distribution of

Indonesia Syariah Bank?

5

1.1.3. Research Objectives

The researcher has decided to conduct a research with the title “The Determinant

of MSME Financing Distribution of Indonesia Syariah Banks” The research

objectives of the study are:

1. To reach out whether it is significant partial influence of:

a. Inflation Rate towards MSME Financing Distribution in Indonesia

Syariah banks.

b. GDP Rate towards MSME Financing Distribution in Indonesia Syariah

banks.

c. NPF towards MSME Financing Distribution in Indonesia Syariah

banks.

d. ROA towards MSME Financing Distribution in Indonesia Syariah

banks.

2. To know whether there is any simultaneous significant influence of

Inflation Rate, GDP Rate, NPF, and ROA toward the MSME Financing

Distribution of Indonesia Syariah Bank.

1.2. Significance of Study

The research of "The Determinant of MSME Financing Distribution of Indonesia

Syariah Banks" could have a significant importance for the researcher itself to

provide a reference to conduct further research in the future. This research also

has importance to society as follows:

1. To represent a beneficial reference for knowledge for other studies.

2. For consideration in knowing the factors that inhibit and determine how to

maximize the MSME Financing Distribution.

3. This research could be used as a knowledge in terms of financing portfolio

and become a reference in the future.

6

1.3. Limitation

The limitation of this study is the big-three Syariah government bank based on

assets that published the financial report during the period of 2011-2016.

1.4. Research Outline

This research is presented with the contents that categorized into five chapters.

The chapters are:

CHAPTER I – INTRODUCTION

This chapter explains the background of this research. The decreasing of MSME

Financing Distribution growth of the Indonesia Syariah Bank has become an

attractive topic to be discussed since the importance of these matters that give

profitability for the government. This chapter is very essential in defining the

problems and finding out the main objectives in conducting this research.

CHAPTER II – LITERATURE REVIEW

This chapter is the collection of theories, concept, philosophies, and previous

researches which have been conducted related to the variables which will be

analyzed in this research. The theoretical review describes the definition of each

variable and the theory which supports this research.

CHAPTER III – METHODOLOGY

Methodology of research describes about the research method, hypotheses,

operational definition of the variables, instrument, and sampling method. The data

will be taken from the Syariah government bank. As this research is using

secondary data, the sources are retrieved from books, journals, websites, or case.

This chapter also includes a theoretical framework as the basis framework of this

research.

7

CHAPTER IV – RESULT AND DISCUSSION

This chapter outlines the main content of the research objectives by discussing

and analyzing the result of raw data and interpreting the result. The data will be

processed using Eviews 9.5 by undergoing several testing. This result will be

interpreted to prove the hypotheses and find out the influences of independent

variables toward dependent variable by having in-depth analysis.

CHAPTER V – CONCLUSION

This chapter will conclude all the analysis and interpretation in the previous

chapter. There will be also a recommendation from the result and advice to the

related parties regarding the results of research which has been accomplished

8

CHAPTER 2

LITERATURE REVIEW

2.1. Introduction

This chapter contains the literature review about a description of the theory,

findings and other research materials obtained from reference materials to be used

as a foundation of research activities to formulate a clear framework of the

formulation of the problem to be observed.

2.2 Micro, Small and Medium Enterprises

According to the Law on Micro, Small and Medium Enterprises of the Republic

of Indonesia number 20 of 2008 the definitions of MSME are:

Micro is a productive enterprise belonging to individuals and/or entities

individuals who meet criteria of micro enterprises as stipulated in this Act.

Small businesses are the economic efforts productively stand-alone, conducted by

an individual or business entity that is owned, controlled, or be a part either

directly or indirectly from medium or large businesses that meet the criteria of a

small business as defined in this Act.

Medium-sized businesses are productive economic activities that stand alone,

carried out by an individual or business entity that is not a subsidiary company or

not the brother of companies owned, controlled, or be a part either directly or

indirectly from a small business or large enterprise with total net assets or annual

sales revenue referred to in this Act.

Based on Law No. 20 of 2008 on SMEs, the criteria of micro, small and medium

enterprises are:

9

1. Criteria Micro:

a. Having a net worth of IDR 50,000,000,- (fifty million rupiah),

excluding land and buildings; or

b. It has an annual sales turnover of IDR 300.000.000,- (three hundred

million rupiah)

2. Criteria for Small Business:

a. Having a net worth of more than IDR 50,000,000,- (fifty million

rupiah) up to a maximum of IDR 500,000,000,- (five hundred million

rupiah) not including land and buildings; or

b. It has an annual sales revenue of more than IDR 300.000.000,- (three

hundred million rupiah) till the maximum of IDR 2.500.000.000,- (two

billion five hundred million rupiah).

3. Criteria Medium Enterprises:

a. Having a net worth of IDR 500,000,000,- (five hundred million rupiah)

up to at most IDR 10,000,000,000,- (ten billion rupiah), excluding land

and buildings; or

b. It has an annual sales revenue of more than IDR 2.500.000.000,- (two

billion five hundred million rupiah) to a maximum of IDR

50,000,000,000,- (fifty billion rupiah).

2.3. Inflation Rate

Inflation is a procedure of rising costs of merchandise all in all and continually

connected with market instruments that can be brought about by different

elements, similar to the ascending of utilization, overabundance liquidity in the

market that set off the utilization of even theory, to incorporate likewise because

of the absence of dispatch dispersion of products. It can likewise be said to

diminish the estimation of a coin is proceeding (Wibowo and Edhi Satrio, 2012).

In this study, the authors use the inflation data obtained from the official website

of the Otoritas Jasa Keuangan (OJK).

10

2.4. Growth Domestic Product (GDP) Rate

Gross Domestic Product (GDP) is utilized to gauge of all merchandise and

ventures created by a nation in a given period. Components in the GDP is income,

expenditure or investment, government spending, and the difference in export

import (Wong, 2011).

The extent of the Gross domestic product is impacted by numerous things,

including household consumption (C), investment (I), government spending (G)

and net exports (X-M) (Kusumawardhani et al, 2012). The GDP data that the

researcher use is obtained from the official website of the Otoritas Jasa Keuangan

(OJK).

2.5. Non-Performing Financing (NPF)

Non-Performing Financing (NPF) is an estimation of the proportion of business

risk that the bank demonstrating the greatness of the danger of credit or money

related issues that exist in a bank (Sulistianingrum, 2013). Financing problems

describe a situation where the approval of the financing repayment is at risk of

failure, and even tend to experience a potential loss. The greater the NPF ratio the

greater the financial risk borne by the bank. Vice versa, if the NPF is getting

smaller, the credit risk of the bank is also smaller. In this case, after the financing

is given, then the banks are required to monitor the use of funding and compliance

capabilities and customer to meet its obligations (Sari, 2013).

NPF can be searched by the formula:

(Eq. 2.1)

2.6. Return on Assets (ROA)

Return on Assets (ROA) is the proportion used to quantify the bank's capacity to

make a benefit. This proportion shows administration's capacity to expand

benefits or benefits at the level of salary, resources, and certain share capital and

in addition to evaluate the capacity of administration to control costs.

11

In other words, it can describe the bank's productivity. The more prominent the

estimation of this proportion demonstrates the level of benefit of bank business is

improving or more advantageous. The stable of ROA proportion reflects the

security of the measure of capital and bank benefits. Stable keeping monetary

conditions will upgrade the capacity of banks in augmenting credit

(Meydianawati, 2007).

The ROA formula:

(Eq. 2.2)

2.7. Previous Research

1. Annisa Nurlestari in her research by the title "Analysis of Factors

Affecting MSME Lending (Study on Commercial Banks Listed in

Indonesia Stock Exchange 2009-2013).” The result of this research was

the CAR variable influence negatively and significantly to the distribution

of MSME credit, ROA variable influence negatively and significantly to

the distribution of MSME credit and DPK and NPL variable influence

positively and not significant to the distribution of MSME credit. Interest

spreads negatively influence variable and not significant to the distribution

of MSME credit.

2. Sulis Estiyani in her research by the title "Factors Affecting Financing

Small and Medium Enterprises In Syariah Banks and Syariah Business

Unit in Indonesia”. The result of the research showed that Third Party

Fund and Inflation simultaneously effect on MSME financing and partial

effect on Third Party Fund, NPF, and Inflation has a significant effect on

the financing of MSME sector in Indonesia

3. Wuri Arianti and Harjum Muharam in their research, “Analysis of the

Influence of Third Party Funds, Capital Adequacy Ratio, Net Performing

Financing and Return On Assets Of Financing In Syariah Banking (Case

Study at Bank Muamalat Indonesia Period 2001-2011)” From the results

12

of the analysis show that only DPK has positive significant influence to

financing, while CAR, NPF, and ROA have not influence to financing.

Stimulatingly the DPK, CAR, NPF, and ROA have significances influence

to financing.

4. Nurhidayah and Any Isvandiari in their research, “Internal Factors and

External Factors that Affecting the Allocation of Small and Medium

Enterprises Financing (Study of Indonesia Syariah bank).” From the

research found that the financing of MSME conducted Syariah bank is not

affected by the Inflation factor, Gross Domestic Product (GDP), the

margin for the result but it is only influenced and Financial Deposit to

Ratio (FDR).

5. Jamilah in her research, “Factors that Affecting the Mudharabah Financing

in Indonesia Syariah Banks” Based on the result of multiple regressions, it

shows that Third Party Funds (DPK) and capital adequacy ratio has

positive influence to the mudharabah financing, while Return on Assets

and Non-Performing Financing and Operating cost to the Operating

Revenue (OER) has negative influence to the mudharabah financing

2.8. Research Gaps

2.8.1. Inflation Rate

The result of Sulis Estiyani (2016), Harera Angga Kusuma (2014), and Sunaryati

and Yazid Afandi (2013) with the results of the Inflation Rate that has a positive

effect on MSME Financing Distribution, while Hafidh Wahyu Purnomo and

Arief Lukman Santoso (2013) and Nurhidayah and Any Isvandiari (2016)

researched, found that the inflation rate has insignificant effect towards MSME

Financing Distribution.

2.8.2. Gross Domestic Product (GDP) Rate

According to research Nurhidayah and Any Isvandiari (2016), GDP Rate has

positive significant effect, but Hafidh Wahyu Purnomo and Arief Lukman

Santoso (2013) point out that the GDP rate has no significant relationship towards

MSME Financing Distribution.

13

2.8.3. Non-Performing Financing (NPF)

Based on Siti Nugraha (2014), Sulis Estiyani (2016), and Sunarsih and Slamet

Hilmi (2013)result, they got negative significant influence, whie for Yuwono and

Meiranto (2012) and Supiatno (2012), they stated that the NPF has no significant

effect towards MSME Financing Distribution.

2.8.4. Return on Assets (ROA)

Siti Nugraha (2014), Sulis Estiyani (2016), and Jamilah and Wahidahwati (2016)

resulted that ROA has negative significant influence, but Yuwono and Meiranto

(2012) and Wuri Arianti and Harjum Muharam (2011) stated that ROA has

insignificant relationship toward MSME Financing Distribution.

14

CHAPTER 3

METHODOLOGY

3.1. Research Methods

This research is in the form of an explanation regarding the relationship between

one variable with other variables through hypotheses testing. This research was

conducted using secondary data, which is the financial reports that published

quarterly, issued by Syariah Bank period 2011 to 2016 from three Syariah Bank

government; Bank Syariah Mandiri (BSM), Bank Negara Indonesia (BNI) Syariah

and Bank Rakyat Indonesia (BRI) Syariah.

This research will be conducted by adopting a quantitative approach to analyzing

determinant factors of MSME Financing Distribution in Indonesia Syariah Bank.

The quantitative method is selected because this research will focus on calculating

by inputting data to find out the result and conclude from the statistical result.

This research emphasize on measuring variables and testing hypotheses to find the

effect of independent variables towards dependent variable.

3.2. Theoretical Framework

Based on the variables mentioned, the variable which influences the MSME

Financing Distribution of Indonesia Syariah Bank can be described as follows,

15

Figure 2.1 Theoretical Framework

Source: Adjusted by Researcher

Based on figure 2.1 on the theoretical framework, this study consists of four

independent variables, Inflation Rate, GDP Rate, NPF, and ROA toward the

dependent variable, MSME Financing Distribution.

3.3. Hypotheses

Testing the hypotheses would be conducted by regression using panel data. These

analyses are used to examine the effect of Inflation Rate, GDP Rate, NPF, and

ROA towards MSME Financing Distribution. Hypotheses from this research as

follows:

Ho1: There is no significant effect of Inflation Rate toward the MSME Financing

Distribution.

Ha1: There is a significant effect of Inflation Rate toward the MSME Financing

Distribution.

Ho2: There is no significant effect of GDP Rate toward the MSME Financing

Distribution.

Inflation Rate [X1]

GDP Rate [X2]

NPF [X3]

MSME Financing

Distribution [Y]

ROA [X4]

16

Ha2: There is a significant effect of GDP Rate toward the MSME Financing

Distribution.

Ho3: There is no significant effect of NPF toward the MSME Financing

Distribution.

Ha3: There is a significant effect of NPF toward the MSME Financing

Distribution.

Ho4: There is no significant effect of ROA toward the MSME Financing

Distribution.

Ha4: There is a significant effect of ROA toward the MSME Financing

Distribution.

3.4. Operational Definition of Variables

Table 3.1 Operational Definition of Variables

Variable Operational

Definition Formula Scale

MSME

Financing

Distributi

on (Y)

Credits allocated

for the

development of

MSME that are

economically

feasible, but have

not bankable

Logarithm of the amount of MSME

financing of commercial banks at each

quarter of the year

Ratio

Inflation

Rate (X1)

Influenced by the

rising of

consumption,

excess liquidity in

the market

Ratio

17

GDP Rate

(X2)

The total of

consumption (C),

investment (I),

government

spending (G) and

net exports (X-M)

GDP = Consumption + Investment +

Government Spending + (Exports -

Imports)

Ratio

NPF (X3)

The amount

classified into

loans that have

the doubtful and

loss to total loan

collectibility

Ratio

ROA (X4)

Comparison of

net profit after tax

to total assets

Ratio

Source: Adjusted by Reseacher (2016, Nurlestari (2013), and Nurhidayah and Isvandiari (2016)

3.5. Instrument

This research will gather data from the bank quarter financial reports, OJK quarter

reports, journals, and websites related, therefore this research will only use those

for analysis. The main tool used is Econometric Views (Eviews) 9.5. Eviews

helps the researcher to make scientific and reliable research (Schwert, 2010). This

data use reviews to process the raw data statistically in order to get the result to be

interpreted in this research, such as normality, heteroscedasticity, autocorellation,

multicolleniarity, and multiple regressions.

Microsoft Excel 2007 also used to process the raw data obtained, as well as

Microsoft Word 2007 which used to compose the research.

18

3.6. Sampling

Sampling Testing is the way toward selecting a proper number of the correct

subjects from the populace as the agents of the examination (Sekaran and Bougie,

2011). If a sampling is done correctly, the result of statistical analysis can be used

to conclude a whole population. The population of this study was taken from the

data bank financial report with data samples per quarter period January 2011 to

June 2016. The population used in this study are all Syariah banking in Indonesia

during the period 2011- 2016 with a total of 12 banks. The samples in this study

were done by using purposive sampling. Purposive sampling technique is done by

selecting a sample in accordance with the criteria that have been set. Criteria in

sampling with purposive sampling technique in this research are to consider

Syariah banking is included in government bank during the period 2011-2016, the

government of Syariah banking are included in the category of large 3, and the

bank providing complete financial ratios in accordance with the variables to be

observed during the period of the study, which was in 2011-2016. Based on these

criteria, then of a total population of 12 Syariah banks, used the total sample of

three banks, namely, PT. Bank Syariah Mandiri Tbk., PT. Bank Negara Indonesia

Syariah Tbk., PT. Bank Rakyat Indonesia Tbk Syariah. The method used in this

research is multiple regression analysis. Multiple regression analysis was utilized

to foresee substantial factors (dependent variable) utilizing information from at

least two free factors (independent variable) were already known to the magnitude

(Santoso, 2010). This quantitative data analysis begins with collecting data and

then processed using Eviews. Eviews is a computer application that can analyze

data and perform statistical calculations to test normality, heteroscedasticity,

autocorellation, multicolleniarity, and multiple regression. The data used in this

research is time series data with the quarterly period. Multiple linear regression

equation is as follows:

Y = 0 + 1X1 + 2X2 + 3X3 + 4X4 + ε

Where:

Y = MSME Financing Distribution

(Eq.1)

19

0 = Intercept/constant

1-4 = Partial regression coefficient

X1 = Inflation Rate

X2 = GDP Rate

X3 = NPF

X4 = ROA

ε = Random Error

20

CHAPTER 4

RESULT AND DISCUSSSION

4.1. Descriptive Analysis

Descriptive statistic provides general information for each variable which includes

the minimum value, maximum value, mean value and standard deviation value

can be seen in Table 4.1 below:

Table 4.1 Descriptive Statistic

MSME

Inflation

Rate GDP Rate NPF ROA

Mean 39.03939 5.820455 5.644545 3.688636 1.195303

Median 30.81000 5.165000 5.745000 3.255000 1.215000

Maximum 77.72000 10.60000 6.500000 6.890000 3.420000

Minimum 16.71000 3.370000 4.670000 1.860000 0.030000

Std. Dev 19.80334 1.978215 0.672035 1.456410 0.713799

Observation 66 66 66 66 66

Source: Eviews 9.5

Based on Table 4.1 it can be seen that the amount of data that examined as many

as 66 observations with Micro, Small Medium Enterprises (MSME) Financing as

the dependent variable, which has a minimum value of 16.71, a maximum value

of 77.72, standard deviation of 19.8 and an average value of 39.04. The greater of

average value than the standard deviation value indicates that the data is spread

out well.

Inflation Rate has a minimum value of 3.37, maximum value of 10.6, standard

deviation of 1.98 and an average value of 5.82. The greater of average value than

the standard deviation value indicates that the data is spread out well.

21

Growth Domestic Product (GDP) Rate has a minimum value of 4.67, the

maximum value of 6.5, the value of a standard deviation of 0.67 and an average

value of 5.64. The greater of average value than the standard deviation value

indicates that the data is spread out well.

Non-Performing Financing (NPF) has a minimum value of 1.86, the maximum

value of 6.89, and the average value obtained is equal to 3.69, while the standard

deviation of 1.45. With a standard deviation appear smaller than the mean and the

mean value of the standard NPF variable on the data spread well NPF.

Return on Assets (ROA) has a minimum value of 0.3, the maximum value of 3.42,

and the average value obtained is at 1.20, while the standard deviation of 0.71.

The greater of average value than the standard deviation value indicates that the

data is spread out well.

4.2. Inferential Analysis

There are three basic approaches used in estimating the regression model with

panel data which are common effect, fixed effect, and random effect. In order to

choose the right approach to be used, the model should pass the Chow test.

4.2.1. Panel Data Regression

1. Chow Test (Likelihood Ratio)

Chow test is used to select whether the approach used is common or fixed

effect.

Table 4.2 Chow Test

Redundant Fixed Effects Test

Equation: Untitled

Test cross-section fixed effects

Effect Test Statistic d.f. Prob

Cross-section F 114.510908 (2.59) 0.0000

Cross-section Chi-Square 104.642919 2 0.0000

Source: Eviews 9.5

22

Based on the table 4.2 the probability value of Chow test is significant at

0.000, less than 0.05, it is mean that the fixed model is preferable for this

research.

4.2.2. Classical Assumption Test

In this study, researcher conducted tests on some classical assumptions used

because using a secondary data. The classical assumption is Normality Test,

Multicolinearity Test, Heteroscedasticity Test, and Autocorrelation Test.

1. Normality Test

The normality test aims to determine whether the regression model, or

residual confounding variables have a normal distribution of data

(Ghozali, 2011)

Table 4.3 Normality Test

Series: Standardized Residuals

Sample 2011Q1 2016Q2

Observation 66

Mean 2.66ee-16

Median -0.011330

Maximum 16.24851

Minimum -20.10671

Std. Dev 7.062086

Skewness -0.422466

Kurtosis 3.161797

Jarque-Bera 2.035241

Probability 0.361454

Source: Eviews 9.5

23

Based on Table 4.3, Normality Test showed that the regression model

already has a residual value that is normally distributed. This is indicated

by probability value is above 0.05, which is 0.36. The amount of data

which produces the residual value that is normally distributed as many as

66 samples. Thus the regression model to meet the assumption of

normality.

2. Autocorrelation Test

This test aims to test whether there is a correlation between an error bullies

period t to the previous period (t-1) in a linear regression model that was

used. If there is a correlation, then there is a problem of autocorrelation.

The autocorrelation problem should not happen if you want to get a good

regression model (Ghozali, 2011). How to detect the presence or absence

of autocorrelation is using the Durbin-Watson test (D-W test). The result

of the Durbin-Watson test calculation that there will be compared with the

value of the Durbin-Watson tables. For this study, the test results

autocorrelation can be seen in table 4.4 below:

Table 4.4 Durbin-Watson Test

Weighted Statistics

Durbin-Watson stat 0.738027

Source: Eviews 9.5

Durbin Watson test calculation results obtained amounted to 0.74 which

lies between -2 and +2. From the calculation above, it can be stated that

the study is free from the autocorrelation problem.

3. Multicollinearity Test

This test aims to examine the correlation between the independent

variables in the regression model. The lack of correlation between the

independent variables indicates the good regression model.

24

Table 4.5 Multicollinearity Test

Inflation_Rate GDP_Rate NPF ROA

Inflation_Rate 1.000000 0.061479 -0.029390 0.111278

GDP_Rate 0.061479 1.000000 -0.459582 0.447093

NPF -0.029390 -0.459582 1.000000 -0.398747

ROA 0.111278 0.447093 -0.398747 1.000000

Source: Eviews 9.5

From Table 4.5 above, it can be seen that each independent variables have

the result of less than 0.7. The highest yield was between GDP Rate and

the NPF, which only reached 0.46. So it can be ascertained that the study

is free from multicollinearity problems.

4. Heteroscedasticity Test

This test aims to see whether there is inequality residual variance from one

observation to another observation. A good regression model and the

expected data are having a similar variance or called homoscedasticity

(Ghozali, 2011).

Table 4.6 Heteroscedasticity Test

Dependent Variable: MSME

Method: Panel Least Squares

Date: 11/25/16 Time 22:10

Sample: 2011Q1 2016Q2

Periods included: 22

Cross-section included: 3

Total panel (balanced) observation: 66

White cross-section standard errors & covariance (d.f. corrected)

Source: Eviews 9.5

25

From the table 4.6 above, it shows that “White cross-section standard

errors & covariance (d.f. corrected)” appears in the result, because the

researcher choose to tick the “white test” in the Eviews software to solve

the heteroscedastisity problem that may exist in this research

4.2.3. Multiple Regression Analysis

Multiple linear regression analysis is an analytical tool to determine and analyze

how much influence an independent variables on the dependent variable. In this

research, the dependent variable used is the MSME Financing Distribution while

the independent variables are Inflation Rate, GDP Rate, NPF, and ROA

Table 4.7 Multiple Regression Analysis

Dependent Variable: MSME

Method: Panel Least Squares

Date: 11/25/16 Time 22:10

Sample: 2011Q1 2016Q2

Periods included: 22

Cross-section included: 3

Total panel (balanced) observation: 66

White cross-section standard errors & covariance (d.f. corrected)

Variable Coefficient Std. Error T-statistic Prob

Inflation_Rate 1.090954 0.306114 3.563887 0.0007

GDP_Rate 4.564523 1.018670 4.480867 0.0000

NPF -3.728057 0.605234 -6.159695 0.0000

ROA -6.893221 1.986048 -3.470823 0.0010

C 28.91582 5.758504 5.021413 0.0000

Source: Eviews 9.5

Based on the output data from Table 4.7 it can be arranged multiple linear

regression equation as follows:

Y = 28.91582 + 1.09095*INFLATION_RATE + 4.56452*GDP RATE –

3.72805*NPF– 6.89322*ROA

(Eq. 2)

26

From the regression equation above, we can interpret as follows:

1. The value of the above equation is constant at 28.92 which means that

MSME Financing Distribution will be worth of 28.92 if variables such as

Inflation Rate, GDP Rate, NPF and ROA were not there.

2. Inflation variable has a positive regression coefficient that is equal to 1.09.

Positive coefficient values indicate that the number of Inflation has a

positive effect toward the MSME Financing Distribution. This illustrates

that if an increasing rate of Inflation as much as one percent, it will cause

the increase in the value of MSME of 1.09 percent, assuming the other

independent variables are constant.

3. The GDP Rate variable has a positive regression coefficient that is equal to

4.56. Positive coefficient values indicate that the GDP Rate have a positive

effect toward the MSME Financing Distribution. This illustrates that if

GDP Rate is increased by one percent, it will cause the increase in the

value of MSME at 4.56 percent, assuming the other independent variables

are constant.

4. NPF variable has a negative regression coefficient that is equal to -3.73.

This negative coefficient value indicates that the NPF influence on the

number of MSME Financing Distribution is negative. This illustrates that

if there is an increase NPF value as much as one percent will lower the

value of MSME amounted to 3.73 percent, assuming the other independent

variables are constant.

5. ROA variable has a negative regression coefficient that is equal to -6.89.

Negative coefficient values indicate that ROA negative effect on MSME

Financing Distribution. This illustrates that if there is an increase ROA

value as much as one percent, it will cause the value of MSME Financing

Distribution will lower the value MSME Financing Distribution to 6.89

percent, assuming that the other independent variables are constant.

27

4.2.4. Hypotheses Testing

4.2.4.1. Coefficient of Determination

Based on these results, it can be shown that the determinant coefficient value (R2)

Table 4.8 Coefficient of Determination

R-squared 0.872829

Adjusted R-squared 0.859896

S.E. of regression 7.412482

Source: Eviews 9.5

According to Table 4.8, the magnitude of an Adjusted R Square is 0.859 this

means that 85.9% of the variation MSME Financing Distribution can be explained

by the variation of the four independent variables, Inflation Rate, GDP Rate, NPF,

and ROA while the remaining 14.1% is explained by other variables outside

research.

4.2.4.2. F-test

The statistical test F (simultaneously) is used to test whether all the independent

variables are the Inflation Rate, GDP Rate, NPF, and ROA were included in the

model have influence together with the dependent variable, MSME Financing

Distribution.

Table 4.9 F-test

Sum squared resid 3241.748

Log likelihood -222.1590

F-statistic 67.49023

Prob(F-statistic) 0.000000

Source: Eviews 9.5

According to Table 4.9 obtained calculated F value of 67.49 with probability of

0.0000. Since the probability is less than 0.05, then the independent variables such

as Inflation Rate, GDP Rate, NPF, and ROA simultaneously affect the MSME

Financing Distribution

28

4.4.3 T-test

Based on Eviews 9.5 outputs, partial influence of four independent variables,

namely Inflation Rate, GDP Rate, NPF, and ROA as shown in Table 4.7. Based

on the test results of T-test, then the result is as follows:

1. T-tests on Inflation Rate.

The first hypothesis states that the Inflation rate positive effect on the

MSME Financing Distribution. Based on the calculation on the result that

the probability of Inflation rate at 0.0007 to the T-statistic value at 3.56.

The probability that lower than 0.05 means that the Inflation rate has a

significant effect partially to MSME Financing Distribution. The positive

result in the T-statistic, the which is 3.56 means that the Inflation rate has

positive effect and significant to MSME Financing Distribution.

Therefore, the Ha1 is accepted (Ho1 rejected).

2. T-tests on Gross Domestic Product (GDP) Rate.

The second hypothesis states that GDP Rate has a positive effect on the

MSME Financing Distribution. Based on the calculation on the result that

GDP Rate variable amounted to probability 0.0000 with a value of T-

statistic of 4.48. The probability is lower than 0.05 means that GDP Rate

has a significant effect partially to MSME Financing Distribution. The

positive result in the T-statistic, the which is 4.48 means that GDP Rate

has positive and significant effect to MSME. Therefore, the Ha2 is

accepted (Ho2 rejected).

3. T-tests on the Net Performing Finance (NPF).

The third hypothesis states that NPF has a negative effect on the MSME

Financing Distribution. Based on the calculation on the result that the

probability of 0.0000 with the NPF variable T-statistic value of -6.16. The

probability which is lower than 0.05 means that NPF has a significant

effect partially to MSME Financing Distribution. The negative result in

the T-statistic, which is -6.16 means that NPF has a negative effect and

29

significant to MSME Financing Distribution. Therefore, the Ha3 is

accepted (Ho3 rejected).

4. T-test of Return On Asset (ROA).

The fourth hypothesis says that ROA negative effect on the MSME

Financing Distribution. Based on the calculation on the result that the

probability of ROA amounted to 0.0010 by T-statistic value of -3.47. The

probability is lower than 0:05 means that the ROA has a significant effect

partially to MSME. The negative result in the T-statistic, which is -3.47

means that ROA has a negative effect and significant to MSME Financing

Distribution. Therefore, the Ha4 is accepted (Ho4 rejected).

4.3. Discussion

1. Inflation Rate has a positive effect on MSME Financing Distribution, Ha1

proven. These results support the results of the study of Sulis Estiyani

(2016), Harera Angga Kusuma (2014), and Sunaryati and Yazid Afandi

(2013) with the results of the Inflation Rate that has a positive effect on

MSME Financing Distribution, and the researcher could not found the the

negative effect result. The greater the level of Inflation rate that occurred

in Indonesia happened, the higher rate of the MSME finance portfolio.

That is because the rate of inflation is very influential in the economy,

particularly in the banking activities. Conditions of high inflation led the

Bank Indonesia issued regulations to raise interest rates on deposits of

banks in Indonesia. This is in order so that inflation can be controlled.

However, because of that, banks are forced to raise interest rates on loans.

2. GDP Rate has a positive effect on MSME Financing Distribution, Ha2

proven. These results support the results of research Nurhidayah and Any

Isvandiari (2016), but the results are different from the results of Harera

Angga Kusuma (2014) research with the result that GDP Rate has a

negative effect. The greater the level of this independent variable that

occurred in Indonesia led to the high rate of the MSME finance portfolio.

30

That is because the economic condition of a country has a positive

relationship with the bank's ability to absorb and distribute public funds.

This indicates that the economic instability of a country which is reflected

in the GDP Rate will lead to goods and services produced within the

community to grow and increase the prosperity of society. The increase of

economic activity will encourage increase public revenue, an increase of

those revenues will promote the ability of people to invest which means

the need for funding to be increased including making requests toward the

MSME Financing Distribution.

3. NPF negatively affects MSME Financing Distribution, Ha3 proven. These

results support the results Siti Nugraha (2014), Sulis Estiyani (2016), and

Sunarsih and Slamet Hilmi (2013) but the results are different from the

results of the research of Annisa Nurlestari (2015) and Nana Nofianti et.

all (2015). Financial problems are inversely proportional to the

distribution of funding, whereby the NPF reflects the level of cost control

and policy or credit, which is run by the bank, so the lower the NPF, the

higher the amount of financing that will be distributed by banks. The

higher the NPF shows the lack of ability of banks to collect the credit

issuance. The less money loan back to the bank, it will cause the bank

funds available for distribution on the wane. As a result, the bank will

reduce the amount of funds that will be distributed to the public.

4. ROA negatively affect MSME Financing Distribution, Ha4 proven. These

results support the results of the study Siti Nugraha (2014), Sulis Estiyani

(2016), and Jamilah and Wahidahwati (2016), but these results differ from

the results of the research of Annisa Nurlestari (2015), and Dwi Fitriani

(2012). The results of this study are not in accordance with the theory that

the higher the ROA then cause the value of financing to be increased. It

means when the ROA is higher, it is because of the lower of MSME

Financing Distribution is channeled by a bank, and when the distribution

of MSME Financing Distribution increased, it will lowered the ROA level.

The difference is probably due to the high value of NPF in three banks that

are used as sample. More and more credit problems reflected in the NPF

ratio indicates the lack of ability of banks to raise funds disbursed. The

31

less money back to the bank financing will lead to bank funds available for

distribution decreasing.

Besides that, It also because of the inconsistency between the increase or

decrease in ROA on the amount of financing on each period. As happened

in Bank Syariah Mandiri’s ROA in 2014 second quarter two were

increased from by 11.49% from the quarter before, but the MSME

Financing Distribution has decreased up to 77.27%, the opposite happened

in Bank Rakyat Indonesia Syariah in 2012 second quarter that declined

ROA of 1.05% from the first quarter followed by a rise in MSME

Financing Distribution by 85% as well as Bank Negara Indonesia Syariah

in 2015 fourth quarter that declined ROA amounted to 4.97% from the

previous quarter, an increase of 7.69% MSME Financing Distribution

happened.

There is also the difference between the financing of banks used as

sample. BSM used their fund more to the consumer banking financing,

BNIS financing more to the retail business, while BRIS is the one who

used their fund mostly in micro financing.

32

CHAPTER 5

CONCLUSION

5.1. Hypotheses Answer

Based on test results and the discussion of the effect of independent variables in

the form of the Inflation Rate, Gross Domestic Product (GDP) rate, Non-

Performing Financing (NPF), and Return on Assets (ROA) towards Micro, Small

Medium Enterprises (MSME) Financing of Indonesia Syariah Government Bank,

the conclusions about the research, are:

1. Regarding the T-test result, it shows that several independent variables

reveal significant influence partially to dependent variable as follow:

a. In the first hypothesis testing, Inflation Rate has a positive and

significant effect toward MSME Financing Distribution. The rate of

inflation is very influential in the economy, particularly in the banking

activities. Conditions of high inflation led Bank Indonesia issued

regulations to raise interest rates on deposits of banks, in order to make

inflation controlled. However, because of that, banks are forced to

raise interest rates on loans.

b. In the second hypothesis testing, GDP Rate has a positive and

significant influence on MSME Financing Distribution. That is

because The economic condition of a country has a positive

relationship with the bank's ability to absorb and distribute public

funds. The increased economic activity will encourage the increased of

public revenue, and then will promote the ability of people to invest,

therefore the need for funding will be increased including the MSME

Financing Distribution.

c. In the third hypothesis testing, it is known that the NPF has a

significant negative effect on the distribution of the MSME Financing

Distribution. The high NPF lead to reluctance of banks to extend

33

credit because they have to use a lot of bank reserves so the financing

tends to be low.

d. In the fourth hypothesis testing, it is known that the ROA has a

significant negative effect on MSME Financing Distribution. The

possibility reason because the only experience bank on MSME

Financing Distribution among the sample is BRIS. The other two

banks; BSM more focus on consumer banking financing and BNIS

more focus in the retail business financing.

2. According to F-statistic test result, Inflation Rate, GDP Rate, NPF, and

ROA have significant value of 0.000. It means that the independent

variables are simultaneously significant influence the dependent variable.

5.2. Recommendations

Future studies should analyze the factors affecting the financing portfolio

that is more diverse, because based on previous research referenced in this

research, the type of variables used are still very limited. There should be

more external factors such as the macroeconomic factors, so that the result

can explain the variation of dependent variable higher than this research

finding.

34

REFFERENCE

Books

Ghozali, I. (2011). Ekonometrika Teori, Konsep, dan Aplikasi dengan SPSS 17.

Semarang: Badan Penerbit Undip

Karim, A. (2010). Bank Islam: Analisis Fiqih dan Keuangan. Jakarta:

RajaGrafindo Persada.

Kasmir. (2012). Analisis Laporan Keuangan. Jakarta: Raja Grafindo Persada

Santoso, S. (2010). Statistik Non Parametrik Konsep dan Aplikasi dengan SPSS.

Jakarta: Elex Media Komputindo

Sekaran, U., & Bougie, R (2011). Research Methods for Business. Great Britain:

Wiley

Journals / Articles / Reports

Arianti, W. N.P. & Harjun M. (2011). Analisis Pengaruh Dana Pihak Ketiga

(DPK), Capital Adequecy Ratio (CAR), Non Performing Financing (NPF) dan

Return On Asset (ROA) terhadap pembiayaan pada Perbankan Syariah.

Semarang: Universitas Diponegoro

Jamilah & Wahidahwati. (2016). Faktor-Faktor yang Mempengaruhi Pembiayaan

Mudharabah Pada Bank Umum Syariah. Surabaya: Sekolah Tinggi Ilmu

Ekonomi Indonesia

Kusumawardhani, Ni Made S., I Gusti Ayu Made S., & Made S. (2012). Faktor-

faktor yang Memengaruhi PDB Indonesia dengan Persamaan Simultan 2SLS. E-

Jurnal Matematika

Meydianawati, L. G. (2007). Analisis Perilaku Penawaran Kredit Perbankan

kepada Sektor UMKM di Indonesia (2002-2006). Buletin Studi Ekonomi

35

Nofianti, N., Tenny B.,& Aditiya E. (2015). Analisis Pengaruh ROA, BOPO,

Suku Bunga, DR, dan NPF Terhadap Tingkat Bagi Hasil Deposito Mudharabah.

Serang: Universitas Sultan Ageng Tirtayasa

Nurhidayah & Isvandiari A. (2016). Faktor Internal dan Faktor Eksternal yang

Mempengaruhi Alokasi Pembiayaan Usaha Kecil Menengah. Malang: Sekolah

Tinggi Ilmu Ekonomi Asia

Purnomo, H. W. & Arief, L. S. (2013). Analisis Faktor-Fkator yang

Mempengaruhi Pembiayaan BerbasisMargin pada Bank umum Syariah di

Indonesia. Surakarta: Universitas Sebelas Maret.

Siswati. (2013). Analisis Penyaluran Dana Bank Syariah. Lampung: Jurnal

Dinamika Manajemen

Sunaryati & Yazid A. (2013) in Fahmi, M. S. Pengaruh CAR, NPF, BOPO, dan

FDR terhadap Profitabilitas Bank Umum Syariah .Yogyakarta: Universitas Islam

Negeri Yogyakarta

Supriyanto. (2006). Pemberdayaan Usaha Mikro, Kecil, dan Menengah (UMKM)

sebagai Salah Satu Penanggulangan Kemiskinan. Yogyakarta: Universitas Negeri

Yogyakarta

Yuwono, F. A. (2012). Analisis Pengaruh Dana Phak Ketiga, Loan to Deposit

Ratio, Capital Adequacy Ratio, Non Performing Loan, Return On Assets dan

Sertifikat Bank Indonesia terhadap Jumlah Penyaluran Kredit. Semarang:

Universitas Indonesia

Thesis

Amithya, Y. F. (2012). Analisis Pengaruh Dana Pihak Ketiga, Loan Deposit

Ratio, Non Performing Loan, Return On Asset, Dan Sertifikat Bank Indonesia

terhadap Jumlah Penyaluran Kredit. Semarang: Universitas Diponegoro

36

Estiyani, S. (2016). Faktor-Faktor yang Mempengaruhi P embiayaan Usaha Kecil

dan Menengah (UKM) pada Bank Umm Syariah dan Unit Usaha Syariah di

Indonesia. Yogyakarta: Universitas Islam Negeri Sunan Kalijaga

Fitriani, D. (2012). Pengaruh Rasio Keuangan Bank terhadap Penyaluran Kredit

Modal Kerja. Padang: Universitas Andalas

Kusuma, H. A. (2014). Analisis Faktor-Faktor yang Mempengaruhi Penyaluran

Kredit Usaha Rakyat (KUR). Semarang: Universitas Diponegoro

Nugraha, S. (2014). Pengaruh ROA, NPF, FDR, BOPO, dan Tingkat Bagi Hasil

terhadap Pembiayaan Mudharabah. Jakarta: Universitas Islam Negeri Syarif

Hidayatullah.

Nurlestari, A. (2015). Analisis Faktor-Faktor yang Mempengaruhi Penyaluran

Kredit UMKM. Semarang: Universitas Diponegoro

Sari, D. W. (2013). Pengaruh Pembiayaan Jual Beli, Pembiayaan Bagi Hasil,

Financing to Deposit Ratio, dan Non Performing Financing terhadap

Profitabilitas. Semarang: Universitas Diponegoro

Sulistianingrum, D. W. (2013). Analisis Pengaruh Financing to Deposit Ratio

(FDR), Dana Pihak Ketiga (DPK), Sertifikat Bank Indonesia Syariah (SBIS), dan

Non Performing Financing (NPF) terhadap Return On Asset (ROA). Jakarta:

Universitas Islam Negeri Syarif Hidayatullah. Jakarta.

Wibowo, Edhi Satrio. (2012). Analisis Pengaruh Suku Bunga, Inflasi, CAR,

BOPO, NPF terhadap Profitabilitas Bank Syariah. Semarang. Universitas

Diponegoro Semarang.

Websites / Electronic Sources

Schwert,G. W. (2010). Descriptive Statistic & Test. Retrieved December 2016,

from Eviews 7 User’s Guide I: http://schwert.ssb.rochester.edu/a425/EV71.pdf

Supiatno, B. R. , Satriawan S, Desmiawati. (2014). Pengaruh NPL, CAR Dan

Tingkat Suku Bunga Terhadap Penyaluran Kredit Perusahaan Perbankan Yang

Terdaftar di Bursa Efek Indonesia Pada Tahun 2009 – 2011 . Portalgaruda.org

37

www.bi.go.id

www.syariahmandiri.co.id

www.bnisyariah.co.id

www.brisyariah.co.id

www.ojk.go.id

38

APPENDICES

Appendix 1: Descriptive Statistic

39

Appendix 2: Multiple Regression Analysis

40

Appendix 3: Normality Test - Histogram

41

Appendix 4: Multocollinearity Test

42

Appendix 5: Chow Test

43

Appendix 6: Table

(%)

Bank Year MSME NPF ROA GDP Inflation

Rate

BSM 2011Q1 34,54 3,3 2,22 6,5 10,6

BSM 2011Q2 34,47 3,49 2,21 6,5 8,5

BSM 2011Q3 30,76 3,21 2,03 6,5 4,93

BSM 2011Q4 24,48 2,42 1,95 6,5 4,15

BSM 2012Q1 25,35 2,52 2,17 6,32 3,37

BSM 2012Q2 25,67 3,04 2,25 6,33 4,49

BSM 2012Q3 25,35 3,10 2,22 6,29 4,48

BSM 2012Q4 33,76 2,82 2,25 6,26 4,41

BSM 2013Q1 33,29 3,44 2,56 5,99 5,26

BSM 2013Q2 32,88 2,90 1,79 5,85 5,65

BSM 2013Q3 32,9 3,40 1,51 5,76 8,60

BSM 2013Q4 31,65 4,32 1,53 5,73 8,36

BSM 2014Q1 27,35 4,88 1,17 5,16 7,76

BSM 2014Q2 30,9 6,46 0,66 5,11 7,09

BSM 2014Q3 30,86 6,76 0,8 5,07 4,35

BSM 2014Q4 30,1 6,84 0,17 5,06 6,47

BSM 2015Q1 29,05 6,81 0,44 4,71 6,54

BSM 2015Q2 33,83 6,67 0,55 4,67 7,07

BSM 2015Q3 27,02 6,89 0,42 4,74 5,07

BSM 2015Q4 27,86 6,06 0,56 5,04 3,95

BSM 2016Q1 26,97 6,42 0,56 4,91 3,50

BSM 2016Q2 26,2 5,58 0,62 5,18 3,45

BRIS 2011Q1 75,41 2,43 0,23 6,5 10,6

BRIS 2011Q2 74,89 3,4 0,2 6,5 8,5

BRIS 2011Q3 77,23 2,8 0,4 6,5 4,93

BRIS 2011Q4 77,72 2,77 0,2 6,5 4,15

BRIS 2012Q1 76,79 3,31 0,17 6,32 3,37

44

BRIS 2012Q2 75,99 2,88 1,21 6,33 4,49

BRIS 2012Q3 75,06 2,87 1,34 6,29 4,48

BRIS 2012Q4 74,49 3,00 1,19 6,26 4,41

BRIS 2013Q1 76,79 3,04 1,71 5,99 5,26

BRIS 2013Q2 68,81 2,89 1,41 5,85 5,65

BRIS 2013Q3 68,91 2,98 1,36 5,76 8,60

BRIS 2013Q4 63,43 4,06 1,15 5,73 8,36

BRIS 2014Q1 71,08 4,04 0,46 5,16 7,76

BRIS 2014Q2 69,72 4,38 0,03 5,11 7,09

BRIS 2014Q3 71,14 4,79 0,2 5,07 4,35

BRIS 2014Q4 42,93 4,60 0,08 5,06 6,47

BRIS 2015Q1 41,27 4,96 0,53 4,71 6,54

BRIS 2015Q2 42,21 5,31 0,78 4,67 7,07

BRIS 2015Q3 41,79 4,90 0,8 4,74 5,07

BRIS 2015Q4 42,67 4,86 0,76 5,04 3,95

BRIS 2016Q1 42,64 4,84 0,99 4,91 3,50

BRIS 2016Q2 43,4 4,87 1,03 5,18 3,45

BNIS 2011Q1 18,79 4,44 3,42 6,5 10,6

BNIS 2011Q2 18,42 4,17 2,22 6,5 8,5

BNIS 2011Q3 16,71 3,6 2,37 6,5 4,93

BNIS 2011Q4 17,42 3,62 1,29 6,5 4,15

BNIS 2012Q1 19,71 4,27 0,63 6,32 3,37

BNIS 2012Q2 21,18 2,45 0,65 6,33 4,49

BNIS 2012Q3 22,96 2,33 1,31 6,29 4,48

BNIS 2012Q4 23,34 2,02 1,48 6,26 4,41

BNIS 2013Q1 25,45 2,13 1,62 5,99 5,26

BNIS 2013Q2 27,89 2,11 1,24 5,85 5,65

BNIS 2013Q3 29,46 2,06 1,22 5,76 8,60

BNIS 2013Q4 30,56 1,86 1,37 5,73 8,36

BNIS 2014Q1 35,17 1,96 1,22 5,16 7,76

BNIS 2014Q2 30,21 1,99 1,11 5,11 7,09

BNIS 2014Q3 30,76 1,99 1,11 5,07 4,35

45

BNIS 2014Q4 24,28 1,86 1,27 5,06 6,47

BNIS 2015Q1 29,97 2,22 1,2 4,71 6,54

BNIS 2015Q2 22,81 2,42 1,3 4,67 7,07

BNIS 2015Q3 22,17 2,54 1,32 4,74 5,07

BNIS 2015Q4 21,12 2,53 1,43 5,04 3,95

BNIS 2016Q1 19,31 2,77 1,65 4,91 3,50

BNIS 2016Q2 19,3 2,80 1,59 5,18 3,45