ANALYSIS OF FACTORS INFLUENCING THE PERFORMANCE OF THE AUDITOR INSPECTORATE OF NORTH SUMATRA PROVINCE WITH AUDITOR INDEPENDENCE AS A MODERATING VARIABLE

This research aims to determine the factors that influence the performance of inspectorate auditors in North Sumatra province with auditor independence as a moderating variable. This type of research is causality research, namely research that aims to analyze possible cause-and-effect relationships based on observations. This research was conducted using primary data with a survey method obtained through distributing questionnaires to auditors. The results of this research are that the first hypothesis is accepted, the Experience variable (X1) has a significant effect on the Auditor Performance variable (Y). The second hypothesis is accepted, the Knowledge variable (X2) has a significant effect on the Auditor Performance variable (Y). The third hypothesis is rejected, The Auditor Motivation variable (X3) has no significant effect on the Auditor Performance variable (Y). The fourth hypothesis is accepted, meaning that Experience (X1), Knowledge (X2), Auditor Motivation (X3) and Independence (Z) have a significant effect together (simultaneously) on the Auditor Performance variable (Y). The fifth hypothesis is accepted. The Independence variable (Z) is a moderator variable that influences the relationship between Experience (X1) and Auditor Performance (Y). The sixth hypothesis is accepted. The Independence variable (Z) is a moderator variable that influences the relationship between Knowledge (X2) and Auditor Performance (Y). The seventh hypothesis is rejected. The independence variable (Z) is not a moderator variable that influences the relationship between Auditor Motivation (X3) and Auditor Performance (Y). In order for the experience of auditors at the North Sumatra Provincial Inspectorate to be of higher quality, it is best to pay attention to the level of education and provide training regarding the implementation of audits and understanding the regulations as an auditor. So that auditors understand better and can carry out their duties well, because the knowledge and education obtained to audit requires a lot of experience. Apart from that, an auditor will learn a lot from the various cases he handles so that the auditor's performance can be of high quality. because the knowledge and education obtained to audit requires a lot of experience. Apart from that, an auditor will learn a lot from the various cases he handles so that the auditor's performance can be of high quality. because the knowledge and education obtained to audit requires a lot of experience. Apart from that, an auditor will learn a lot from the various cases he handles so that the auditor's performance can be of high quality.


INTRODUCTION
Public trust in a profession is determined by reliability, accuracy, timeliness and the quality of services or services that can be provided by the profession in question.To build trust, the behavior of professional actors needs to be regulated and the quality of their work can be accounted for, so that the public can have confidence in the quality of a professional's work.Audit work is a profession.Auditing is the activity of collecting and examining evidence related to information to determine and create a report on the level of conformity between the information and established criteria.The purpose of an audit is to verify whether the subject of the audit is in accordance with regulations, standards and approved methods.People who carry out audit tasks in a company or organization are called auditors.Auditor is a profession that focuses on auditing activities.The auditor profession is a job that is based on complex knowledge and can only be carried out by individuals with certain abilities and educational background.An auditor is required to have good performance and quality.Auditor performance is a process carried out by auditors in carrying out their duties in accordance with the responsibilities given to them, and is one of the benchmarks used to determine whether the work carried out will be good or otherwise.According to Mulyadi (2010), a performance auditor is a public accountant who carries out objective audit assignments on the financial statements of a company or other organization with the aim of determining whether the financial statements are presented fairly in accordance with generally accepted accounting principles, in all material respects, financial position, and business results of the company.According to Dewi & Sudana (2018), experience in auditing financial reports is one of the factors that influences auditor performance.Experienced auditors have a better understanding of financial reports and are able to provide reasonable explanations for errors in financial reports and can group errors based on the audit objectives and the structure of the underlying accounting system.
Knowledge also influences auditor performance.Where the increasing development of knowledge and technology has resulted in increasingly complex services to the community.Therefore, an auditor is required to have extensive knowledge regarding both auditing science and the technical knowledge that he must master.Sucipto (2007) defines knowledge according to the scope of audit as the auditor's ability to master the audit field (analyzing financial reports).Motivational factors that influence auditor performance according to Soegoto ( 2009) are a series of forces that cause people to behave in a certain way to achieve good and adequate work satisfaction and productivity.Work motivation for auditors is an important factor in producing quality audit reports.Auditors must also have an independent attitude so that audit reports can be of high quality.According to Kurnia et al (2014), independence is an auditor's attitude that does not take sides with anyone, including clients, and prioritizes the interests of users of financial reports and is obliged to defend the results of its audit findings.
Currently, the quality of the government's internal audit results is still in the spotlight of various parties, especially the public.In fact, the performance of the Government Internal Supervisory Apparatus, in this case the North Sumatra Provincial Inspectorate, in carrying out inspections and supervision of government agencies in North Sumatra Province is currently still in the spotlight, because there are still many inspection findings that were not detected by the inspectorate apparatus, but were discovered by the Audit Agency.Finance (BPK).In 2014, the North Sumatra Representative Audit Board (BPK) recommended that the North Sumatra Provincial Government (Pemprovsu) improve the performance of the Provincial Government Inspectorate by developing standards for work facilities and infrastructure to support the performance of the government's internal supervision.This is part of a follow-up to a number of findings regarding the performance of the Government's Internal Supervisory Apparatus.Then in December 2018, HeadInspectorateNorth Sumatra Ok Henry said that the North Sumatra Provincial Inspectorate was weak or could be said to be slow in handling cases.This is proven by the current condition of the North Sumatra Provincial Inspectorate only carrying out monitoring and no longer acting as an examiner or investigator of cases.This case refers to the State Civil Apparatus (ASN) which was involved in the realm of misappropriation of public money.North Sumatra has the title or achieved rank one for ASN with the most corruption to date.These ASNs will later be dishonorably dismissed by their respective agencies, because the decree has been signed by the Governor of North Sumatra, Edy Rahmayadi.
Apart from that, in July 2021 the Indonesian Financial Audit Agency (BPK) also discovered 8 activities of the North Sumatra Provincial Government (Pemprovsu) with a budget intended to handle Covid-19.The budget issue was also highlighted by the DPRD of North Sumatra in the plenary meeting regarding the Provincial Accountability Report (LPPJ) of the Governor of North Sumatra, Edy Rahmayadi for 2020 some time ago.The above findings indicate that the performance of the Government Internal Supervisory Apparatus at the North Sumatra Province Inspectorate has not been optimal.

IMPLEMENTATION METHOD Research design
This type of research is causality research, namely research that aims to analyze possible cause-and-effect relationships based on observing existing effects and looking for factors that might be the cause through certain data (Suryabratha, 2003).Researchers use this research design to provide empirical evidence and analyze the influence of factors that influence the performance of North Sumatra Province Inspectorate auditors with auditor independence as a moderating variable.

Operational Definition of Variables
The operational definition of a variable is a definition that is used as a basis for determining the value of each variable.The variables used in this research are the dependent variable, independent variable and moderating variable.The dependent variable (dependent variable) is the variable that is the researcher's main concern.Independent variables (independent variables) are variables that influence the dependent variable.Meanwhile, moderating variables are independent variables that will strengthen or weaken the relationship between other independent variables and the dependent variable.

Population, Sample and Sampling Technique
According to Arikunto (2006), population is the entire research object.The population in this study was the Functional Auditor Inspector of North Sumatra Province, totaling 47 respondents.According to Sugiyono (2013), a sample is part of the number or characteristics possessed by a population which must be able to represent that population.The sample for this research is all members of the research population or also called census sampling.The sampling technique in this research used the census method where the entire population was used as the research sample, so that the number of observations in this research was 47 respondents, namely the auditors of the functional officials of the Inspectorate of North Sumatra Province.

Research Instrument
This research uses a questionnaire research instrument.A questionnaire is an instrument that contains a list of questions used to collect research data from respondents.The questionnaire contains a series of questions that are structured in a structured manner.According to Sugiyono (2013: 199), the definition of a questionnaire is a data collection technique that is carried out by giving questions or written statements to respondents to answer.

Data collection technique
This research was conducted using primary data with a survey method obtained through distributing questionnaires to auditors, which contained various statements related to the variables studied, namely auditor performance, experience, knowledge, motivation and auditor independence as moderating variables.Primary data was obtained from the answers filled in by research respondents, namely the Functional Auditor Inspectorate of North Sumatra Province.Questionnaires were distributed by meeting the Inspectorate Auditor Functional Officer directly.The method used to measure the questions in the questionnaire for each variable in this research was using a Likert Scale.The Likert scale is the perception of respondents by stating that they strongly disagree or strongly agree with the questions asked with a score of 1 (STS = Strongly Disagree), score 2 (TS = Disagree), score 3 (N = Neutral), score 4 (S = Agree), and a score of 5 (SS = Strongly Agree).
In the measurement, each respondent is asked for their opinion regarding a statement, with a rating scale from 1 to 5. Positive responses (maximum) are given the highest value ( 5) and negative responses (minimum) are given the smallest value (1).In this research, to make it easier for respondents to answer the questionnaire, the assessment scale is as follows: Scale 1: Strongly Disagree (STS) Scale 2: Disagree (TS) Scale 3: Neutral (N) Scale 4: Agree (S) Scale 5: Strongly Agree (SS)

Data analysis technique
The data analysis method is a process of simplifying data into a form that is easier to read and interpret.Testing for the first hypothesis uses a multiple linear regression method which aims to predict the condition of the dependent variable when it is related to two or more independent variables, and for the second hypothesis uses residual regression analysis.This research data was processed using the SPSS (Statistical Package for Social Science) program.Source: Data processed from attachment 2 (2022)

RESULTS AND DISCUSSION
Based on table 1 above, it shows that N or the amount of data for each valid variable is 47, out of 47 sample data on Auditor Performance (Y), the minimum value is 0.18, the maximum value is 0.30, from the period it is known that the mean value is 24, 89, and a standard deviation value of 3.974, which means the mean value is greater than the standard value so that the data deviation is low so the value is spread evenly.Experience (X1), the minimum value is 0.13, the maximum value is 0.20, from the period it is known that the mean value is 16.43, and the standard deviation value is 2.134, which means the mean value is greater than the standard value so that the data deviation that occurs is low then the distribution of values is even.Knowledge (X2), the minimum value is 0.12, the maximum value is 0.20, from the period it is known that the mean value is 16.38, and the standard deviation value is 1.596, which means the mean value is greater than the standard value so that the data deviation that occurs is low then the distribution of values is even.
Auditor Motivation (X3), minimum value is 0.9, maximum value is 0.15, from the period it is known that the mean value is 12.83, and the standard deviation value is 1.494, which means the mean value is greater than the standard value so data deviations occur low, the value distribution is even.Independence (Z), the minimum value is 0.16, the maximum value is 0.24, from the period it is known that the mean value is 20.55, and the standard deviation value is 2.339, which means the mean value is greater than the standard value so that the data deviation that occurs is low then the distribution of values is even.

Test Research Instruments
a. Test the validity of the instrument Validity is a measure that shows the level of validity/validity of an instrument.An instrument that is less valid has low validity.Meanwhile, valid instruments have high validity.An instrument is said to be valid if it can reveal variables carefully.The level of the instrument shows the extent to which the data collected does not deviate from the description of the variable in question (Suharsini, 1998: 160).
Content validity means that the contents of the statements in the instrument are in accordance with the indicators of each variable.Meanwhile, empirical validity means that the researcher tries the instrument on the target variable.Meanwhile, empirical validity means that the researcher tries the instrument on targets that are in accordance with the research objectives.Along is also called a trial activity.Empirical validity uses item analysis techniques, which are used by correlating the scores on the items in question with the total score.Testing the validity of the items in this study used the computer program SPSS version 25.For the interpretation of the coefficients, if r count > r table is obtained, it can be concluded that the questionnaire items are included in the valid category.
Validity testing uses SPSS version 25.00 with criteria based on the calculated r value as follows: Source: Data processed from attachment 3 (2022) Table 2 proves that the results of testing the research instrument on the Auditor Performance (Y) variable obtained a calculated r value (corrected item-total correlation) > r table 0.290 for all statement items so that it was concluded that the 6 items of the Auditor Performance (Y) variable instrument could be used.as a data collection tool in research.Source: Data processed from attachment 3 (2022) Table 3 proves that the results of testing the research instrument on the Experience variable (X1) obtained a calculated r value (corrected item-total correlation) > r table 0.290 for all statement items so that it was concluded that the 4 items of the Experience variable (X1) instrument can be used as a tool.data collection in research.4 proves that the results of testing research instruments on the Knowledge variable (X2) obtained a calculated r value (corrected item-total correlation) > r table 0.290 for all statement items so that it is concluded that the 4 items of the Knowledge variable (X2) instrument can be used as a tool.data collection in research.Source: Data processed from attachment 3 (2022) Table 5 proves that the results of testing the research instrument on the Auditor Motivation variable (X3) obtained a calculated r value (corrected item-totalcorrelation)> r table 0.290 for all statement items so it is concluded that the 3 items of the Auditor Motivation variable instrument (X3) can be used as a data collection tool in research.Source: Data processed from attachment 3 (2022) Table 6 proves that the results of testing the research instrument on the Independence (Z) variable obtained a calculated r value (corrected item-total correlation) > r table 0.290 for all statement items so that it was concluded that the 5 items of the Independence (Z) variable instrument could be used as a tool.data collection in research.

b. Instrument Reliability Test
According to Sugiyono (2001: 97), a reliable instrument is an instrument that is used several times to measure the same object and will produce the same data.The odd-even reliability test is because the research sample was taken using a proportional random sampling technique.Reliability testing is carried out on items whose validity has been tested, so that invalid items are not included.In this research, to test reliability, the Alpha Cronbach formula was used.If the calculated r is greater than the table r, then the instrument is said to be relabeled.Data processing to test reliability in this research used the SPSS version 25 computer program.Based on the reliability test using Cronbach Alpha, all research variables are reliable because Cronbach Alpha is greater than 0.6.According to Sugiyono (2013:67) a factor is declared reliable if Cronbach Alpha is greater than 0.6.So the results of this research indicate that the measurement tool in this research has met the reliability test (reliable and can be used as a measuring tool).

Classic assumption test
The testing of classical assumptions with the SPSS 25.00 program carried out in this research includes: a. Normality test The Normality Test aims to test whether in the regression model, confounding or residual variables have a normal distribution (Ghozali, 2016: 154).Data normality testing can be done using two methods, graphics and statistics.The graphic method normality test uses a normal probability plot, while the statistical method normality test uses the one sample Kolmogorov Smirnov Test.

Figure 1. Normal P Plot
Data that is normally distributed will form a straight diagonal line and plotting the residual data will be compared with the diagonal line.If the residual data distribution is normal then the line depicting the actual data will follow the diagonal line (Ghozali, 2016: 352).Data that is normally distributed will form a straight diagonal line and plotting the residual data will be compared with the diagonal line.If the residual data distribution is normal then the line depicting the actual data will follow the diagonal line (Ghozali, 2016: 353).The test results using SPSS 25.00 are as follows: From the output in table 8, it can be seen that the significance value (Monte Carlo Sig.) for all variables is 0.570.If the significance is more than 0.05, then the residual value is normal, so it can be concluded that all variables are normally distributed.

b. Heteroscedasticity Test
The heteroscedasticity test aims to test whether the regression model has unequal variances from the residuals of one observation to another.One way to detect the presence or absence of heteroscedasticity is with the Glejser Test.In the Glejser test, if the independent variable is statistically significant in influencing the dependent variable then there is an indication that heteroscedasticity is occurring.On the other hand, if the independent variable is not statistically significant in influencing the dependent variable then there is no indication of heteroscedasticity.This is observed from the probability of significance above the 5% confidence level (Ghozali, 2016;138).The results of data processing using SPSS 25.00 show the results in the following table: a. Dependent Variable: ABS_RES Source: Data processed from attachment 4 (2022) Table 9 shows the significance value of the Independence variable (Z) of 0.894, the significance value of the Experience variable (X1) of 0.604, the significance value of the Knowledge variable (X2) of 0.392 and the significance value of the Auditor Motivation variable (X3) of 0.572, where the value of this variable is greater of 0.05 so it can be concluded that there are no symptoms of heteroscedasticity.

c. Multicollinearity Test
The multicollinearity test aims to find out whether in the regression model there is a correlation between the independent variables.The multicollinearity test in this research is seen from the tolerance value or variance inflation factor (VIF).The calculation of the tolerance value or VIF using the SPSS 25.00 for Windows program can be seen in Table 10 below: Based on table 10, it can be seen that the tolerance value of Independence Style (Z) is 0.050, Experience (X1) is 0.854, Knowledge (X2) is 0.705, Auditor Motivation (X3) is 0.933, all of which are greater than 0.10 while the VIF of Independence (Z) is 19.874,Experience (X1) is 18.623, Knowledge (X2) is 1.419, Auditor Motivation (X3) is 1.071, all of which are smaller than 10.Based on the calculation results above, it can be seen that the tolerance values for all variables independence is greater than 0.10 and the VIF value of all independent variables is also smaller than 5 so that there are no symptoms of correlation in the independent variables.So it can be concluded that there are no symptoms of multicollinearity between the independent variables in the regression model.

Multiple Linear Regression Testing
Multiple linear regression testing explains the large role of the variables Experience (X1), Knowledge (X2), Auditor Motivation (X3) and Independence (Z) on the Auditor Performance variable (Y).Data analysis in this study used multiple linear regression analysis using SPSS 25.0 for windows.The analysis of each variable is explained in the following description:  3)" = 44), the ttable value is 2.015.From this description it can be seen that tcount (4.521) > ttable (2.015) , likewise, with a significance value of 0.000 < 0.05, it can be concluded that the second hypothesis is accepted, meaning that the Knowledge variable (X2) has a significant effect on the Auditor Performance variable (Y).
3) Hypothesis Testing The Effect of the Auditor Motivation Variable (X3) on the Auditor Performance Variable (Y) The form of hypothesis testing based on statistics can be described as follows: Decision Making Criteria: a) Reject the hypothesis if tcount < ttable or -tcount > -ttable or Sig value.> 0.05 b) Accept the hypothesis if tcount ≥ ttable or -tcount ≤ -ttable or Sig.< 0.05 From table 12, the tcount value is -0.290.With α = 5%, ttable (5%; 47-k"( 3)" = 44), the ttable value is 2.015.From the description it can be seen that tcount (-0.290) < ttable ( 2.015), likewise with a significance value of 0.773 > 0.05, it can be concluded that the third hypothesis is rejected, meaning that the Auditor Motivation variable (X3) has no significant effect on the Auditor Performance variable (Y).

b. F Test (Simultaneous)
This test basically shows whether all the independent variables included in this model have a joint influence on the dependent variable.The results of the F test can be seen in table 13 below: The form of hypothesis testing based on statistics can be described as follows: Decision Making Criteria: a) Accept the hypothesis if the calculated F value > F table or Sig.< 0.05 .b) Reject the hypothesis if the calculated F value < F table or Sig.> 0.05.

1 )
If r count > r table orr count < -r table then the statement is declared valid.2)If r count < r table orr count > -r table then the statement is declared invalid

Volumes 3 No. 5 (2023)
ANALYSIS OF FACTORS INFLUENCING THE PERFORMANCE OF THE AUDITOR INSPECTORATE OF NORTH SUMATRA PROVINCE WITH AUDITOR INDEPENDENCE ASA MODERATING VARIABLE Tuti Rahmawati, Keulana Erwin, Muammar Khaddafi 1778 International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration |IJEBAS E-ISSN: 2808-4713 |https://radjapublika.com/index.php/IJEBASThistest was carried out on 47 respondents, then df = 47-k (3) = 44, with α = 5%, the r table value is 0.290(Ghozali, 2016), then the calculated r value will be compared with the table r value as in table2 below:

Table 2 .
Auditor Performance Validity Test Results (Y)

Table 6 .
Independence Validity Test Results (Z)

Table 7 .
Reliability Test Results

Table 8 .
One Sample Kolmogorov Smirnov Test One-Sample Kolmogorov-Smirnov Test b. Calculated from data.c.Lilliefors Significance Correction.d.Based on 10000 sampled tables with starting seed 2000000.Source: Data processed from attachment 4 (2022)

Table 11 .
Multiple Linear Regression ResultsCoefficientsa .The regression coefficient value of Independence (Z) (b1) is 1.900 indicating the large role of Independence (Z) on Auditor Performance (Y) assuming the Auditor Performance E-ISSN: 2808-4713 |https://radjapublika.com/index.php/IJEBASvariable(Y) is constant.This means that if Independence (Z) increases by 1 value unit, then it is predicted that Auditor Performance (Y) will increase by 1,900 value units assuming that Auditor Performance (Y) is constant.The t statistical test is also called the individual significance test.This test shows how far the independent variable partially influences the dependent variable.In this research, partial hypothesis testing was carried out on each independent variable as in Table12 below: e