###### Economy

# Indonesian Foreign Debt Prediction by Analyzing Foreign Exchange Reserves Against Indonesia's Export Value

Indonesia's foreign debt has increased dramatically in the last decade, both government and private debt, and has consumed the portion of Indonesia's State Budget (APBN). The amount of debt principal and interest is almost double the Indonesian development

**DODI IRWAN SIREGAR**

**Dosen STIE Persada Bunda JL.Diponegoro No.42 Pekanbaru**

*Abstract*

*:**Indonesia's foreign debt has increased dramatically in the last decade,*

both government and private debt, and has consumed the portion of Indonesia's

State Budget (APBN). The amount of debt principal and interest is almost double

the Indonesian development budget. For this reason, there is a need for an

effort to pay it off, that is, every country needs foreign exchange reserves as

a foreign payment instrument, export activities will increase the country's

foreign exchange reserves, which in turn can strengthen economic fundamentals.

One of the government's efforts to obtain foreign exchange from abroad is by

making loans to other countries (foreign debt) and exporting natural and

non-natural resource products abroad. From the proceeds of foreign exchange, it

can be used to increase state development funds. This study uses multiple

linear regression analysis to predict and predict changes in the value of

certain variables if other variables change. Correlation is one of the analytical

techniques in statistics that is used to find the relationship of how strong

the relationship between two or more variables is quantitative. By using a

linearity test where F arithmetic > F tabel is 61.252 > 3.98, then Ho is

rejected. This means that multiple linear regression analysis can be used to

predict Indonesia's foreign debt by analyzing foreign exchange reserves against

the value of Indonesian exports. Obtained the multiple linear regression

equation is Y = 16167.1 + 3031.22X1 - 489X2, the relationship between the

variables above is 0.957917473 is the superior correlation. Where

both government and private debt, and has consumed the portion of Indonesia's

State Budget (APBN). The amount of debt principal and interest is almost double

the Indonesian development budget. For this reason, there is a need for an

effort to pay it off, that is, every country needs foreign exchange reserves as

a foreign payment instrument, export activities will increase the country's

foreign exchange reserves, which in turn can strengthen economic fundamentals.

One of the government's efforts to obtain foreign exchange from abroad is by

making loans to other countries (foreign debt) and exporting natural and

non-natural resource products abroad. From the proceeds of foreign exchange, it

can be used to increase state development funds. This study uses multiple

linear regression analysis to predict and predict changes in the value of

certain variables if other variables change. Correlation is one of the analytical

techniques in statistics that is used to find the relationship of how strong

the relationship between two or more variables is quantitative. By using a

linearity test where F arithmetic > F tabel is 61.252 > 3.98, then Ho is

rejected. This means that multiple linear regression analysis can be used to

predict Indonesia's foreign debt by analyzing foreign exchange reserves against

the value of Indonesian exports. Obtained the multiple linear regression

equation is Y = 16167.1 + 3031.22X1 - 489X2, the relationship between the

variables above is 0.957917473 is the superior correlation. Where

*t*

*₁*

*arithmetic >*

*t*

*1*

*table = 15.52 > 2.178, then Ho is rejected meaning, there is a large*

(significant) influence partially between foreign exchange reserves and foreign

debt.

(significant) influence partially between foreign exchange reserves and foreign

debt.

*t*

*2*

*arithmetic ≤*

*t*

*2*

*table = -1.273 ≤ 2.178, Ho accepted the meaning, there is no significant*

(significant) effect partially between the export value of foreign debt.

(significant) effect partially between the export value of foreign debt.

*Keywords:**Foreign Debt,*

Foreign Exchange Reserves, Export Value.

Foreign Exchange Reserves, Export Value.

**Introductions**

During the economic crisis, Indonesia's foreign debt, including government

and private foreign debt, has increased dramatically in rupiah. Therefore, the

Indonesian government must add new foreign debt to pay for the old foreign debt

that has matured. The accumulation of foreign debt and interest will be paid

through the Republic of Indonesia National Budget by installments in each

fiscal year. This causes a reduction in prosperity and prosperity in the

future, so that it will clearly burden the Indonesian people. In the short

term, foreign debt is very helpful for the Indonesian government in its efforts

to cover the budget deficit of state revenues and expenditures, due to the

financing of routine expenditures and considerable development expenditures.

Thus, the rate of economic growth can be driven in accordance with the targets

set previously. But in the long run, it turns out that the government's foreign

debt can cause various economic problems in Indonesia (

*Atmaja U.S, 2000*).

How do you keep Indonesia's debt from facing a big risk so that it can be

repaid? The trick is to maintain foreign exchange reserves. Foreign exchange

reserves, aside from being used as maintaining the stability of the rupiah, are

also used to pay off government foreign debt. Using foreign exchange reserves

to keep the rupiah. With our high foreign exchange reserves, it means we have

the ability to pay off debts (

*Zambrut ,*

2019).

2019

To be able to bring in foreign exchange, the government can not only rely

on foreign exchange or the foreign exchange market. On the other hand the

government must be able to strengthen the current account balance. The way is

to increase the current account receipts income, namely income derived from

exports of goods and services and other natural resource exports, both oil and

gas and non-oil and gas.

**Multiple Linear Regression Analysis**

This multiple linear regression analysis

is used to predict changes in the value of certain variables if other variables

change. Multiple regression is said, because the number of independent

variables as more than one predictor, multiple linear regression equations are

used. Regression analysis is a relationship that is obtained and expressed in

the form of mathematical equations that express functional relationships

between variables.

According to

*Drapper*

and Smith (1992)regression analysis is an analytical method that can be

and Smith (1992)

used to analyze data and draw meaningful conclusions about the relationship of

variable dependence to other variables. Regression is divided into 2, namely,

simple linear regression analysis is used to obtain mathematical relationships

in the form of an equation between non-independent variables with a single

independent variable. Multiple linear regression analysis is a linear

relationship between two or more independent variables (X1, X2,

.... Xn) with the dependent variable (Y). This analysis is to determine the

direction of the relationship between the independent variable and the

dependent variable whether each independent variable is positively or

negatively related and to predict the value of the dependent variable if the

value of the independent variable increases or decreases. Data used is usually

interval or ratio scale

The method that

can be used to estimate the parameters of simple linear regression models as

well as multiple linear regression models is the ordinary least square / OLS

method and the maximum likelihood method (

*Kutner*

et.al, 2004).

et.al, 2004

**Multiple Linear Correlation**

Correlation

coefficient is a number that states the strength of the relationship between

two or more variables, can also determine the direction of the relationship of

the two variables, the correlation value is (r) = (-1 ≤ 0 ≤ 1).

Multiple

correlation analysis is an extension of simple correlation analysis. In the

analysis of multiple correlation aims to find out how the degree of

relationship between several independent variables (Variables X1, X2,....,Xk)

with the dependent variable (Variable Y) together. For the strength of the

relationship, the value of the correlation coefficient is between -1 to 1,

while the direction is expressed in the form of positive (+) and negative (-).

The multiple linear correlation coefficients are calculated using the following

formula:

Based

on multiple correlation, given the RY.1.2 ,..., n notation

calculated through the path of the occurrence of the relationship between

several independent variables (X1, X2, ... ..., Xn) with

one dependent variable (Y), that is in the form of multiple linear regression Y

= a + b1.X1 + b2.X2 + ,…, + bn.Xn.

The strength interval of a number of statistical authors makes the interval

categorization of the strength of the correlation relationship. Jonathan

Sarwono, for example, makes the strength intervals of relations as follows:

Multiple Correlation is a

correlation that intends to see the relationship between 3 or more variables

(two or more dependent variables and one independent variable). Multiple

correlations are related to the isolation of independent variable variables as

they are correlated with the dependent variable. In addition, according to

*Akdon and Ridwan (2013)*multiple

correlation is a value that gives a strong influence or relationship between

two or more variables together with other variables.

The assumptions

related to the multiple regression analysis are:

1.

Independent variables and dependent

variables have a linear relationship

2.

All variables, both independent and

dependent variables, are continuous random variables.

3.

Conditional distribution of the value of

each variable is normally distributed (multivariate normal distribution).

4.

For various combinations of variable

values with one another, the variance of the conditional distribution of each

variable is homogeneous (asumsu homoscedasticity applies to all variables).

For each variable, the value of observations from one another, is not

related. Multiple correlation is a correlation consisting of two independent

variables (X1, X2) or more, and one dependent variable (Y). the relationship

between variables can be described as follows:

**Data and**

Analysis

Analysis

**Types and Research Approaches**

This research

includes Explanatory Research with a Quantitative approach, using multiple

linear analysis methods due to more than one independent variable. The

variables that influence are called independent variables and the variables

that are affected are called dependent variables (dependent variables).

**Variables in measurement**

This study consists of two independent variables, namely the Indonesian

Export Value Abroad (X1) and Indonesia's Gross Domestic Income (X2),

while the dependent variable is the Rupiah (IDR - USD) Currency Exchange Rate

abbreviated as Variable (Y).

**Data Collection Technique**

Data collection techniques carried out to obtain relevant data from the

problems studied are through library research (Library Research), namely by

reading and studying the literature contained in the library, with the

intention to put a theoretical foundation on the main problems being discussed.

**Data Collection Technique**

Data collection techniques carried out to obtain relevant data from the

problems studied are through library research (Library Research), namely by

reading and studying the literature contained in the library, with the

intention to put a theoretical foundation on the main problems being discussed.

**Table 1.**Data on

Indonesian External Debt

2003 - 2016

Year

Foreign

Debt (Y)

2003

86657

2004

87492

2005

82431

2006

78595

2007

141180

2008

155080

2009

172871

2010

202413

2011

225375

2012

252364

2013

266109

2014

293328

2015

310730

2016

316407

*Source: BPS Indonesia 2017.*

Foreign debt or foreign loans, are a portion of a country's total debt

obtained from creditors outside the country. Recipients of foreign debt can be

in the form of governments, companies, or individuals. The form of debt can be

in the form of money obtained from private banks, governments of other

countries, or international financial institutions such as the IMF and World

Bank.

**Tabel 2.**Table of

Indonesian Export Data (X1) /billion US$ sinceyear 2011 – 2016

**Year**

**Indonesia Export Value**

**/ billion US$**

2003

61.1

2004

71.6

2005

85.7

2006

100.8

2007

114.1

2008

137

2009

116.5

2010

157.8

2011

203.5

2012

190

2013

182.6

2014

176

2015

150.4

2016

145.2

*Source: BPS Indonesia 2017.*

Export is the process of transporting goods or commodities from one

country to another. (

*Merriam-Webster's.*

2003). This process is often used by companies with small to medium

2003

business scale as the main strategy to compete at the international level (

*Deresky, Helen. 2006*).

**Table**

3.Indonesian

3.

Foreign Exchange Reserves2003 –

2016

**Year**

**Indonesian**

Foreign Exchange Reserves (X

Foreign Exchange Reserves (X

**1**

**)**

2003

36.3

2004

36.3

2005

34.7

2006

42.6

2007

56.9

2008

51.6

2009

66.1

2010

96.2

2011

110.1

2012

112.8

2013

99.4

2014

111.9

2015

105.9

2016

116.4

*Sumber : BPS Indonesia 2017*

**Results and Discussion**

This research predicts and predicts the position of Indonesia's External

Debt in the future by processing and analyzing past data, as the dependent

variable (bound), is the Value of Indonesia's Exports Outside Multiple linear

regression analysis with dependent variable is Indonesian Foreign Debt

abbreviated as (Y), and independent variable (independent) is Indonesian

Foreign Exchange Reserves (X1), and Indonesian Export Value as (X2).

Data from the variables above are as follows:

In a study at the stage of analyzing data, multiple

linear regression is the development of simple linear regression, which can be

used to predict future demand based on past data analysis or to determine the

effect of one or more independent variables on one variable dependent is used.

The application of multiple methods of the number of independent variables used

is more than one which influences independent non-independent variables (

*Siregar S, 2012*).

From the table of independent and bound variable data

above, we obtain multiple linear regression equations with two predictors.

Start by creating a helper table as follows:

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