JURNAL SYNTAX IDEA p�ISSN: 2723-4339 e-ISSN: 2548-1398 |
Vol. 5, No. 6, Juni 2023 |
ANALYSIS
OF THE EFFECT OF VARIATION ORDER ON WASTEWATER PROJECTS
Muhammad Riska Dirgantoro, Sarwono Hardjomuljad
Fakultas Teknik, Universitas Mercu Buana
E-mail: [email protected],
[email protected]
Abstract
The objectives of this study are: (1)
Knowing the factors causing variation order in the Wastewater project. (2)
Knowing the effects of variation orders on contractors/service providers in
wastewater projects. The type of research used in this study is the
quantitative research method. The quantitative research method uses research
data in the form of numbers and analyzed using statistics from the results of
data collection that have been verified and validated by experts, research
variables that will be used in the next questionnaire stage obtained 11
variables of factors causing variation orders and 10 variables of the influence
of variation orders on contractors, can be seen in appendix 2. then the following
conclusions can be drawn: a) The dominant factors causing the Variation Order
in wastewater projects in the DKI Jakarta Government, b) The dominant influence
of the Variation Order on service providers/contractors.
Keywords: Wastewater; Project; Variation Order
INTRODUCTION
DKI Jakarta City as the Metropolitan
Capital has various urban problems that are urgent to overcome. One of the
urban problems is water pollution in rivers and water bodies that pass through
the city of DKI Jakarta. Indicators of water pollution that are easily visible
and can be felt are concentrated and black water in waterways and rivers that
are black and smell bad.
Figure 1
Identification of Wastewater Management in DKI Jakarta
From the identification of wastewater
management in DKI Jakarta, it can be seen that 71% of wastewater is still
managed individually with septic tanks, 16% with integrated treatment, 2% with
sewerage, and 11% is not treated at all (in slum areas). In the implementation
of a construction project, the change order is regulated by the terms of the
contract. A contract is a legally binding agreement between the parties who
sign it. To define and facilitate various contract provisions of construction
projects, many industrial organizations have introduced standard contract
documents and agreements (Syal & Bora, 2016).
Variation Order is something that
always happens in every construction project, in several studies it was found
to be one of the factors causing claims. Variation orders or change orders
always have implications for cost and time, whatever the variation there will
be a risk to the work being done both in the delay of work and additional costs
that must be borne (Hardjomuljadi,
2016).
In projects organized by the Government,
Variation orders have been regulated in article 87 of Presidential Regulation
Number 4 of 2015 concerning the Fourth Amendment to Presidential Regulation
Number 54 of 2010 which reads "paragraph (1): if there is a discrepancy
between field conditions during implementation, with drawings/or technical
specifications specified in the Contract Document, KDP together with the
Provider of Goods/Services can make changes to the contract which include: (a)
Increase or decrease the volume of work stated in the contract; (b) Increase
and/or decrease the type of work; (c) Change the technical specifications of
the work by field needs; (d) Change the execution schedule.
The contract change referred to in
paragraph (1), applies to work using the Unit Price Contract or part of the
work using the unit price of the Lump sum and Unit Price Combined
Contract". The term change order is more commonly called a variation
order, but in government-organized projects, the term change order is better
known as a change order contract. In construction projects, work order changes
(variation order or change order), in fact, often occur because the application
of project planning methods does not always work well. The main cause of
variation order or change order is the lack of understanding of related parties
at the planning stage or in the implementation of construction projects.
Therefore, there are many impacts caused both in terms of cost, quality, and
time.
If changes occur very often, it will
have an impact on increasing costs, late project completion, and also building
quality that is not following the quality plan. A project will experience
project delays if it is not completed as soon as possible. Punctuality in
construction projects greatly affects the absorption of funds and physical
realization in the field so that it is expected to have maximum project time
performance, where the project can be completed on time, or even ahead of the
schedule planned (Khalim, 2020).
In the wastewater project in DKI
Jakarta, there has been a Variation Order that is being faced and completed by
the parties involved in the project. Efforts to deal with the Variation Order
are problems that are not expected by construction industry players because
they will be faced with serious ethical problems and disputes, if not based on
good technical innovation in dealing with Variation Order problems (Budiutomo, Santoso, & Hakim, 2021). Research is needed to
analyze the right strategy in dealing with the Variation Order in the implementation
of wastewater development construction projects located in DKI Jakarta, on the
causes and impacts caused so as not to have a significant effect.
Based on the background stated above,
the author identifies the following problems; (a) There are changes in the
technical specifications of the work following field needs. (b) There is an
adjustment in the type of work and volume of work after calculations are
carried out in the field that allow changes in costs. The problem is formulated
from the background of the existing problem in the research topic, as a
research question, namely: (1) What factors cause variation orders in the
Wastewater project? (2) What are the effects of variation orders on
contractors/service providers in wastewater projects?
The objectives of this study are: (1)
Knowing the factors causing variation order in the Wastewater project. (2) Knowing
the effects of variation orders on contractors/service providers in wastewater
projects. The benefit of this research is as a reference during the auction evaluation
stage about how much-working capital must be owned by prospective service
providers at the time of verification.
RESEARCH METHODS
The type of
research used in this study is the quantitative research method. Quantitative
research methods use research data in the form of numbers and are analyzed
using statistics (Zano, 2019).
The research method
used will determine a process starting from data collection, processing data
into information to be analyzed, and finally will get results in the form of
findings that can be concluded. Survey research is conducted to determine what
factors are a cause of variation orders in wastewater projects and what factors
influence the cost performance of contractors.
Research design is
a concept or framework in a study. The research method contains a framework of
thought that explains the aspects to be researched in this study. The research
design was made to determine the most appropriate research method to answer the
problem in research that is tailored to the topic, namely how the effect of
variation orders on service provider financing on projects with a unit price
contract system in wastewater projects.
In this study, a
deductive hypothesis approach will be carried out, where problems are
formulated from finding facts and drawing specific conclusions from general
statements, or from general to specific. To determine the factors causing
variation orders and their consequences, a survey approach will be carried out
on several research objects on projects that have just been running or have
just been implemented, then reviewed and analyzed the survey results from
several correspondents related to the research object. The survey results
through the distribution of questionnaires will be carried out to parties related
to the implementation of construction projects, namely service
providers/contractors, service users/owners, and consultants.
For the topic
raised in this study, namely the effect of variation orders on service provider
financing in wastewater projects factors that cause variation orders for survey
methods are formulated into 2 (two) variables that influence each other,
namely: (a) Dependent variables as variables that are influenced, namely
contractor cost performance. (b) Independent variable as an influencing
variable, namely variation order in the unit price contract system, which
includes: (1) Contract documents. (2) Stakeholders. (3) Design. (4) Service
providers/contractors|
The independent
variable (X) is the variable that affects the occurrence of Variation Order
(Y). The following variables are the results of previous research which are
grouped and then will be tested for validity by experts. Based on experience
and interviews with several stakeholders, these variables can be seen in the
table below.
Table
1 Free Variables (X) in Research
Variable |
|
Indicator |
Source |
Contract Document (X1) |
X1.1 |
Incomplete Contract Documents |
(Memon et al., 2014). (Mohammad & Hamzah,
2019) |
X1.2 |
Technical specifications that are not clearly stated
on the RKS, bill of quantity, and
tender drawings. |
(S. Gumolili et al., 2012). (Tenno &; Suroso, 2021) |
|
X1.3 |
Addition
or reduction
of work items |
(Sandy A. Gumolili, Dinas,
& Pemprov, 2012) (Mitra et al., 2020) |
|
Variable |
|
Indicator |
Source |
Stakeholder (X2) |
X2.1 |
The project owner requests for optimization
of building functions. |
(Assbeihat, 2017). (Ana Yuni Martanti, 2018) |
X2.2 |
The owner instructs additional work |
(Hardjomuljadi, 2016). (Assbeihat, 2017) |
|
X2.3 |
Owner's delay in approving
drawings, contract design, and clarification |
(Memon et al., 2014) (S. Gumolili et al., 2012) |
|
Variable |
|
Indicator |
Source |
X3.1 |
A mismatch between the image and the field
conditions. |
(S. Gumolili et al., 2012) (Ana Yuni Martanti, 2018) (Muluk, Misriani, Atmaja,
Ali, & Monica, 2018) (Mohammad & Hamzah,
2019) |
|
X3.2 |
Details
of the initial tender drawings are incomplete / less clear. |
(S. Gumolili et al., 2012) (Assbeihat, 2017) �(Muluk et al., 2018)� (Nurmala, n.d.) |
|
X3.3 |
Design
changes |
(Nurmala, n.d.).� (S. Gumolili et al., 2012) (Hardjomuljadi, 2016) |
|
X3.4 |
Delay in the shop
drawing approval by consultants and
owners. |
(Ana Yuni Martanti, 2018) |
|
X3.5 |
Significant volume differences between
drawings, field conditions, and the Bill of Quantity |
(Tenno & Suroso, 2021) |
Variable |
|
Indicator |
Source |
Service Provider / Contractor (X4) |
X4.1 |
The contractor's work start schedule is
later than the planned schedule |
(Muluk et al., 2018) |
X4.2 |
Poor
material control |
(S. Gumolili et al., 2012) (Muluk et al., 2018) |
|
X4.3 |
Lack of contractor teamwork in the
implementation of work |
(Assbeihat, 2017)(Muluk
et al., 2018)(Muluk et al., 2018) |
|
X4.4 |
Changes in work methods |
(Tenno & Suroso, 2021) (Muluk et al., 2018) |
|
X4.5 |
Errors
and omissions in the calculation of volume estimation |
(Muluk et al., 2018), (Tenno & Suroso, 2021) |
|
X4.6 |
Less quick decision-making by contractors |
(Assbeihat, 2017)(Muluk et al., 2018)(Muluk et al., 2018) |
|
X4.7 |
Poor project management |
(Wali & Saber, 2019) |
|
X4.8 |
Disruption
of cash flow of service providers/contractors |
Observation |
|
X4.9 |
Project profit/profit increases |
Observation |
|
X4.10 |
Project profit/profit drops |
Observation |
|
X4.11 |
Quality
of work drops |
Observation |
|
X4.12 |
Quality of work goes up |
Observation |
In field studies,
the effect of variation orders on contractors includes; (1) Project
information. (2) Initial budget. (3) Project achievements during project
implementation. In this study take samples that are part of the population
where the characteristics of the sample are those that represent the entire
population, namely the parties involved in the implementation of the Wastewater
project.
The three parties
are (a) Party I, originating from the owner or work unit at the DKI Jakarta
Government involved in the implementation of the project; (2) Party II,
consisting of supervisory consultants involved in the implementation of the
project; (3) Third Party, originating from the service provider/implementing
contractor on the DKI Jakarta Government project.
For the measurement
of the questionnaire value, a measurement technique with a Linkert scale will
be used, namely in the form of what factors cause variation orders in the
Wastewater project for the unit price contract system, with the following
measurement scale: Description for the measurement scale of the frequency level
of factors causing variation orders in construction projects; (a) Never. (b)
Very Rare. (c) Rarely. (d) Often. (e) Very Often.
In the survey method
research, primary data was obtained from the results of collecting
questionnaires on the three respondents, consisting of service users/owners or
work units at the DKI Jakarta Government involved in project implementation,
supervisory consultants involved in project implementation, and service
providers/contractors implementing Wastewater in DKI Jakarta Government
projects.
Secondary data for
survey methods and field studies are obtained from literature studies such as
journals, books, references, and other studies related to the research topic.
From the responses from correspondents regarding the factors causing variation
orders in the Air project for the unit price contract system, an analysis of
the calculation of the Relative Importance Index (RII) was carried out with the
formula:
�����������������������������������������������
Where:
ƩW = The
weight of each factor by respondents ranged from 1 to 5
H = Largest weight
S = The sum of respondents'
responses from never there to always there.
RII is used
as a ranking technique for each statement and compares responses received from
the three respondents, consisting of service users/owners or work units in the
DKI Jakarta Government involved in project implementation, supervisory
consultants involved in project implementation, and service providers/contractors
implementing Wastewater in DKI Jakarta Government projects. To measure the
confidence received from the data, the level of reliability and validity will
be tested on the data obtained from the questionnaire.
Validity
tests are used to measure the validity or absence of a questionnaire, the
questionnaire will be said to be valid if the questionnaire can or can measure
the object being measured. The validity test is used to determine whether or
not it is significant by comparing the calculated r-value
(judging from the corrected item value � total correlation) with the table r
value. Where if the corrected item value � total correlation ≥ r table
then the data is valid.
Reliability
tests are used to describe stability and reliability so that the measuring
instrument has high reliability and can be trusted (Ana Yuni Yuni Martanti, 2019). The
reliability test in this study used the internal reliability coefficient of
alpha. With a reliability test, a study will know how each question in the
questionnaire is related. The reliability test used is to calculate the alpha
coefficient, the questionnaire question can be said to have a level of
reliability if the value of the Cronbach Alpha coefficient is above 0.6.
Reliability testing with the help of SPSS (Statistical Package for the Social
Sciences) version 26 program.
The
normality test in this study aims to determine whether a test variable is
normally distributed or not. Testing in the study using the Kolmogorov-Smirnov
one-sample test. A data has a normal distribution if the value of sig. (α)
greater than the predetermined significant level. In this test, the significant
level determined is 5%.
RESULTS AND DISCUSSION
The data collection
method is carried out through the distribution of questionnaires with question
items obtained from predetermined research variables based on literature
studies, which are related to the factors causing variation orders and their
effects on construction project performance. Data collection with the
distribution of questionnaires consists of a validation stage by experts and a
subsequent stage of correspondents. The data that has been obtained is then
tested for validity and reliability, after which data analysis is carried out
with a statistical analysis of the SPSS v.26.0 programs.
Table
2 Research Expert Profile
Expert |
Educational Background |
Experience Background in Project Construction |
Length of Experience |
Expert 1 |
S3 |
Academy |
30 years |
Expert 2 |
S2 |
Consultant
Practitioner |
40 years |
Expert 3 |
S2 |
Consultant
Practitioner |
22 years old |
Expert 4 |
S2 |
Contractor
Practitioner |
13 years old |
Expert 5 |
S1 |
Consultant Practitioner |
20 years |
Expert 6 |
S1 |
Contractor Practitioner |
12 years |
Expert 7 |
S1 |
Bureaucracy |
7 years |
The data obtained is a statement of
agreement or no to the variables that cause variation orders and their effects
on contractor financing. The format form of expert validation of the
questionnaire can be seen in Appendix 1. The results obtained from the data
collection process against expert validation are then used as a basis for collecting
questionnaire data on the three respondent parties consisting of, owners or
work units at the DKI Jakarta Government involved in project implementation,
consultants both planning consultants and supervisory consultants involved in
project implementation, and service providers/contractors implementing
wastewater in DKI Jakarta Government projects.
The data obtained from experts are
described in the data tabulation in Table 3 below. For tabulations, the number
1 indicates a value for the expert who agrees with the variable, and the number
0 indicates the expert who disagrees. From the results of the sum, if it is
greater than or equal to 4, the variable is considered to be usable in the
questionnaire.
Table 3 Expert
Validation Results
Variable (X) |
P1 |
P2 |
P3 |
�P4 |
P5 |
P6 |
P7 |
Sum |
Result |
X1.1 |
�� 1 |
� 1 |
�� 0 |
�1 |
0 |
1 |
1 |
5 |
Accepted |
X1.2 |
�� 1 |
� 1 |
�� 0 |
�1 |
0 |
0 |
1 |
4 |
Accepted |
X1.3 |
�� 1 |
� 1 |
�� 1 |
�1 |
0 |
1 |
1 |
6 |
Accepted |
X2.1 |
�� 1 |
� 1 |
�� 1 |
�0 |
0 |
1 |
1 |
5 |
Accepted |
X2.2 |
�� 1 |
� 1 |
�� 1 |
�1 |
1 |
1 |
1 |
7 |
Accepted |
X2.3 |
�� 1 |
� 1 |
�� 1 |
�0 |
0 |
0 |
1 |
4 |
Accepted |
X3.1 |
�� 1 |
� 1 |
�� 1 |
�1 |
0 |
1 |
1 |
6 |
Accepted |
X3.2 |
�� 1 |
� 1 |
�� 0 |
�1 |
0 |
1 |
1 |
6 |
Accepted |
X3.3 |
�� 1 |
� 1 |
�� 1 |
�1 |
1 |
1 |
1 |
7 |
Accepted |
X3.4 |
�� 1 |
� 1 |
�� 1 |
�1 |
0 |
0 |
1 |
5 |
Accepted |
X3.5 |
�� 1 |
� 1 |
�� 1 |
�1 |
0 |
1 |
1 |
6 |
Accepted |
X4.1 |
�� 1 |
� 1 |
�� 0 |
�1 |
0 |
0 |
0 |
3 |
Rejected |
X4.2 |
�� 1 |
� 1 |
�� 0 |
�1 |
0 |
0 |
1 |
4 |
Accepted |
X4.3 |
�� 1 |
� 1 |
�� 0 |
�1 |
0 |
0 |
1 |
4 |
Accepted |
X4.4 |
�� 1 |
� 1 |
�� 1 |
�0 |
1 |
0 |
0 |
4 |
Accepted |
X4.5 |
�� 1 |
� 1 |
�� 0 |
�0 |
0 |
1 |
0 |
3 |
Rejected |
X4.6 |
�� 1 |
� 1 |
�� 1 |
�1 |
0 |
0 |
0 |
4 |
Accepted |
X4.7 |
�� 1 |
� 1 |
�� 0 |
�1 |
1 |
0 |
0 |
4 |
Accepted |
X4.8 |
�� 1 |
� 1 |
1 |
�1 |
1 |
0 |
1 |
6 |
Accepted |
X4.9 |
�� 1 |
� 1 |
1 |
�1 |
1 |
1 |
1 |
7 |
Accepted |
X4.10 |
�� 1 |
� 1 |
1 |
�1 |
1 |
0 |
1 |
6 |
Accepted |
X4.11 |
�� 1 |
� 1 |
1 |
�1 |
1 |
0 |
1 |
6 |
Accepted |
X4.12 |
�� 1 |
� 1 |
1 |
�1 |
1 |
1 |
1 |
7 |
Accepted |
From the
results of data collection that have been verified and validated by experts, research
variables that will be used in the next questionnaire stage obtained 11
variables of factors causing variation orders and 10 variables of the influence
of variation orders on contractors, which can be seen in Appendix 2.
Furthermore,
11 variables of factors causing variation orders and 10 variables of influence
of variation orders on contractors are submitted in the form of questionnaire
questions and made into a format that can be seen in Appendix 2. The
questionnaire for respondents itself was distributed to the three respondents,
consisting of service users/owners or work units at the DKI Jakarta Government
involved in project implementation, consultants, both planning consultants and
supervisory consultants involved in implementation. Here is the data from
respondents in this study.
Figure 2 Respondent Composition
(Source: processed by the
researcher)
Figure 2
shows that the respondents in this study consisted of 40 respondents consisting
of 30 contractor respondents, 5 consultant respondents, and 5 owner respondents
with the following percentages, namely 75% contractors/service providers, 12%
consultants, and 13% owners/service users involved in wastewater projects,
especially in DKI Jakarta Government projects. With the educational background
of each respondent is listed in Figure 4.2 and the long experience of work is
listed in Figure 3.
Figure
3 Educational Background of Correspondent
(Source: processed researcher)
Figure 4 Length of Work Experience
(Source:
processed researcher)
The next stage of this study was tabulating data on statements obtained from respondents, namely the factors that cause the Variation Order on wastewater projects to project costs. The results of the data tabulation were then analyzed using SPSS version 26.0. The results of the data tabulation can be seen in Appendix 3. After tabulating the data, the next step before analyzing the RII calculation of the factors that cause the Variation Order in the DKI Jakarta Government wastewater project and the effect of the Variation Order on the financing of service providers/contractors is to test the results of respondents' statements. These tests include:
A.
Validity Test
Validity according to Sugiyono (2013) is a measure that can show the validity or validity of the instrument. In this validity test refers to an instrument in carrying out its function. Variables obtained from articles, journals, and scientific papers published. Data processing using the help of the SPSS for Windows program. An instrument can be said to be valid if the instrument can be used to measure what is to be measured. To calculate data measured by a formula:
Where:
X = The score obtained by the
subject from all items
Y = Total score obtained from all
items
ΣX = Number of scores in
distribution X
ΣY = Number of scores in the Y
distribution
ΣX2 = Number of squares in the
distribution score
X ΣY2 = Number of squares in
distribution score Y
N = Number of respondents
The interpretation of the
processing results is to compare the Sig value with the α value (0,05) by
criteria: (a) If Sig < 0,05 then the statement item is valid. (b) If Sig
> 0,05 then the statement item is invalid.
In addition, it can also be seen by
comparing the calculated r-value with the table r-value, namely: (1) If recalculate> table, Then the
question items tested are declared valid. (2) If calculate < table, Then the
question item tested is declared invalid. The calculated value is the value
contained from the corrected item�total correlation and looks at the table
value with significance 0,05 for test 2 side (two-tailed) and N=40, df=(N-2), df=40 �2 = 38, then
obtained label (0,05;38) =0,312. A correlation coefficient of at least 0.312
differentiating power is considered satisfactory. This limitation is a
convention, so test authors may determine their limits on the discriminating
power of items in consideration of the content and purpose of the scale
prepared.
Table 4 Validity Test Data Results
Variable Code |
r calculate |
r table |
Result |
X1.1 |
0,398 |
0,312 |
VALID |
X1.2 |
0,361 |
0,312 |
VALID |
X1.3 |
0,354 |
0,312 |
VALID |
X2.1 |
0,431 |
0,312 |
VALID |
X2.2 |
0,397 |
0,312 |
VALID |
X2.3 |
0,757 |
0,312 |
VALID |
X3.1 |
0,757 |
0,312 |
VALID |
X3.2 |
0,811 |
0,312 |
VALID |
X3.3 |
0,597 |
0,312 |
VALID |
X3.4 |
0,593 |
0,312 |
VALID |
X3.5 |
0,674 |
0,312 |
VALID |
X4.2 |
0,379 |
0,312 |
VALID |
X4.3 |
0,617 |
0,312 |
VALID |
X4.4 |
0,805 |
0,312 |
VALID |
X4.6 |
0,376 |
0,312 |
VALID |
X4.7 |
0,313 |
0,312 |
VALID |
X4.8 |
0,379 |
0,312 |
VALID |
X4.9 |
0,617 |
0,312 |
VALID |
X4.10 |
0,849 |
0,312 |
VALID |
X4.11 |
0,634 |
0,312 |
VALID |
X4.12 |
0,654 |
0,312 |
VALID |
Source: (processed by the researcher)
From the
results of the statistical validity test data above, it can be seen that none
of the statement items of the variable are valid. The minimum requirement is
considered qualified with a value of r = 0.312. So if
the correlation between items with a total score of more than 0.312 then the
variable is declared valid.
B.
Reliability Test
Reliability
tests are performed to determine how far the measurement results remain
consistent when measuring two or more times of the same symptoms using the same
measurement tool. There are several reliability testing methods, one of which
will be used in this study is Cronbach's Alpha method. To see the level of
reliability based on Cronbach's Alpha value can be seen in the following table:
Table 5 Reliability Levels
Cronbach�s Alpha |
Reliability Level |
0.00 s/d 0.20 |
Less Reliable |
0.20 s/d 0.40 |
Somewhat Reliable |
0.40 s/d 0.60 |
Quite Reliable |
0.60 s/d 0.80 |
Reliable |
0.80 s/d 1.00 |
Highly Reliable |
Source: Parametric Statistical
Research
In this
study, after a validity test was carried out and all factors were declared
valid, the factors were included in the reliability test. The reliability test
results can be seen in the following table 6:
Table 6 Reliability Test Results
Reliability
Statistics |
|
Cronbach�s
Alpha |
N of Items |
.895 |
21 |
(Source:
SPSS V.26 researcher)
Table 6 can explain the value of
Cronbach's Alpha with the number of indicators of the independent variable as
many as 21 variables with a value > 0.80. Cronbach's alpha value > 0.80 so
this variable is declared reliable with the level of reliability being very
reliable because it is located in the range of 0.80 � 1.00.
C.
Normality Test
Normality Analysis is a sample
testing method to determine the level of normality of answer data from research
respondents. The goal is to find out the distribution of data in a variable
used in research, whether it is normally distributed or not. Normality analysis
in this study used the Kolmogorov�Smirnov Test. Data are normally distributed
if the Kolmogorov�Smirnov significance value is greater than 0.05. In this
normality analysis, tests were carried out on variables that had passed the validity
and reliability analysis, namely 21 variable indicators with 40 research
respondents, and processed using IBM SPSS v26 software to obtain data normality
information.
Here are
the results of normality analysis using the Kolmogorov � Smirnov Test:
Table 7 Normality Analysis Results
(Source: SPSS V.26 researcher)
Based on
the results of normality analysis using the Kolmogorov�Smirnov Normality Test,
the significance value of Asiymp. sig was obtained.
(2-tailed) 0.074 > 0.05. So according to the basis of decision-making in the
Kolmogrov-Smirnov normality test above, it can be
concluded that the data are normally distributed. Thus, the assumption or
statement of normality in the regression model has been fulfilled.
D.
Implementation of RII
(Relative Importance Index)
RII is used
as a ranking technique for each statement and compares responses received from
the three respondents, consisting of Owners in the DKI Jakarta Government
involved in project implementation, supervisory consultants involved in project
implementation, and wastewater implementation contractors in DKI Jakarta
Government projects. The calculation of RII from the results of processing
respondent data and the calculation of RII from the factors that cause the
Variation Order in the DKI Jakarta government wastewater project can be seen in
Table 8.
Table 8
Factors that Cause Variation Order in DKI Jakarta
Government wastewater project
10 |
Delay in the shop drawing approval by consultants and owners. (X3.4) |
0,607 |
8 |
0,760 |
4 |
0,800 |
1 |
0,650 |
4 |
11 |
Significant volume differences between drawings, field conditions, and
the Bill of Quantity. (X3.5) |
0,620 |
5 |
0,640 |
9 |
0,680 |
6 |
0,630 |
9 |
From the table of RII calculation
results on 11 variable factors that cause the Variation Order in the DKI
Jakarta government wastewater project, 5 dominant factors are made to cause the
Variation Order in the DKI Jakarta government wastewater project in Table 9.
Table 9
Dominant factors that cause Variation Order in DKI
Jakarta Government wastewater project
No |
������� ������Contractor |
Consultant |
Service User / Owner |
�
1 |
Design changes |
Design changes |
Delay in the shop drawing approval �by consultants and owners |
�
2 |
The owner instructs additional work |
Incomplete/unclear
details of the initial tender drawings |
Design changes |
�
3 |
Project owner
request for optimization of building functions |
A mismatch between image and field conditions |
Incomplete/unclear details of the initial
tender drawings |
�
4 |
Technical specifications that are not clearly
stated in the RKS, bill of quantity, and tender drawings |
Delay in the shop
drawing approval �by consultants
and owners |
The owner instructs
additional work |
�
5 |
Significant volume differences between
drawings, field conditions, and Bill of Quantity |
The owner instructs additional work |
Addition or subtraction of work items |
The
calculation of RII from the results of processing respondent data for the effect
of Variation Order on service providers/contractors can be seen in Table 10.
Table 10
Effect of Variation Order on service provider/contractor
No |
Effect of Variation Order |
Contractor |
Consultant |
Service User/owner |
Cumulative |
||||
RII |
Level |
RII |
Level |
RII |
Level |
RII |
Level |
||
1 |
Poor material control. (X4.2) |
0,567 |
9 |
0,480 |
�������� 9 |
0,600 |
6 |
0,560 |
9 |
2 |
Lack of contractor teamwork in the implementation of work.
(X4.3) |
0,580 |
7 |
0,600 |
�������� 7 |
0,600 |
6 |
0,585 |
7 |
3 |
Changes in working methods. (X4.4) |
0,607 |
4 |
0,840 |
� �������3 |
0,680 |
5 |
0,645 |
4 |
4 |
Less quick decision-making by contractors. (X4.6) |
0,627 |
3 |
0,680 |
�������� 6 |
0,560 |
10 |
0,625 |
6 |
5 |
Poor project
management. (X4.7) |
0,733 |
1 |
0,800 |
������� 4 |
0,720 |
4 |
0,740 |
1 |
6 |
Disruption
of cash flow of service providers/contractors. (X4.8) |
0,567 |
9 |
0,480 |
������� 9 |
0,600 |
6 |
0,560 |
9 |
7 |
Project profit/profit goes up. (X4.9) |
0,580 |
7 |
0,600 |
������� 7 |
0,600 |
6 |
0,585 |
7 |
8 |
Project profit/profit falls. (X4.10) |
0,593 |
5 |
0,880 |
�����
1 |
0,760 |
2 |
0,650 |
3 |
9 |
The quality of work dropped. (X4.11) |
0,660 |
2 |
0,880 |
����
���1 |
0,760 |
2 |
0,700 |
2 |
10 |
The quality of work is up. (X4.12) |
0,587 |
6 |
0,760 |
������� 5 |
0,800 |
1 |
0,635 |
5 |
From the
table of RII calculation results on 10 variables of the influence of Variation
Order on service providers/contractors, then 5 dominant influences of Variation
Order on service providers/contractors are made in table 11.
Table 11
Effect of Dominant Variation Order on the Contractor
No |
����������
Contractor |
Consultant |
Service User / Owner |
|
� 1 |
Poor project management |
Quality of work drops |
Quality of work goes up |
|
� 2 |
Quality of work drops |
Project profit drops |
Quality of work drops |
|
�� 3 |
� Less quick decision-making by contractors |
Changes
in work methods |
Project profit/profit drops |
|
� 4 |
Changes in work methods |
Poor project management |
Poor project management |
|
� 5 |
Project profit/profit drops |
Quality of work goes up |
Quality of work goes up |
From the
table of RII calculation results on 11 variables of dominant factors that cause
the existence of Variation Order and 10 variables of the dominant influence of
Variation Order on service providers/contractors, a cumulative table of
calculations is then made in Table 12.
Table 12
Cumulative dominant factors that cause Variation Order
and cumulative dominant influence of Variation Order on service providers/contractors
No |
The dominant factors that cause the existence of Variation Order are cumulatively |
The dominant influence of Variation Order is cumulatively |
�1 |
Design changes. �(X3.3) |
Poor project management. (X4.7) |
�2 |
The owner instructed additional work. (X2.2) |
The quality of work dropped. (X4.11) |
�3 |
Details of the initial tender drawings are
incomplete / less clear. (X3.2) |
Project profit/profit falls.
(X4.10) |
�4 |
The project owner
requests for optimization of building functions. (X2.1) |
Changes in working methods.
(X4.4) |
�5 |
Delay in the shop drawing approval by
consultants and owners. (X3.4) |
The quality of work is up. (X4.12) |
The results of survey research to
determine the dominant factors causing the occurrence of Variation Order and
the most dominant influence of Variation Order through the calculation of
cumulative RII from the three correspondent parties are as follows:
The top five ranks of the
cumulative RII calculation of the dominant factors causing the Variation Order
are; (a) Design changes. (X3.3) with an RII value of 0.715; (b) The owner
instructs additional work. (X2.2) with an RII value of 0.686; (c) Details of
the initial tender drawings are incomplete/unclear. (X3.2) with an RII value of
0.665; (d) Differences in project owner requests for optimization of building
functions. (X2.1) with an RII value of 0.650; (e) Delay in approval of shopdrawing by consultants and owners (X3.4) with an RII
value of 0.650.
The top five ranks of the
cumulative RII calculation of the dominant influence of the Variation Order
are; (1) Poor project management. (X4.7) with an RII value of 0.740; (2) The
quality of work has fallen. (X4.11) with an RII value of 0.700; (3) Project
profit/profit decreased (X4.10) with an RII value of 0.650; (4) Changes in working
methods. (X4.4) with an RII value of 0.645; (5) The quality of work goes up.
(X4.12) with an RII value of 0.635;
CONCLUSION
Based on the results of the discussion and data analysis to
answer the formulation of the problem in this study, the following conclusions
can be drawn:
The dominant factors causing Variation Order in wastewater
projects in the DKI Jakarta Government are (1) Design changes. (2) The owner
instructs additional work. (3) Details of the initial tender drawings are
incomplete/unclear. (4) Differences in project owner requests for optimization
of building functions. (5) Delay in approval of shop drawing by consultants and
owners.
The dominant influence of Variation Order on service
providers/contractors is () Poor project management. (b) The quality of work
has fallen. (c) Project profit decreases. (d) Changes in work methods. (e)
Quality of work goes up.
BIBLIOGRAPHY
Assbeihat, Jamal M. (2017). Factors
Affecting Change Orders In Public Construction Projects. (December).
Budiutomo, Suryadi, Santoso, Nugroho Adhi,
& Hakim, Arif Rohman. (2021). Sistem Informasi E-Commerce pada Toko L-One
Komputer Tegal Berbasis Website. Jurnal Indonesia Sosial Teknologi, 2(01),
39�50.
Gumolili, S., Sompie, B., & Rantung, J.
(2012). Analisa Faktor-Faktor Penyebab Change Order Dan Pengaruhnya Terhadap
Kinerja Waktu Pelaksanaan Proyek Konstruksi Di Lingkungan Pemerintah Provinsi
Sulawesi Utara. Jurnal Ilmiah Media Engineering, 2(4), 98522.
Gumolili, Sandy A., Dinas, Staf, &
Pemprov, Energi. (2012). ANALISA FAKTOR-FAKTOR PENYEBAB CHANGE ORDER DAN
PENGARUHNYA TERHADAP KINERJA WAKTU PELAKSANAAN PROVINSI SULAWESI UTARA. 2(4).
Hardjomuljadi, Sarwono. (2016). Variation
order, the causal or the resolver of claims and disputes in the construction
projects. International Journal of Applied Engineering Research, 11(14),
8128�8135.
Khalim, Muhammad Abdul. (2020). Analisis
Contract Change Order Pada Pelaksanaan Proyek Konstruksi.
Martanti, Ana Yuni. (2018). ANALISIS
FAKTOR PENYEBAB CONTRACT CHANGE ORDER DAN PROYEK KONSTRUKSI PEMERINTAH. 7(1),
32�42.
Martanti, Ana Yuni Yuni. (2019). Analisis
Faktor Penyebab Contract Change Order dan Pengaruhnya Terhadap Kinerja
Kontraktor Pada Proyek Konstruksi Pemerintah. Rekayasa Sipil, 7(1),
32�42.
Memon, Aftab Hameed, Rahman, Ismail Abdul,
Faris, Mohamad, & Hasan, Abul. (2014). Significant Causes and Effects of
Variation Orders in Construction Projects. (November).
https://doi.org/10.19026/rjaset.7.826
Mitra, Jurnal, Sipil, Teknik, Junius,
Alexander, Studi, Program, Teknik, Sarjana, Tarumanagara, Universitas, Studi,
Program, Teknik, Sarjana, & Tarumanagara, Universitas. (2020). DAMPAK
CHANGE ORDER PADA PROYEK PERKERASAN JALAN. 3(1), 199�206.
Mohammad, Noraziah, & Hamzah, Zabidi.
(2019). A review of causes of variation order in the construction of terrace
housing projects in. 03013.
Muluk, Mafriyal, Misriani, Merley, Atmaja,
Jajang, Ali, Syaifullah, & Monica, Mona. (2018). Identifikasi
Faktor-Faktor Penyebab Change Order pada Proyek Konstruksi Jalan di Sumatera
Barat. XV(2), 77�87.
Nurmala, Ade. (n.d.). PENYEBAB DAN
DAMPAK VARIATION ORDER ( VO ) PADA PELAKSANAAN Akibat adanya perubahan
pekerjaan seringkali menimbulkan masalah di pihak penyedia jasa terlebih kedalam
pekerjaan yang menjadi semakin rumit . Berikut ini faktor-faktor penyebab dari
perubahan p. 63�77.
Sugiyono, Dr. (2013). Metode penelitian
pendidikan pendekatan kuantitatif, kualitatif dan R&D.
Syal, M., & Bora, M. (2016). Change
order clauses in standard contract documents. Practice Periodical on
Structural Design and Construction, 21(2), 4015021.
https://doi.org/10.1061/(asce)sc.1943-5576.0000281
Tenno, Zen, & Suroso, Agus. (2021).
Analisis Faktor Penyebab CCO dan Pengaruhnya Terhadap Biaya Kontraktor Pada
Proyek Jalan Tol. Jurnal Aplikasi Teknik Sipil, 19(3), 335. https://doi.org/10.12962/j2579-891x.v19i3.9537
Wali, Khalil Ismail, & Saber, Nazik Imad.
(2019). An Analysis of Causes and Factors Affecting Change Orders Occurrence in
Construction Projects in Iraq. Zanco Journal of Pure and Applied Sciences,
31(6). https://doi.org/10.21271/zjpas.31.6.1
Zano, Bobby Roy. (2019). Analisis pengaruh
kualitas produk, harga dan iklan terhadap keputusan pembelian sepeda motor
Yamaha pada PT Surya Timur Sakti Jatim Surabaya. Agora, 7(1).
Muhammad Riska Dirgantoro,
Sarwono Hardjomuljad (2023) |
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