Zambrut
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Economy

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

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 SIREGARDosen 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
t arithmetic > t1 table = 15.52 > 2.178, then Ho is rejected meaning, there is a large
(significant) influence partially between foreign exchange reserves and foreign
debt.
t2 arithmetic ≤ t2 table = -1.273 ≤ 2.178, Ho accepted the meaning, there is no significant
(significant) effect partially between the export value of foreign debt.


Keywords: Foreign Debt,
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
).

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
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 (X
1, 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
).


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

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
business scale as the main strategy to compete at the international level (Deresky, Helen. 2006).


Table
3.
Indonesian
Foreign Exchange Reserves
2003 –
2016

Year
Indonesian
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:


Bibliography
Akdon and Ridwan. 2013. Formulas and Data in Statistical Analysis.
Bandung: Alfabeta.

Atmadja A. S, 2000. Indonesian Foreign Debt Development and Impact. Journal of Economic
Accounting, Faculty of Economics - Petra Christian University. Vol. 2, No. 1,
May 2000: 83 - 94.

Deresky, Helen. 2006. International Management. 4th ed. United States of America. Addison
- Wesley. Page 237.

Draper, N., H. Smith. 1992. Analysis of Applied Regression Second
Edition
. Translation by Bambang Sumantri. Gramedia Main Library, Jakarta.

Indriantoro, nurdan Supomo, Bambang
(1999). Business Research Methodology for
Accounting & Management
. Yogyakarta: BPFE Publisher.

Kelley, W. D., and Jr. Ratliff, T.A.,
Nenadic, C. 1992. Basic Statistics for
laboratories, a primary for laboratory worker
. Van Nostrand Reinhold, New
York.

Kutner, M.H., C.J. Nachtsheim., And J.
Neter. 2004. Applied Linear Regression
Models
. 4th ed. New York.

McGraw-Hill Companies, Inc.Mankiw,
Gregory. 2006. Introduction to
Macroeconomics
, Third Edition, Salemba Empat Jakarta.

Merriam-Webster's. 2003. Collegiate Dictionary. 11th ed. United
States of America
. Merriam-Webster, Inc. 2003. p. 441.

Samuelson, Paul A. & Nordhaus, William
D. 2004. Macroeconomic science.
Indonesian Edition, Jakarta: PT Media Global Education.

Siregar, S. 2012. Parametric Statistics for Quantitative Research, First Printing,
Jakarta: PT Bumi Aksara.
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