95
Analysis of Stakeholder Management in the Jakarta- Bandung High Speed Train Project on the Project Environment of 1st Section Area (DK 4 to DK 40) Based
on PMBOK 6th Edition
Ali Sunandar, Anjas Handayani, Mohammad Sobirin, and Yuwono Anggoro Faculty of Engineering, Mercu Buana University, Indonesia
ali.sunandar@gmail.com,anjas.handayani@gmail.com , sobirinmkj2018@gmail.com, yuwonoanggoro@gmail.com
Abstract
The High Speed Railway project between Jakarta and Bandung, which starts from Halim to Tegal Luar approximately 142.3 km, with most of which will use the toll road route will be carried out under the Business to Business scheme by BUMN. The construction of the Jakarta - Bandung Fast Train project in the section 1 area has experienced work stoppages due to the environmental impact on the area around the project. This study aims to determine the stakeholders and their dominant factors that influence the environmental management of the Jakarta - Bandung Fast Train project. The analysis of these stakeholders uses statistical software to determine the significant influence between the dependent and independent variables. Then, a pattern of work relations between stakeholders is arranged so that it can improve environmental management. The results of the study indicate that there are 5 out of 25 stakeholders who have a significant effect on work environment management. After that, explained the procedures for improving the work relationship pattern of stakeholder management for environmental management to make it better based on PMBOK 6th Edition.
Keywords
Environment, Fast Train Jakarta - Bandung, PMBOK, Stakeholders, , Statistical Software.
1. Preliminary
The High Speed Railway project between Jakarta and Bandung, which starts from Halim to Tegal Luar approximately 142.3 km, with most of which will use the toll road route will be carried out under the Business to Business scheme by BUMN. The Jakarta - Bandung Fast Train Project is one type of government-business cooperation project implemented in the BOT (Build, Operate, Transfer) model.
The Jakarta-Bandung Fast Train construction project was carried out without making a Strategic Environmental Assessment (KLHS) first, this is not in accordance with the Environmental Management Law (UUPLH) which states that KLHS was made before Policies, Plans and Programs were carried out. The work for the Jakarta-Bandung High Speed Rail based in Jakarta requires increased supervision as this work has many environmental impacts. Environmental impacts that must be considered are not structured drainage channels or sewage channels so that the impact of flooding. Many of the works carried out by the contractors damaged the city's drainage system, causing rainwater to flood. Damage to drainage is caused by being buried by excavated soil, bore pile discarded soil, and construction of pile cap construction works.
Apart from the absence of temporary drainage, the fast train project also does not pay attention to utilities so that there are many problems with PLN and PDAM in the work area. Land disposal from the bore pile work has also caused the toll road to become contaminated by heavy vehicles that enter and exit the project. The construction implementation problems inspired the author to write a final project entitled "Stakeholder Management Analysis of the Jakarta - Bandung Fast Train Project on the Project Environment Area Section 1 (DK 4 to DK 40) Based on PMBOK 6th Edition". The purpose of this study is to determine the most influential stakeholders in environmental management of the Jakarta-Bandung Fast Train project; knowing the dominant factors of each stakeholder that influence the environmental management of the Jakarta-Bandung Fast Train project; as well as analyzing how stakeholder management improves the relationship between stakeholders to improve environmental management of the Jakarta-Bandung Fast Train project.
2. Research Methodology
The research methodology is a series of activities or procedures used in conducting a study. This type of research is a quantitative research. The object of this research, namelystakeholder or stakeholders in the Jakarta - Bandung Fast Train Project with sample collection technique using convenience sampling technique.
96
The data used in this study are divided into two, namely primary and secondary data. Primary data is obtained from stakeholders and workers who play a role in the Jakarta-Bandung Section 1 High Speed Train project using:
1) Questionnaire (questionnaire) 2) Interview (interview)
3) Meanwhile, secondary data in this study were obtained from literature studies, including:
4) Book 5) Journal 6) Internet
7) Previous research 2.1. Data Collection
2.1.1. Data Collection 1st Phase (First Expert Validation)
The first stage questionnaire contains the identification of influential stakeholders and the dominant factors of each stakeholder obtained from literature studies. The questionnaire is given to experts for verification, clarification, and validation. Experts are asked whether they agree or disagree with these variables and fill in the information fields for input on each variable. In addition, experts are also asked to fill in additional variable columns if there are additional variables.
2.1.2. Data Collection 2nd Phase (Pilot Survey)
After conducting the first stage questionnaire and obtaining the variables from expert validation, data collection was carried out through the second stage questionnaire. The second stage of data collection is a pilot survey, namely using a questionnaire using an online questionnaire to prospective respondents to get an opinion on whether these variables are easy to understand or simplification is still needed. In addition, the pilot survey is also a test of the questionnaire in order to get refinement before the questionnaire is submitted to the real respondents.
2.1.3. Data Collection 3rd Phase (Respondents)
The questionnaire distributed in the respondent's questionnaire or Phase III data collection is the result of a pilot survey questionnaire that has been improved so that each variable of interest in the questionnaire is easily understood and understood by the respondent. Respondents at the third stage of data collection are people who are directly involved in the construction of the Jakarta-Bandung Fast Train Project.
2.1.4. Data Collection 4th Phase (Final Expert Validation)
After data analysis is carried out, the results obtained are then returned to the experts to be finally validated at the final stage. The expert will decide whether the predetermined factors are valid or not. Data collection at this stage was carried out by interviewing and filling out forms. Interviews were conducted to obtain expert opinion regarding the results of research and further discussion on the relationship between stakeholders and environmental management. Interviews were conducted in person or indirectly through online liaison applications.
2.2. Data Analysis 2.2.1. Validity test
Validity is a measure that shows the level of validity or validity of an instrument. An instrument that is valid or valid has high validity, whereas an instrument that is less valid means having low validity. An instrument is said to be valid if it is able to measure what is desired and to reveal data from the variables under study appropriately. The basis for decision making if r countr table, then the items in the questionnaire are declared valid. Researchers used statistical calculation programs in the validity test.
2.2.2. Reliability Test
(Wiratna Sujarweni, Utami, and Adams 2019)reliability is the reliability of a measurement that shows the extent to which the measurement is unbiased (error free). And therefore it ensures consistent measurement across time and across various items in the instrument. In other words, the reliability of a measurement is an indication of the stability and consistency with which the instrument measures concepts and helps judge the accuracy of a measurement.Reliability analysis that is commonly used is the Cronbach Alpha (C-alpha) analysis and with the help of statistical calculation programs.
Cronbach Alpha (C-alpha) formula:
97 Information :
r = Instrument Reliability Value k = Number of Question Items σ_b^2 = Variation of Grains σ_1^2 = Total Variation
According to Romie (2020) the basis for decision making in the reliability test is interpreted as follows:
1. If the Cronbach Alpha value is> 0.70 then the questionnaire or questionnaire is declared reliable or consistent.
2. If the Cronbach Alpha value <0.70, the questionnaire or questionnaire is declared unreliable or inconsistent
2.2.3. Normality test
According to the normality test is a test carried out with the aim of testing whether in the regression model, confounding or residual variables have a normal distribution. As it is known, the t and F tests assume that the residual value follows a normal distribution. If this assumption is violated, the statistical test becomes invalid for a small sample size. On the basis of decision making if the significance value (Sig.)> 0.05, the data is normally distributed and if the significance value (Sig.) <0.05, then the data is not normally distributed.
2.2.4. Heteroskedesticity test
The heteroscedasticity test aims to test whether in the regression model there is an inequality of variance from the residuals of one observation to another. If the residual variance from one observation to another is constant, it is called homoscedasticity and if it is different it is called heteroscedasticity. A good regression model is one that is homoscedastic or does not occur heteroscedasticity (Priyastama 2020)
The basis of analysis is as follows:
1. If there is a certain pattern, for example, the dots form a certain regular pattern (wavy, widened then narrowed), then it indicates that heteroscedasticity has occurred.
2. If there is no clear pattern, and the dots spread above and below the 0 on the Y axis, there is no heteroscedasticity.
2.2.5 Multicolonierity Test
According to , the multicolonierity test aims to test whether the regression model finds a correlation between independent variables. A good regression model, there should be no correlation between the independent variables, if the independent variables are correlated, the variables are not orthogonal.
Multicolonierity can also be seen from (1) tolerance value and its counterpart, (2) variance inflation factor (VIF). These two measures show which independent variable is explained by the other independent variables. In simple terms, each independent variable becomes the dependent variable and regresses to the other independent variables. Tolerance measures the variability of the selected independent variable that is not explained by other independent variables. So a low tolerance value is the same as a high VIF value (because VIF = 1 / Tolerance). The cut off value that is commonly used to show multicolonierity is the Tolerance value ≤ 0.10 or the same as the VIF value ≥ 10.
2.2.6. Autocorrelation Test
the autocorrelation test aims to test whether in the linear regression model there is a correlation between confounding errors in period t with confounding errors in period t -1 (previous). In this study, to test the presence or absence of autocorrelation symptoms using the Durbin-Watson test (DW test). The Durbin - Watson (DW) test is used for level one autocorrelation, and requires a constant in the regression model and no more variables between the independent variables.(Ghozali 2018)
2.2.7. F Statistical Test (Model Feasibility)
The F test basically shows whether all the independent variables have a joint influence on the bound variable. This test also uses a significance level of 5% or 0.05(Ghozali 2018).
JIf the F value is greater than 4, then Ho can be rejected at the 5% degree of confidence, in other words the alternative hypothesis (H1) is accepted, which states that all independent variables simultaneously and significantly affect the dependent variable.
98 2.2.8. Hypothesis Test (T Test)
The T test basically shows how far the influence of one explanatory variable or independent variable individually in explaining the variation of the dependent variable(Ghozali 2018) . Secontinued states the hypothesis formula as follows:
1. The null hypothesis (H0) is whether the parameter of a variable (bi) is equal to zero, or H0: bi = 0Which means if an independent variable is not a significant explanation for the dependent variable.
2. The alternative hypothesis (H1) is whether the parameter of a variable (bi) is not equal to zero, or:
H1: bi ≠ 0 Which means if the independent variable is a significant explanation of the dependent variable.
2.2.9. Determination Test
(Priyastama 2020)states that the coefficient of determination (R2) essentially measures how far the model's ability to explain variations in the dependent variable. The value of the coefficient of determination is between 0 and 1. Small R2 means that the ability of the independent variables to explain the variation in the dependent variable is very limited. However, if the R2 value approaches 1, this indicates that the independent variables provide almost all the information needed to predict the variation in the dependent variable.
The coefficient of determination of zero means that the independent variable has no effect on the dependent variable at all. When it approaches one, it means that the independent variable has more influence on the dependent variable. The value of R2 is in the interval 0≤ R2 ≤ 1. Logically, the better the model estimate is in describing the data, the closer R is to the value 1 (one). The value of R2 can be obtained by the formula:
R2 = (r) 2 x 100%
Where:
R2 = Coefficient of determination r = Correlation coefficient 3. Data Collection and Analysis 3.1. Data Collection
The types of data used in this study are:
1.Primary data, namely data obtained from questionnaires and interviews. In accordance with the discussion in Chapter 3 regarding the data collection method in this study, it consists of several stages.
2.Secondary data, namely data obtained from literature studies such as books, journals, and previous research results related to this research. Secondary data aims to identify research variables.
Data collection methods in this study are:
1. Data collection 1st Phase (Initial Expert Validation) 2. Data collection 2nd Phase (Pilot Survey)
3. Data collection 3rd Phase (Respondents)
4. Data collection 4th Phase (Final Expert Validation) 3.2. Data Analysis
The validity test is carried out by comparing the calculated r value with the r table (attached) for degree of freedom (df) = n - 2, in this case n is the number of samples in this study, yaiu (n) = 101. Then the amount of df can be calculated with 101-2 = 99. With df = 99 and a significance value of 5% obtained r table = 0.165. The rules that apply if r count> r table (0.165), then the research instrument in the questionnaire can be said to be valid.
The following are the results of the validity test of the stage 3 questionnaire results (respondents):
99
Table 1. Variable X Test Results
Table 2. Variable Y Test Results
From the table above it can be seen that of the 25 variables X and one variable Y. The results of the validity of all variables are valid because R count> R Table.
3.2.2. Reliability Testing
The basis for decision making in the Cronbach's alpha reliability test is as follows:
1. If the Cronbach's Alpha value is> 0.7 then the questionnaire is declared reliable or consistent 2. If the Cronbach's Alpha value <0.7, the questionnaire is declared unreliable or inconsistent
The following are the results of the reliability test on the recapitulation of the results of the stage 3 questionnaire (respondents):
Table 3. Reliability Test Results Influence of Stakeholders
Reability Statistics Cronbach's Alpha N of items
0.873 26
Table 4. Effect Reliability Test Results
Dominant Factors of Stakeholders Reability Statistics Cronbach's Alpha N of items
0.947 89
Thus from the table above Cronbach's Alpha value> 0.7 then the questionnaire is declared reliable or consistent. The reliability results of all variables are reliable because all Cronbach's Alpha values meet the parameters.
No. Variable R table
R count
Valid / No
1 X1 0.165 0.368 VALID
2 X2 0.165 0.565 VALID
3 X3 0.165 0.517 VALID
4 X4 0.165 0.211 VALID
5 X5 0.165 0.565 VALID
6 X6 0.165 0.396 VALID
7 X7 0.165 0.239 VALID
8 X8 0.165 0.482 VALID
9 X9 0.165 0.496 VALID
10 X10 0.165 0.237 VALID 11 X11 0.165 0.234 VALID 12 X12 0.165 0.519 VALID 13 X13 0.165 0.468 VALID 14 X14 0.165 0.274 VALID 15 X15 0.165 0.209 VALID 16 X16 0.165 0.295 VALID 17 X17 0.165 0.215 VALID 18 X18 0.165 0.510 VALID 19 X19 0.165 0.608 VALID 20 X20 0.165 0.435 VALID 21 X21 0.165 0.486 VALID 22 X22 0.165 0.634 VALID 23 X23 0.165 0.524 VALID 24 X24 0.165 0.546 VALID 25 X25 0.165 0.408 VALID
No. Variable R table
R
count Valid / No
1 Y 0.165 0.499 VALID
100 3.2.3. Normality Testing
In this study, a normality test was carried out by looking at the Kolmogorov - Smirnov value with the help of statistical software. The basis for decision making in the Kolmogorov - Smirnov normality test is as follows:
1. If the significance value (Sig.)> 0.05, the research data is normally distributed 2. If the significance value (Sig.) <0.05, the research data is not normally distributed The following are the results of normality testing on the primary data of researchers,
Table 5. Normality Test Result Influence of Stakeholders
Table 6. Normality Test Results of Dominant Factors stakeholder
Based on the results of the statistical software program output for the normality test in the table above, it can be concluded that: The Asiymp.Sig (2-tailed) significance value is 0.250 and 0.100 with a significance requirement greater than 0.05. So in accordance with the basis of decision making in the Kolmogorov-Smirnor normality test, it can be concluded that the data is normally distributed.
3.2.4 .Multicolinearity Testing / Variance Inflation Factor (VIF)
Tolerancemeasure the variability of the selected independent variable that is not explained by other independent variables. So a low tolerance value is the same as a high VIF value (because VIF = 1 / Tolerance).
The cut off value that is commonly used to show multicollinearity is the Tolerance value ≤ 0.10 or equal to the VIF value ≥ 10. The following are the results of the multicollinearity test:
One-Sample Kolmogorov-Smirnov Test Unstandardized
R sidual
N 101
Normal Parametersa,
b
Mean .0000000
Std.
Deviation .24951140 Most
Extreme Differences
Absolute .095 Positive .071 Negative -.095 Statistical Test .095 Asymp. Sig. (2-tailed) .250c
a. Test distribution is Normal.
b. Calculated from data.
c. Lilliefors Significance Correction.
One-Sample Kolmogorov-Smirnov Test Unstandardized
Residual
N 101
Normal Parametersa,
b
Mean .0000000
Std.
Deviation .19017280 Most Extreme
Differences
Absolute .121
Positive .121
Negative -.079
Statistical Test .121
Asymp. Sig. (2-tailed) .100c a. Test distribution is Normal.
b. Calculated from data.
c. Lilliefors Significance Correction.
101
Table 7. Multicolinearity Test Result the Influence of Dominant Stakeholder
Table 8. Multicolinearity Test Results Factors stakeholder
The VIF results from the classical assumption test on constant variables are still between 1 to 10 so there is no multicollinearity.
3.2.5. Autocorrelation Testing
Detect autocorrelation using the Durbin Watson value. If the Durbin Watson value is between -2 to 2 there is no autocorrelation. Following are the results of the autocorrelation test:
Table 9. Stakeholder Autocorrelation Test Results Coefficientsa
Model
Collinearity Statistics Tolerance VIF 1 (Constant)
X01 .489 2,043
X03 .286 3,501
X07 .838 1,193
X15 .791 1,264
X16 .367 2,727
X20 .185 5,401
a. Dependent Variable: Y
Coefficientsa Model
Collinearity Statistics Tolerance VIF 1 (Constant)
X4.8 .578 1,729
X7.2 .504 1,982
X7.3 .587 1,705
X10.1 .576 1,735
X10.2 .552 1,812
X12.5 .542 1,844
X15.3 .387 2,586
X15.4 .519 1,926
X21.1 .596 1,678
X23.1 .600 1,665
X25.1 .450 2,221
X25.2 .410 2,439
a. Dependent Variable: Y
Model Summaryb
Model R R Square Adjusted R Square Std. Error of
the Estimate
Durbin- Watson
1 .845a .713 .695 .343 1,999
a. Predictors: (Constant), X b. Dependent Variable: Y
102
Table 10. Autocorrelation Test Results of Dominant Stakeholder Factors Model Summaryb
Model R R Square
Adjusted R Square
Std. Error of the Estimate
Durbin- Watson
1 .965a .931 .792 .331 1,971
a. Predictors: (Constant), X b. Dependent Variable: Y
The results of the Durbin Watson values of 1,999 and 1,971 mean that the value is still between -2 to 2 which means that there is no autocorrelation.
3.2.6. Heteroskedesticity Testing
A good regression model is homoscedasticity or heteroscedasticity does not occur. The condition is if the Sig value> 0.05 then there is no heterokesdasticity. The following are the results of the heterokesdasticity test:
Table 11. Stakeholder Heteroscedesticity Test Results Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
1 (Constant) -529 .430 1,231 .221
X01 -002 .069 -004 -.026 .980
X03 -109 .088 -.251 -1,245 .216
X04 .415 .118 .892 1,527 .100
X15 -353 .114 -740 3,089 .320
X16 .015 .068 .033 .224 .823
X17 .043 .072 .066 .593 .555
X20 .110 .114 .224 .960 .339
a. Dependent Variable: abs_res
Table 12. Results of the Stakeholder Dominant Heteroscedicity Test Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 4,534 .440 10,306 .000
X4.8 .101 .090 .153 1,122 .265
X7.2 .029 .100 .042 .287 .775
X7.3 .029 .099 .039 .292 .771
X10.1 .063 .081 .106 .780 .438
X10.2 .127 .090 .196 1,409 .162
X12.5 .007 .081 .013 .090 .929
X15.3 .010 .116 -.015 -.088 .930
X15.4 .037 .091 -.058 .404 .688
X21.1 -.045 .091 -.066 -493 .623
X23.1 .008 .093 .012 .089 .929
X25.1 -.005 .110 -007 -.048 .962
X25.2 .120 .106 .183 1,133 .260
a. Dependent Variable: abs_res
The results of the stakeholder table and the dominant stakeholder factors show that the Sig. > 0.05, it is not significant so it can be concluded that the regression model does not have heterokesdasticity.
3.2.7 Model Feasibility Testing (F Test)
The F test on multiple linear regression is used to see the feasibility of the regression model Has it met the eligibility of the model?
H0 = Does not meet the eligibility of the model H1 = Meets model eligibility
103
The condition is if sig> 0.05 then H0 is accepted and if sig <0.05 then H0 is rejected. The following are the results of the F test on stakeholders and sub variables on the project environment
Table 13. F-Test Results Stakeholder influence ANOVAa
Model Sum of
Squares
df Mean Square
F Sig.
1 Regression 32,448 24 1,352 16,505 .000b Residual 6,226 76 .082
Total 38,673 100
a. Dependent Variable: Project Environment b. Predictors: (Constant),
With the conditions listed above that the Sig. 0.000b less than 0.05 means that H0 is rejected that the data meets the model's feasibility.
Table 14. F-Test Results of the Influence of Dominant Stakeholder Factors ANOVAa
Model
Sum of Squares df
Mean
Square F Sig.
1 Regression 49,017 67 .732 6,676 .000b Residual 3,617 33 .110
Total 52,634 100
a. Dependent Variable: Y b. Predictors: (Constant),
With the conditions listed above that the Sig. 0.000b less than 0.05 means that H0 is rejected that the data meets the model's feasibility.
3.2.8 Model Feasibility Testing (F Test)
The hypothesis is the researcher 's guess on the relationship of the variables studied. The research hypothesis in this study is:
a. H0 = There is no significant influence of stakeholder management on the Jakarta - Bandung fast train project on problems that occur in the project environment.
b. H1 = There is a significant influence of stakeholder management on the Jakarta - Bandung fast train project on problems that occur in the project environment.
c. H0 = There is no significant influence of dominant factors from stakeholders in the Jakarta - Bandung fast train project on problems that occur in the project environment.
d. H1 = There is a significant influence on the dominant factors of stakeholders in the Jakarta - Bandung fast train project on problems that occur in the project environment.
The decision making method is if Sig> 0.05 then H0 is rejected and if Sig <0.05 then H0 is accepted. The following are the results of the T test on the effect of X on Y:
1. Based on the results of the Stakeholder Influence Hypothesis Test, there are 5 stakeholders of the Jakarta Bandung Fast Train project that significantly influence the problems in the project environment, namely X03, X04, X12, X15, and X20 with the results of Sig <0.05. For other stakeholders having a Sig> 0.05, H0 it is accepted that there is no significant influence of stakeholder management on the Jakarta - Bandung fast train project on problems that occur in the project environment
The regression equation is:
Y = 0.548 + 0.179X3 + 0.121X4 + 0.160X12 + 0.161X15 + 0.133X20 + e.
Where:
Y = Project Environment;
X03 = HSRCC (High Speed Railway Contractor Consortium);
X04 = PT. Wijaya Karya (Persero) Tbk;
X12 = PT. Victory Utama Karya;
X15 = PT. China West Development Indonesia;
X20 = Communities around the KCJB section 1 project area.
104
The formula above shows that X has a positive effect with its respective values on Y, then the higher X the higher Y. It can be said that the higher the project stakeholder management, the more influential it is on project environmental management.
2. Based on the results of the hypothesis test of dominant factors, there are 6 dominant factors from each stakeholder of the Jakarta-Bandung Fast Train project which significantly influence the problems in the project environment, namely X16, X19, X22, X63, X67 and X76 with the results of Sig <0.05. For other stakeholders having a Sig> 0.05, H0 it is accepted that there is no significant influence of dominant stakeholder factors in the Jakarta - Bandung fast train project on problems that occur in the project environment.
The regression equation is:
Y = 1.059 + 0.525X16 + 0.797X19 + 0.371X22 + 0.162X63 + 1.225X67 + 0.154X76 + e Where:
Y = Project Environment;
X16 = X3.6 = Pay attention to the utility in the project area (HSRcc);
X19 = X4.2 = protecting the environment in the project implementation process (Wika 2016) X22 = X4.5 = Maintaining the quality and performance of project implementation(Wika 2016)
X63 = X13.4 = Coordination and communication between stakeholders regarding the project environment (PT. Trocon);
X67 = X14.2 = Coordination and communication between stakeholders regarding the project environment (PT. Wika Beton);
X76 = X17.1 = Providing regulations to the project implementation contractor to carry out environmental treatment and care (Kementrian Lingkungan Hidup).
The formula above shows that X has a positive effect with its respective values on Y, then the higher X the higher Y. It can be said that the higher the project stakeholder management, the more influential it is on project environmental management.
3.2.9 Coefficient of Determination
The following are the results of the determination coefficient test for the variables that have been determined by the researcher:
Table 15. Determination Coefficient Test Results Influence of Stakeholders.
Table 16. Determination Coefficient Test Results Dominant Factors of Stakeholders
he independent variable (X) or stakeholder and the dominant factor is more than 2, then the reading in the Adjusted R Square table is 0.788 and 0.792. This means that 78.8% and 79.2% of the environmental problems of the Jakarta - Bandung Fast Train project are influenced by stakeholder management and its dominant factors, so the rest is influenced by other factors that have not been examined in this study.
Model Summaryb
Model R R
Square
Adjusted R Square
Std.
Error of the Estimate
1 .916a .839 .788 .286
a. Predictors: (Constant), b. Dependent Variable: Y
Model Summaryb
Model R R
Square
Adjusted R Square
Std. Error of the Estimate
1 .965a .931 .792 .331
a. Predictors: (Constant), b. Dependent Variable: Y
105 3.3. Discussion of Research Results
From the results of plotting stakeholder data against mapping based on PMBOK, the following results were obtained:
Project Area Community
Figure 1. Stakeholder Mapping
Information about the influence and interests of each stakeholder is combined with a strategic work plan and relationship patterns according to the figure below:
Figure 2. Pattern of stakeholder relations a. Inform.
This level reveals little effort to involve stakeholders in the project. It is necessary to be informed of decisions made that can affect them directly. It is unlikely that they will play an active role in making decisions. However, whether they are to highlight a particular issue with a decision, it is likely that serious consideration will be given back before a decision is made.
b. Consult.
This is a way of keeping stakeholders informed about the project. Since stakeholders with higher in influence but lower in importance should be kept, they should be consulted to seek their opinion and input for important decisions that directly or indirectly affect them. It is unlikely that the strategy will be changed as a result of such consultations, but tactics can be well adapted to maintain a higher level of commitment.
c. Get involved.
Although not very high in influence, stakeholders with standard interests basically need to be involved in all activities in the project according to their interests because they have the power to make decisions that impact the project. Work directly with these stakeholders to ensure that their concerns are consistently understood, considered, and reflected in alternatives developed. As long as their interests are achieved, they remain satisfied and maintain a passive rather than active interest in environmental management of the project.
Project Area Community Project Area Community Project Area Community Project Area Community
Pt Psbi Pt Synohidro
Pt Crec Pt Cdjo Pt Pn Vii
Pt Kai Pt Keller Franki
Pt Victory Pt Trocon Pt Chinawent Ministry Of Environment
Pt Pdam Pt Pln Dishub Satlantas Bpn Bekasi City Bpn Bekasi District
PJR
Project Area Community
106 d. Partner / collaborate.
The stakeholders who have a high degree of influence and importance for the success of the project, they will provide 'coalition support' for the project in planning and implementation. As such, they should be treated as partners to increase their engagement and commitment. This can be achieved by revising and adjusting the project strategy, objectives, and results if necessary to win their support.
4. Conclusion
The conclusions of this study include:
1. The stakeholder management analysis of the Jakarta-Bandung Fast Train project starts from the formulation of real problems, determining the objectives to be achieved, identifying potential parties to play a role in achieving goals and compiling a plan or strategy for how these parties can be integrated so that they are created. conditions that can encourage the achievement of goals, namely environmental management that is better than before.
2. From the analysis stage the statistical results show that:
a. Based on the results of the T Test, there are 5 stakeholders of the Jakarta-Bandung Fast Train project that significantly influence problems in the project environment with the results of Sig <0.05, namely:
a) X03. HSRCC (High Speed Railway Contractor Consortium);
b) X04. PT. Wijaya Karya (Persero) Tbk;
c) X12. PT. Trocon Indah Perkasa;
d) X15. PT. China West Development Indonesia;
e) X20. Communities around the KCJB section 1 project area.
f)
b. Based on the results of hypothesis testing, there are 6 dominant factors in the stakeholders of the Jakarta Bandung Fast Train project which significantly influence the problems in the project environment with the results of Sig <0.05, namely:
a) X3.6. Pay attention to utilities in the project area (HSRcc);
b) X4.2. Protecting the environment in the project implementation process (Wika 2016) c) X4.5. Maintain the quality and performance of project implementation (Wika 2016)
d) X13.4. Coordination and communication between stakeholders regarding the project environment (PT.
Trocon Indah Perkasa);
e) X14.2. Coordination and communication between stakeholders regarding the project environment (PT.
Wika Beton);
f) X17.1. Providing regulations to the project implementation contractor to carry out environmental treatment and care (PT. Kementrian Lingkungan dan Kehutanan).
3. Improvement work plans to improve relationships between stakeholders for improving project environmental management are based on PMBOK 6th edition, 2017 in Knowlegde Area Stakeholder Management, namely:
a) Inform b) Consult c) Get involved
d) Collaborate / Partner
Reference
Ghozali. 2018. Aplikasi Analisis Multivariate Dengan Program Ibm Spss 25 (9thed.). 9th ed. semarang.
Priyastama, Romie. 2020. The Book of SPSS. Yogyakarta.
Wika. 2016. “(PT. Wijaya Karya (Persero) Tbk). Keterbukaan Informasi,.” 1–29.
Wiratna Sujarweni, V., Lila Retnani Utami, and Sony Adams. 2019. The Master Book of SPSS : Pintar Mengolah Data Statistik Untuk Segala Keperluan Secara Otodidak / V. Wiratna Sujarweni, Lila Retnani Utami; Penyunting, Sony Adams. Yogyakarta.
Biography
Ali Sunandar, is a researcher at the Center for Sustainable Infrastructure Development UI (CSID UI) and a lecturer at Mercubuana University. Ali is also active in various organizations such as the Indonesian Transportation Society (MTI), the Indonesian Infrastructure Society (MII) and the Indonesia Economic Forum (IEF). His main interest is the development of sustainable economic and social infrastructure in Jakarta, especially in improving institutional approaches and funding innovations. With the institutional
107
harmonization approach to infrastructure bureaucracy and funding innovation, Jakarta not only has the infrastructure but is also service-oriented to citizens.
Anjas Handayani, Born in Jakarta on March 9, 1977. He works as a lecturer at Mercu Buana University. He graduated with a Bachelor of Civil Engineering from Mercu Buana University in 1999 and obtained a master's degree in Civil Engineering with a concentration in Construction Management from Universitaas Pelita Harapan Jakarta. Currently teaching several subjects such as Project Cost Estimation and Economic Engineering, Construction Management, Construction Quality Management Systems. Until now, he is also actively working in one of the BUMN subsidiaries in Indonesia.
Muhamad Sobirin,Born in Cirebon on January 12, 1960. Lecturer at the Civil Engineering study program at Mercu Buana University. Obtained a Bachelor of Civil Engineering degree from the Jakarta National and Natural Sciences Institute in 1991 with the title of his thesis is Textile Industry Wastewater Planning. Then obtained the title of Master of Civil Engineer with a concentration in Construction Management from Tamajagakarsa University, Jakarta in 2015 with the title Thesis, The Effect of Substructural Work and Finishing on the Quality of Jakarta Projects. He also teaches subjects such as pavement planning, statics, heavy equipment, engineering chemistry, geometry, soil mechanics, material properties, mathematics, engineering chemistry.
Yuwono Anggoro,Born in Cirebon on November 29, 1996. Worked as Quality Control for PT Wijaya Karya High Speed Railway Project. He graduated from the Bandung State Polytechnic Civil Engineering Diploma 3 in 2018 and obtained a Civil Engineering degree from Mercubuana University in 2021. Has worked at PT. Ecosif Multi Kreasi as the site manager for the aluminum louver Cooling Tower and Ventilation Tower MRT Project installation. Until now, he is also actively working in a state-owned company in Indonesia.
