Volume 3, Number 13, November�2022 e-ISSN: 2797-6068 and p-ISSN: 2777-0915
THE EFFECT OF
INCOME ON THE SELECTION OF THE PRICE OF WALL PAINTING MATERIALS IN LOW INCOME
COMMUNITY HOME CONSTRUCTION (MBR)
Adelina Ratika Isti, Sugini
Universitas Islam Indonesia, Indonesia
Email:
[email protected], [email protected]
KEYWORDS Income of selection; (MBR); wall painting |
ABSTRACT Indonesia is no longer a developing country, but
has grown gradually to become a developed country. In fact, according to The
Office of the United States Trade Representative (USTR), which released the
official release on Saturday 22 February 2020, Indonesia is no longer in the
developing country category. The Ministry of Public Works and Public Housing
(PUPR) admits that housing development for low-income people (MBR) is
currently experiencing a number of obstacles. This study aims to determine
the effect of income on the price of wall paint materials. The methodology to
be used in this study is a rationalistic quantitative methodology.
Quantitative research method is research that is full of nuances of numbers
in data collection techniques in the field. Quantitative research has
characteristics namely hard sciences, 'brief' and narrow focus,
reductionistic, logical and deductive reasoning, knowledge base: causal
relationships testing theories, control over variables, instruments, basic
elements of analysis: numbers, statistical data analysis, generalization
Based on statistical tests using the IBM SPSS application, the results
obtained are in the form of a very strong influence between monthly income on
the choice of wall paint material prices in the construction of houses for
Low Income Communities (MBR). |
INTRODUCTION
Indonesia is no longer a developing country, but has grown gradually
to become a developed country (Winarno, 2018). In fact,
according to The Office of the United States Trade Representative (USTR), which
released the official release on Saturday 22 February 2020, Indonesia is no
longer in the developing country category (Patriasari, 2020).
Urbanization and population growth mean urban areas in Indonesia lack
affordable and quality housing. The shortage is estimated at 820,000 � 920,000
new units per year in urban areas due to the high rate of household formation
and migration to urban areas (Sidemen, 2017).
The results of the 2010 population census
show that as many as 22% or 13 million households out of 61 million Indonesian
households do not have a place to live. While 78% of the population already has
a place to live, there are still many whose homes are not suitable for
habitation or are located in illegal places (Rostiana,
2014).
The Ministry of
Public Works and Public Housing (PUPR) admits that housing development for
low-income people (MBR) is currently experiencing a number of obstacles (Winarno,
2018). The Director
General of Housing Provision of the Ministry of PUPR said that there were at
least 3 obstacles faced by MBR housing developers in urban areas, namely limited
land, regional regulations and financing (Widarti,
Marfuaf, & Retnosari, 2019).
The third problem is financing. This financing problem can be a very
important problem for low-income people (MBR). As is well known, low-income
people (MBR) have limitations in financing housing construction due to their
monthly income. One of the biggest factors of financing is the price of the
materials used in the construction of the house (Hakim, 2016).
Generally, the quality of a material product is directly proportional to
the price of the material. So usually to reduce the number of financing from building
houses, people choose materials at affordable prices (Agustriana, 2018).
In this study, researchers tried to find the effect of low-income
people's income (MBR) on the choice of wall paint prices that they would use
for finishing the houses they built (Abdurahman &
Rudiarto, 2017).
The purpose of this research is to find a relationship or effect between
consumer income on the choice of wall paint material prices in low-income
community (MBR) house construction (Indrianingrum, 2016).
The benefit of this research is to be able to find out the relationship
or influence between consumer income on the choice of wall paint material
prices in the construction of houses for low-income people (MBR) (Sunarti, Yuliastuti,
& Indriastjario, 2018).
RESEARCH METHOD
The methodology to
be used in this study is a rationalistic quantitative methodology. Quantitative
research method is research that is full of nuances of numbers in data
collection techniques in the field. Quantitative research has characteristics
namely hard sciences, 'brief' and narrow focus, reductionistic, logical and
deductive reasoning, knowledge base: causal relationships testing theories,
control over variables, instruments, basic elements of analysis: numbers,
statistical data analysis, generalization
Based on (Kusumo
& SITI, 2011)
in
Quantitative Research Methodology, rationalistic research methodology is a
valid science which is an abstraction, simplification or idealization of
reality, and is proven to be coherent with its logical system. Some
descriptions of the method approach, namely:
a)
Data collection methods:
� The data
that has been studied are not isolated from their environment, selected by
random or purposive sampling
� The survey
is directed by a hypothesis/theoretical basis
b)
Methods of data analysis:
� The
results are brought to the environmental setting, then raised to the population
� After
obtaining data directly from the population, then the data is analyzed using a
statistical approach using the SPSS application.
c)
Arguments in Rationalistic research:
RESULTS AND DISCUSSION
The statistical
hypothesis in this study is that there is an effect of income on the choice of
wall paint material prices in the construction of low-income community houses (MBR)
(Taufik & Sriharyati, 2020).
H0: ρ ≤ 0
H1: ρ
> 0
The following is a Dummy Data Table from Consumers who
buy Wall Paint
Table 2
Research data
Consumer Name |
Sex |
Age |
Work |
Wall Paint Material Price Options |
Income |
A |
1 |
43 |
1 |
55.000 |
1 |
B |
1 |
55 |
5 |
88.000 |
3 |
C |
1 |
41 |
1 |
88.000 |
2 |
D |
1 |
53 |
1 |
88.000 |
3 |
E |
1 |
48 |
3 |
170.000 |
5 |
F |
1 |
58 |
7 |
230.000 |
6 |
G |
1 |
45 |
1 |
55.000 |
0 |
H |
1 |
65 |
0 |
55.000 |
2 |
I |
1 |
37 |
5 |
230.000 |
5 |
J |
1 |
61 |
1 |
55.000 |
0 |
K |
1 |
35 |
3 |
230.000 |
6 |
L |
1 |
67 |
1 |
88.000 |
2 |
M |
1 |
48 |
2 |
88.000 |
2 |
N |
1 |
51 |
7 |
230.000 |
6 |
O |
1 |
35 |
4 |
230.000 |
5 |
P |
2 |
48 |
3 |
170.000 |
4 |
Q |
2 |
42 |
2 |
88.000 |
2 |
R |
2 |
46 |
6 |
230.000 |
6 |
S |
2 |
39 |
0 |
55.000 |
0 |
T |
2 |
34 |
6 |
170.000 |
4 |
U |
2 |
36 |
1 |
170.000 |
4 |
V |
2 |
30 |
5 |
230.000 |
6 |
W |
2 |
41 |
7 |
230.000 |
5 |
X |
2 |
52 |
3 |
170.000 |
4 |
Y |
2 |
42 |
7 |
55.000 |
2 |
Z |
2 |
39 |
5 |
55.000 |
1 |
AA |
2 |
32 |
5 |
170.000 |
3 |
BB |
2 |
40 |
2 |
55.000 |
1 |
CC |
2 |
33 |
5 |
230.000 |
6 |
DD |
2 |
58 |
3 |
170.000 |
5 |
Source: Researcher (2022)
Table 3
Research Description
Wall Paint Consumer Data Variable
Variable |
code |
Information |
Gender |
0 |
Man |
1 |
Woman |
|
Work |
0 |
Not Working |
1 |
Other Jobs |
|
2 |
Honorary |
|
3 |
civil servant |
|
4 |
POLRI/TNI |
|
5 |
Private sector employee |
|
6 |
Health workers |
|
7 |
Self-employed |
|
Family Income per Month |
0 |
< 1,000,000 |
1 |
1,000,000 � 1,500,000 |
|
2 |
1,500,000 � 2,000,000 |
|
3 |
2,000,000 �2,500,000 |
|
4 |
2,500,000 � 3,000,000 |
|
5 |
3,000,000 � 3,500,000 |
|
6 |
3,500,000 � 4,000,000 |
Source: Researcher (2022)
Based on the results of the dummy data collection
above, the researcher conducted a statistical analysis using the IBM SPSS
application. The steps taken by researchers in analyzing data using the SPSS
application are as follows:
1.
Open the IBM SPSS
application, then enter the results of the initial data based on the results of
the questionnaire and interviews provided by the consumer in the Data View
sheet.
Image 1
DataView IBM SPSS
Source: Researcher (2022)
2.
Enter variables
and parameters based on the results of questionnaires and interviews provided
by consumers in the Variable View sheet.
Figure 2
Variable View IBM SPSS
Source: Researcher (2022)
From the image data in the IBM SPSS application, the
variable is income per month and the parameter is the choice of wall paint
material prices.
The analysis process used in
this study uses associative analysis, which is looking for relationships from
the interval ratio data form. Thus the tests that need
to be carried out are Partial Correlation and Regression to see the results of
the Curve Estimation.
Partial
Correlation Test
Partial
correlation analysis (Partial Correlation) is used to determine the
relationship between two variables where other variables that are considered
influential are controlled or fixed (as control variables). The correlation
value (r) ranges from 1 to -1, the value closer to 1 or -1 means the
relationship between the two variables is getting stronger, conversely a value
close to 0 means the relationship between the two variables is getting weaker.
Positive values indicate a unidirectional relationship (X increases, Y
increases) and negative values indicate an inverse relationship (X increases, Y
decreases). The data used is usually an interval or ratio scale.
The following are the steps used to perform the
Partial Correlation test:
1.
Select Menu
Analyze, then select Corralate, and finally select
Partial.
Figure 3
Partial Correlation Test
Source: Researcher (2022)
2. Then the results will come out as follows:
Figure 4
Partial Correlation Test Results
Source: Researcher (2022)
Based on Figure 4, it can be said that the
correlation between the variable in the form of income per month and the
parameter in the form of the price choice of wall paint material is 0.947.
According to (Sugiyono, 2011) the guidelines for providing an interpretation of
the correlation coefficient are as follows:
0,00 - 0,199 =
very low
0,20 - 0,399 =
Low
0,40 - 0,599 =
currently
0,60 - 0,799 =
strong
0,80 - 1,000 =
very strong
So, this indicates that the relationship between the two variables is
very strong or it can be said that the two variables influence each other.
Regression Test
The following are the steps used to carry out the
Regression test:
1.
Select the Analyze
Menu, then select Regression, and finally select Curve Estimation.
Figure 5
� Regression
Test
Source: Researcher (2022)
2.
2. Next, the Curve
Estimation Window will appear. Enter Dependent and Variable data. Then check
the options as shown in the image below:
Figure 6
� Window of
Curve Estimation
Source: Researcher (2022)
3. Then the results will come out as follows:
Figure 7
� Results from
Curve Fit
Source: Researcher (2022)
Figure 8
Model Summary and Parameter Estimates
Source: Researcher (2022)
The results of the Regression Test are
shown in Figure 12. It can be seen that the R value is large, in Linear the R
value is 0.897, Quadratic 0.909 and Cubic 0.932. Of the three values above, the
highest R value is the Cubic equation of 0.932 or 93.2%, then the Quadratic
equation R value is 0.909 or 90.9% and the lowest is the Linear equation R
value of 0.897 or 89.7%.
Based on the value above in the Curve
image, the effect of income or monthly salary on the price of the chosen wall
paint material is as follows:
Figure 9
The Influence Curve of Monthly Income or Salary on the
Price of the Selected Wall Paint Material
Source: Researcher (2022)
CONCLUSION
Based on statistical tests using
the IBM SPSS application, the results obtained are in the form of a very strong
influence between monthly income on the choice of wall paint material prices in
the construction of houses for Low Income Communities (MBR). The relationship
between monthly income and the choice of wall paint material prices in the
construction of Low-Income Communities (MBR) houses is 94.7% and it can be said
that between monthly income and the choice of wall paint material prices are
mutually influential.
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Copyright holders:
Adelina Ratika
Isti, Sugini
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Devotion - Journal of Research and Community
Service
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