Surakarta is a well-known city in Central Java because many cultural and art festivals are held here. As a developing city, there are always ongoing or planned construction projects. In the process, a project does not always run smoothly. Problems such as schedule changes and cost overrun that will continue with contract change orders often occur especially in large projects that take a long time to complete and have large costs. A contract change order is a written order submitted from the owner to the contractor signed by both the owner and the contractor that is issued after the execution of the initial contract, authorizing changes in work or adjustments in the amount of cost or volume of work as well as time of work.

AIn this study, the researchers do a development from previous research written by Nur Sahid, Ika Setyaningsih, Mochamad Solikin and Bariq Al Salam with the title "Analisis Faktor–Faktor Penyebab Pembengkakan Biaya oleh Kontraktor Proyek Jalan APBD Kota Solo Tahun 2017-2018". Data were collected using the results of a questionnaire from the study, then the data which originally only contained the variable of factors causing cost overrun were separated into factors affecting cost overrun and contract change orders based on Presidential Regulation of the Republic of Indonesia Number 16 Year 2018 Concerning Procurement of Goods / Services Article 54 -55. Those data then processed further using the SPSS (Statistic Product and Service Solutions) program to obtain the title of the study "Analysis Factors Of Cost Overrun AndContractChangeOrderImpactOnRoadConstructionProjectIn Surakarta Apbd 2017-2018". No one has done research related to the analysis of the effect of cost overrun on contract change orders on road projects in Surakarta, therefore it is important to conduct this research so that contractors related to road construction projects can make decisions, and the right solutions to overcome or avoid various causes contract changes that can result in losses.         

Figure 1: Research Method Flow Chart

This Chapter will explain about the results of this study, which starts from data collection using a questionnaire from a research written by Nur Sahid, Ika Setyaningsih, Mochamad Solikin and Bariq Al Salam with the title "Analisis Faktor–Faktor Penyebab Pembengkakan Biaya oleh Kontraktor Proyek Jalan APBD Kota Solo Tahun 2017-2018". Data from the questionnaire that originally contained variables about cost overrun were further developed in order to obtain variables regarding contract change orders using Presidential Regulation of the Republic of Indonesia Number 16 of 2018 concerning Procurement of Goods / Services Articles 54-55 (Figure 1).

Validity Test

This research was conducted with a total sample of 50 projects and with an error rate of 5%. From these data finally obtained rTable value of 0.276. Based on the results obtained after conducting a validity test, all independent variables (X) have the value of rcount>rTable and the value of sig.<5%, so it can be said that all variables are valid and can be used in this study (Table 1-2).

Reliability Test

If Cronbach's Alpha value>0.6, the research instrument can be said to be reliable. Conversely, if the Cronbach's Alpha value<0.6, then the research instrument cannot be said to be reliable. The results of the study can be said to be reliable if there are similarities of data at different times. After all the questionnaire results are declared valid, the next analysis is to perform a Cronbach’s Alpha reliability test>0.6 (Table 3 and Table 4).

Classical Assumption Test 

Normality Test: Normality test is carried out to find out whether the data to be used as supporting research is normally distributed or not. To determine whether a variable is normal or not, then Kolmogorov- Smirnov is a formula used in this normality test (Table 5-6).

The normality test shows Asym. Sig. (2-tailed) = 0.200> 0.05, indicating that the residual data is normally distributed. This confirms the data meets normality assumptions. The normal P–P regression plot below also supports this (Figure 2).

Table 1: Validity Test of Cost Overrun

Table 2: Validity Test of Contract Change Order

Figure 2: Normal PP Plot of Regression Standardized for Cost Overrun

The pictures above show that the diagonal line in the graph predicts the ideal state following the normal distribution line. The points that are around the diagonal line are the state of the tested data where, most of the points are around the line. Based on the two tests, it can be concluded that the data of this study are normally distributed.

Table 3: Reliability Test of Cost Overrun

Table 4: Reliability Test of Contract Change Order

Figure 3: Normal PP Plot of Regression Standardized for Contract Change Order

Table 5: Kolmogorov-Smirnov Test for Cost Overrun

Table 6: Kolmogorov-Smirnov Test for Contract Change Order

Table 7: Multicollinearity test for Cost Overrun

Table 8: Multicollinearity test for Contract Change Order

Figure 4: Normal PP Plot of Regression Standardized for Contract Change Order

Table 9: Multiple Linear Regression test for Cost Overrun

Table 10: Multiple Linear Regression test for Contract Change Order

Multicollinearity Test

Multicollinearity test aims to determine whether there is a relationship between independent variables. And can be seen from the value of tolerance and VIF value. If the tolerance value>0.1 then multicollinearity does not occur and if the VIF value<10 then multicollinearity does not occur. In this study, the multicollinearity test was carried out using SPSS version 23. It can be seen in Table below (Table 7-8).

From the 2 tables above it can be seen that the VIF value<10 and Tolerance value>0.1 means that there is no multicollinearity problem between variables. So it can be concluded that there is no multicollinearity in this study.

Heteroscedasticity Test

Heteroscedasticity test is performed to test whether in the regression model there are variance or residual inequalities between one observations to another.

From the above output it can be seen that the diffuse points do not form a clear pattern, and the diffuse points are around the number 0 on the Y axis. So it can be concluded that there is no heteroscedasticity problem in the regression modelb (Figure 3-4).

Multiple Linear Regression

Multiple Linear Regression analysis is used to get the dominant factor causing cost overruns in road improvement projects in Solo. Based on the results of data processing using SPSS 23, a summary is obtained as follows:

Based on the table above, the regression equation is obtained as follows (Table 9-10):

Y= -1,271 + 0,106.X1.1 + 0,079X1.2 + -0,020X1.3 + -0,010X2.1 + -0,127X2.2 + 0,024X2.3 + - 0,021X2.4 + 0,127X2.5 + 0,102X3.1 + 0,053X3.2 + 0,205X4.1 + -0,032X4.2 + 0,004X4.3 + 0,167X5.1

+ 0,109X6.1 + 0,100X6.2 + 0,053X6.3 + -0,051X7.1 + 0,038X7.2 + -0,057X8.1 + 0,105X9.1 + Ɛ

Based on the table above, the regression equation is obtained as follows:

Y=   -0,824           +                0,072.XCCO1.1                      +      0,042.XCCO1.2              +      0,036.XCCO1.3              +      -0,058.XCCO1.4        + 0,072.XCCO2.1 + 0,026.XCCO2.2+0,049.XCCO3.1+ 0,117.XCCO4.1+0,079.XCCO4.2+

0,018.XCCO4.3 + -0,044.XCCO5.1 + 0,110.XCCO5.2 + 0,017.XCCO6.1 + 0,094.XCCO6.2 +

0,101.XCCO7.1 + -0,042.XCCO8.1 + 0,125.XCCO8.2 + Ɛ

(t) Test

(t) test was conducted to determine whether the independent variable has a significant effect or not. With a confidence level of 95%, a table of 2.01954 was obtained. The results of the (t) test can be seen in Table attachment.

Based on the data generated in Table V.6.1 and V.6.2 can be seen the results of the t factor test of the Contract change order variable. All contract change order variables do not meet the significant requirements because t count is smaller than tTable or significant value is more than 0.05 and both (Table 11-12).

(F) Test

(f) test is done by comparing the value of F count and F Table on ANOVA Table results of multiple linear regression analysis. By using SPSS Version 25 program, ANOVA Table is obtained from the results of multiple linear regression in this study as follows:

The (f) test was used to determine the effect of all the independent variables included in the regression model together against the dependent variable tested at the 0.05 significance level and to find FTable with the formula FTable = (k; nk) = (9; 51-9) = 9; 42 for Cost Overrun, and (8; 51-8) = 8; 43. Then the number is used as a reference to look up or see FTable values. Then determined the f Table value is 2.11 for Cost Overrun and 2.16 for Contract Change Order.

It is known that the value of Fcount 41.290>FTable is 2.11 with a significance value of 0.000<0.05 for Cost Overrun and Fcount 31.625 with a significance value of 0.000<0.05. So as the basis for the decision in the (f) test that is H0 will be rejected if the sig value is less than 0.05 and Fcount>FTable then H0 is rejected, so it can be concluded that the hypothesis is accepted (Table 13-14).

Figure 5: Heteroscedasticity test for Cost Overrun with Scatterplot

Figure 6: Heteroscedasticity test for Contract Change Order with Scatterplot

Table 11: Table (t) test analysis of Cost Overrun

Table 12: Table analysis of (t) test of Contract Change Order

Table 13: (f) test for Cost Overrun

Impact of Cost Overrun and Contract Change Order in the Occurrence of Cost Overrun

Based on the results of the T-Test variable cost overrun and contract change order in SPSS which can be seen in Table 11 and 12, it can be seen that 4 factors of the variable cost overrun are significant to the occurrence of cost overrun whereas from the contract change order variable there is no single variable that meets the significant requirements for the occurrence of cost overrun. To find out more about how much these significant variables affect the occurrence of cost overrun, researchers look for the percentage contribution of each variable to the occurrence of cost overrun. Percentage of significant variables is sought using the formula of Effective Contribution (SE) and Relative Contribution (SR). The percentage of factors that are significant to the occurrence of cost overrun is as follows:

• X1.1 (Not concerning the effect of inflation and exlation) with 8,39% relative contribution

• X4.1 (Use of imported materials) with 29,64% relative contribution

Table 14: (f) test for Contract Change Order

• X5.1 (Imposing overtime work too often) with 18,70% relative contribution

• X6.2 (Maintenance costs not according to plan) with 9,07% relative contribution

Based on descriptive analysis, can be concluded that the factors of Cost overrun variables that have significant influence on the occurrence of cost overrun is X1 (Estimated costs), X4 (Material), X5 (Labor), X6 (Equipment) while none of the Contract Change Order variables have significant influence (Figure 5-6).

The most dominant factor influencing cost overruns on road projects in the city of Solo in 2017-2018 based on significant result of T-Test is the use of imported materials (X4.1) with 28% Effective contribution and 30% relative contribution for Cost Overrun factors and none for Contract Change Order factors. While based on diagram venn the most dominant factors is X5.1 (Doing overtime too often) for cost overrun factors and XCCO5.2 (Bad labor productivity) with 15,33% Relative Contribution for Contract Change Order factorps.

The percentage of Cost Overrun variables those have significant influence on the occurrence of cost overrun based on (T)-test are as follows:

• X1.1 (Not counting the effects of inflation and exlation) with 8% relative contribution

• X4.1 (Use of imported materials) with 30% relative contribution

• X5.1 (Imposing overtime work) with 19% relative contribution

• X6.2 (Maintenance costs not according to plan) with 12% relative contribution

None of Contract Change Order variables have significant effect on the occurrence of cost overrun.

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