International Journal of Human Capital Management, Vol. 2 (2), December 2018 International Journal of Human Capital Management, Vol. 7 (1), June 2023 1 | P a g e International Journal of Human Capital Available online at Management http://journal.unj.ac.id/unj/index.php/ijhcm E-ISSN 2580-9164 Vol. 7, No. 1, June 2023, p 1-27 ELECTRONIC AUDIT (E-AUDIT), AUDIT JUDGEMENT, CORRUPTION DETECTION AND AUDIT QUALITY: BPK RI Luqman Hakim Universitas Muhammadiyah Jakarta Email: luqman.hakim@umj.ac.id Raymond Pardomuan Universitas Muhammadiyah Jakarta Email: raymond.pardomuan@gmail.com Mitsalina Tantri Universitas Muhammadiyah Jakarta Email: mitsalina.tantri@live.com ABSTRACT This research is based on the phenomenon that occurred related to the existence of several limitations faced by the Supreme Audit Agency of the Republic of Indonesia in carrying out audits of financial statements in public sector entities. This condition risks affecting audit quality, resulting in the emergence of information gaps between BPK RI, the public and related stakeholders. The purpose of this study was to examine and analyze whether the effect of each independent variable (X1, X2 and X3), including electronic audit (e-audit), audit judgment and the detection of corruption affecting audit quality as the dependent variable (Y ). The research method used is a quantitative method. This study examines the effect of e- audit, audit judgment and corruption detection on audit quality. This study uses primary and secondary data with a research instrument in the form of a questionnaire. The object of this study is the IB Auditorate as a work unit equivalent to echelon II under the Main Auditorate of State Finance I at the Head Office of the Supreme Audit Agency of the Republic of Indonesia. The number of respondents in this study were 40 auditors/examiners in the IB Auditorate. The type of analysis in this study was statistical descriptive analysis. The statistical analysis tools used are the SPSS and Eviews applications. The results of the empirical study revealed that each of the independent variables (X1, X2, and X3), including e-audit, audit judgment and detection of corruption, had a positive and significant effect on audit quality as the dependent variable (Y). Future research is needed to develop with other indicators besides the quality of electronic audit (e-audit), audit judgement, corruption detection and audit quality. Keywords: E-Audits, Audit Judgment, Corruption Detection, Audit Quality. http://journal.unj.ac.id/unj/index.php/ International Journal of Human Capital Management, Vol. 7 (1), June 2023 2 | P a g e Received: 15 February 2023 Accepted: 28 February 2023 Publish: June 2023 How to Cite: Hakim., et.al. (2023). Electronic Audit (E-Audit), Audit Judgement, Corruption Detection and Audit Quality: BPK RI. International Journal of Human Capital Management, 7 (1), 1-27. https://doi.org/10.21009/IJHCM.07.01.1 INTRODUCTION In addition to the limited time that has been set constitutionally, financial audits at the BPK RI also have their own challenges related to the lack of availability of human resources (HR) for auditors (examiners). Some of the limitations above have the potential to affect audit quality at BPK RI, whose reports are submitted to the public/stakeholders (Pradita, 2014), (Rachmadia, 2015) and (Tri Cahyono & Siswantoro, 2016). This condition also gave rise to a phenomenon known as the perception gap. There is a perception gap between the public's expectations, especially LK users and the independent audit function by BPK RI which is inseparable from the link with the quality of the audit itself. (Boyd, Boyd & Boyd, 2001 in Tampubolon & Tobing, 2019) and (Auliyana, 2017). The opinion sheet published in the BPK RI Inspection Report (LHP) states that LK has been presented fairly according to standards, but the facts still contain serious problems. In several cases it is known that there are Central/Regional Government entities with the best LK predicate, namely Unqualified (WTP) which does not necessarily guarantee freedom from the occurrence of irregular practices, especially criminal acts of corruption, thus affecting the emergence of reputational r isk in the form of reduced public trust (Warkini et al. al. 2020), (Layli & Arifin, 2020), (Algam, 2018), (Tehupuring, 2018) and (Gunarwanto, 2017 in Utomo et al. 2018). These problems are as contained in table 1 below: Table 1 Corruption Cases in the Central/Regional Government and Opinion Status of BPK RI No Defendant's Position Corruption Case Case Year BPK RI Opinion in Case Year 1 Governor of Riau Archipelago (an NB) Reclamation permit corruption case on Kep. Riau 2019 WTP 2 Regent of Purbalingga (an T) Corruption case in the construction of an Islamic Center in Purbalingga Regency 2018 WTP Mayor of Blitar (an MSA) Corruption case in the construction of a Junior High School in Blitar City 2018 WTP International Journal of Human Capital Management, Vol. 7 (1), June 2023 3 | P a g e No Defendant's Position Corruption Case Case Year BPK RI Opinion in Case Year 3 Inspector General of the Ministry of Village PDTT (an SGT) Giving bribes in order to influence the results of examination of LK 2017 WTP 4 Minister of Religion (an SA) Implementation of Hajj and Misuse of Ministerial Operational Funds 2011- 2014 2011: WTP DPP 2012: WTP DPP 2013: WTP DPP 2014: WTP DPP 5 Minister of Energy and Mineral Resources (an JW) Misusing the Minister's Operational Funds (DOM) and receiving gratuities 2011- 2013 2011: WTP 2012: WTP 2013: WTP Source: BPK RI and KPK: processed Even worse, in several cases related to the quality of BPK RI audits, there is a risk of irregularities that also involve internal parties (bPK RI auditors), so that it indirectly constitutes a lie to the public regarding audit opinions (Layli & Arifin, 2020). As explained in the following data: Table 2 Corruption Cases Involving BPK RI Auditors No . Corruption Case Value of Bribery/Gratification 1 The bribery case of one former BPK RI auditor related to an examination at BUMN PT Jasa Marga Purbaleunyi branch in 2017 One former BPK RI auditor was proven to have received a Harley Davidson motorbike worth IDR 115 million from the former GM Jasa Marga Purbaleunyi Branch 2 The bribery case of two former BPK RI auditors related to the Opinion on the Audit Result Report (LHP) on the Financial Statements of the Ministry of Villages, Development of Disadvantaged Regions and Transmigration (Kemendes PDT) for the 2016 Fiscal Year Two former BPK RI auditors were proven to have received Rp. 240 million in stages from the former Inspector General of the Ministry of Village PDT 3. The bribery case of two former BPK RI auditors related to the Opinion on the Examination Report (LHP) on the Two former BPK RI auditors were proven to have received Rp. 400 million from former International Journal of Human Capital Management, Vol. 7 (1), June 2023 4 | P a g e Bekasi City Government's financial statements for the 2009 Fiscal Year Bekasi City Government officials 4. The bribery case of two former BPK RI auditors related to the Opinion on the Audit Result Report (LHP) on the Regional Financial Statements of Tomohon City, North Sulawesi in the 2007 Fiscal Year Two former BPK RI auditors accepted a bribe of Rp. 600 million given by the Mayor of Tomohon, to provide a WTP opinion on LK. In addition to receiving facilitation for giving opinions, the two people also received hotel facilities and vehicle rentals taken from Tomohon city government funds in the amount of IDR 7.5 million. From audit quality issues, it can also lead to legal risk vulnerability through lawsuits/lawsuits from auditees who do not accept/objection to BPK RI's audit quality. The data on the number of lawsuits and subpoenas against audits conducted by the BPK RI as of 2019 are presented in the figure below (BPK RI, 2019): ‘ Figure 1. Number of Claims from the Examined Party to BPK Source of the 2019 BPK RI Annual Report From the description of the data and facts above, Herawati & Selfia (2019) and Francis (2014) in Algam (2018) explain the correlation related to the emergence of gaps in terms of the audit function, that this is due to an audit failure which is most likely to occur. on low audit quality. BPK RI needs to improve audit quality according to the mandate, fulfill stakeholder requests and audits that pay attention to public issues in a strategic, anticipatory and responsive manner. In this study, efforts to measure audit quality refer to public sector audit practices carried out by the BPK RI. As stated in Article 9 of Law Number 15 of 2006 concerning BPK RI, this can be interpreted that in carrying out its duties the BPK has the authority to determine, among other things: the object of inspection; planning and carrying out inspections; determine the time and method of inspection; determine the type of documents, data and information; set inspection standards; and establishing a code of auditing ethics. With this authority, BPK RI seeks to narrow the level of gap (expectation gap), so as to be able to reduce the risks that hinder the achievement of a quality audit process. International Journal of Human Capital Management, Vol. 7 (1), June 2023 5 | P a g e Research from Supriadi et al. (2019), Rufaedah (2017), Amerthajaya & Aryani M (2016) and Darono (2014) explain that in order to achieve good audit quality, it is necessary to carry out an electronic-based inspection (e-audit) or known as a computer-assisted audit technique (hereinafter abbreviated as TABK). Given the scope of inspection (central/regional government entities and the complexity of existing transactions is not comparable to the availability of human resources and sufficient time, e-audit can be considered capable of producing quality audits if errors are caused by human negligence/accidents and the time required in the process). audits can be minimized. Besides having to implement e-audit, other factors to achieve high audit quality, research from Sulistyawati et al. (2019), Wijaksana & Krisna Dewi (2019), Fitriana et al. (2014) in Sulistyawati et al. (2019), Gasendi et al. (2017), Hasanah & Rosini (2016) and Yendrawati & Mukti (2015) that auditors are required to have an attitude of professional judgment in carrying out audit procedures or audit judgments. The results of research by Sukmawati & Faisal (2015) show that the audit quality of the auditors can be influenced by audit judgment competence. In forming audit judgment, it is influenced by several aspects, including professional audit expert ise, accountability pressure, and task complexity. As an important part of ensuring the quality of state financial audits, the SPKN reveals that the RI BPK auditors also consider the ability to detect fraud risks, especially corruption in carrying out their procedures (BPK RI, 2017). This is very much needed, because corruption is a serious problem that has an impact on state financial losses (Aksa, 2018). Research from Sihombing et al. (2019) and Umar et al. (2019) explained that the auditor has a high role and responsibility in being able to detect corruption in the LK he audits, then the auditor can communicate to stakeholders, if he finds indications of fraud in the LK. Based on the phenomenon, the literature used and the results of previous research that was stated earlier, this research will focus on the research objects of E-Audit, Audit Judgment and Corruption Detection and Audit Quality. This research was conducted on auditors/investigators in the IB Auditorate environment at BPK RI. So the title of this study is the Effect of E-Audit, Audit Judgment and Corruption Detection on Audit Quality (Empirical Study on BPK RI's IB Auditorate). LITERATURE REVIEW Agency Theory (Grand Theory) In agency theory (agency theory), agency relationships arise when one or more people (principal) hire another person (agent) to provide a service and then delegate decision-making authority to the agent. Agency theory is a theory used by companies to base their business practices. Jensen and Meckling (1976) describe the agency relationship as:“agency relationship as a contract under which one or more persons (the principals) engage another person (the agent) to perform some service on their behalf which involves delegating some decisions making authority to the agent”. Agency theory is also known as contractual theory which views a company as a contractual agreement between company members. They also state that the agency relationship is as a contract between one or more parties who employ other parties to perform a service for their benefit which includes delegating some decision-making powers to these other parties. Thus, this theory indicates that there is an interest in every party in the company to achieve goals (Godfrey et al. 2010) and (Hoesada, 2020). Nandari & Latrini (2018) and Messier et al. (2011) in Atiqoh & Riduwan (2016) explains that there are differences (conflicts) of interests that can arise between principals (owners) and agents (management). Management has a tendency to compile LK to appear to be good, so that the owner (principal) perceives their performance as good. Rosiana et al. (2019), Sugiarmini & International Journal of Human Capital Management, Vol. 7 (1), June 2023 6 | P a g e Datrini (2017) and Nandari & Latrini (2015) explain that as an effort to reduce deviations by management in preparing LK, an evaluation or test is required by an independent party, namely the auditor. The role of the auditor is needed to mediate conflicts that occur between management and stakeholders/owners. Meanwhile, the relevance between agency theory and audit quality in the public sector according to research by Isnadiva & Haryanto (2021), Saleh & Ratmono (2017) and Sugiarmini & Datrini (2017) can be illustrated that the correlation between the public and the government is like the principal and agent. The public (society) as the principal and the government (executive, legislative and judicial institutions) as the agent. The public gives regulatory authority to the government and provides resources (budget). As a manifestation of accountability for the authority given, the government publishes accountability reports. Electronic Audit (E-Audit) According to Setiady (2016) that electronic-based audits or e-audits are not much different from audits in general. It's just that in e-audits, procedures for collecting, verifying and validating evidence are carried out with computer aids, so as to improve audit quality effectively and efficiently. The evidence collected for evaluation has begun to leave the hard copy form but has taken the form of a computer data archive file (ADK) or soft copy format. Suhartini et al. (2021) and Nindyastuti & Kiswara (2014) explain that e-audit is carried out by preparing software tools by the auditor that are customized to form a database synergy that stores financial information from the entity (auditee) to test accounts and transactions contained in LK. In the context of public sector finance, according to Florida, E-audit, is a computer-assisted audit that utilizes electronic records in completing all or part of the audit procedure. Public sector e-audits are carried out by preparing customized software to form a database that stores various information and the financial performance of public sector entities. Supriadi et al. (2019), Rufaedah (2017), Atmaja (2016) and Darono (2014), explain that the e-audit developed by BPK RI is included in the auditing around the computer category, namely audit techniques using audit software or TABK. In this approach, testing is applied only to input and output quality, not testing the system. This approach is more appropriate and relevant to accommodate the needs of BPK RI's auditing duties on financial data and information processed by public sector agencies digitally. The electronic data required in the audit process is first prepared by the entity and then sent (transferred) via internet access which is connected in synergy with the BPK e-audit portal. Audit Judgment Wijaksana & Krisna Dewi (2019), Gasendi et al. (2017), Pektra and Kurnia, (2015) in Gracea et al (2017), Hasanah & Rosini (2016) and Yendrawati & Mukti (2015) explain that the definition of audit judgment is a perspective, policy or consideration in responding to information that affects documentation evidence as well as the auditor's decision maker's opinion on an entity's LK. Judgment can be determined from an auditor's individual perception/assessment regarding the risks and conditions that exist in the field. As for Fitriana, et al (2014) in Sulistyawati et al. (2019) explains that at each stage of the audit, audit judgment is required in the FI audit process, starting from audit plans, testing to audit reports. Audit judgment is a judgment that influences audit documentation and opinion decisions made by the auditor. Audit judgment is needed in the audit process because the audit is not carried out on all evidence of transactions within the organization, but only an adequate sample. Based on this sample of evidence, the auditor will provide an opinion on the entity's audited LK, so that it can be said that audit judgment greatly determines the outcome of the audit. International Journal of Human Capital Management, Vol. 7 (1), June 2023 7 | P a g e In the public sector audit conception, the term audit judgment is also known as professional judgment. This has received special attention in BPK RI Regulation No. 1 of 2017 concerning State Financial Audit Standards (SPKN) which explains that: The examiner must prioritize the principle of professional judgment. Professional judgment is the application of the examiner's collective knowledge, skills and experience to the audit process. Professional judgments are made by auditors who are trained, have knowledge and experience so as to have the competence necessary to make reasonable judgments. Furthermore, based on the SPKN it can be seen that the BPK RI examiner (auditor) must use professional judgment to determine matters related to the examination (audit) carried out, both in financial audits, performance audits, and audits with a specific purpose. These professional judgments are related to interference with independence, considerations about the results of previous audits and follow-up on recommendations relating to the objectives of the audits carried out, professional considerations regarding audit procedures designed to assess material misstatements and consider the internal control of the entity being audited (BPK RI, 2017). Corruption Detection Tuannakota in a book entitled Forensic Accounting and Investigative Audit, second edition (2010) explains that corruption is a problem related to the economic and institutional system. Economic and institutional systems determine the scope of corruption and the incentives to engage in corruption. Economic and institutional systems that increase the benefits or "advantages" of corruption tend to have four characteristics: (a) individual officials have abso lute power (substantial monopoly power) on decision making; (b) the official concerned has leniency in authority (discretion) the big one; (c) they don't need to be held accountable (noaccountable) to their actions, and (d) they operate in a low-disclosure environment (an environment of low transparency). These four characteristics give birth to the following formula or equation: Where: C = corruption(corruption) PM = monopoly power (absolute power) D = direction (relaxing authority) A = accountability(accountability) Tdm = transparency of decision making (openness in decision making) The development of the factors that lead to the occurrence of fraud from the development of the theory from time to time can be briefly described in the following table: Table 3 Fraud Types No Approach (Theory) Causative factor Researcher (Initiator) 1. Fraud Triangle 1. pressure 2. Opportunity 3. Rationalization Donald R. Cressey (1950) 2. FraudDiamond 1. Incentives 2. Opportunity 3. Rationalization Wolfe & Hermanson (2004) International Journal of Human Capital Management, Vol. 7 (1), June 2023 8 | P a g e 4. capabilities 3. Pentagon fraud 1. Arrogance 2. Competence 3. Opportunity 4. Rationalization 5. pressure Crowe (2011) 4. Fraud Star 1. Incentives 2. Opportunity 3. pressure 4. Rationalization 5. capabilities 6. Lack of Integrity Haryono Umar (2016) Source: processed data The ACFE (Association of Certified Fraud Examiners; 2000) as quoted by Ristianingsih (2017) explains that corruption is one part of the three typologies of acts or acts of fraud (fraud) apart from asset misappropriation and fraudulent statements. statements). According to ACFE corruption is divided into: Conflict of interest (conflict of interest), bribe (brinerry), Illegal giving (illegal gravity), economic blackmail (economic extortion). Corruption is the most difficult type of fraud to detect because it involves cooperation with other parties such as bribery or collusion. However, in a more in-depth study, this occurs due to detrimental cooperation, including abuse of authority/conflict of interest, bribery, illegal gratuities, and economic extortion. economic extortion) Ristianingsih (2017). According to Umar (2020) and Wulandari (2019), explaining that fraud (corruption) detection is a method or process of determining and discovering through the auditor's ability to reveal illegal acts/actions caused by intentional misstatement of LK. Information: Y = indication of corruption (corruption indication) X1 = pressure (pressure) X2 = opportunity X3 = justification (rationalization) X4 = ability (capabililty) X5 = loss of integrity (lack of integrity) Constant (a) = this means that if all independent variables have a value of zero (0), then the dependent variable is -2.106. Audit Quality Audit is a process of collecting and evaluating evidence about information to determine and report the level of conformity between information and established criteria, auditing must be carried out by competent and independent people (Arens, Alvin A, Randal J. Elder, Mark S. Beasley, 2017). Audit is used to reduce the information gap that exists between management and stakeholders through the use of services from external auditors. Duff (2004) in Dunakhir (2017) and Knechel, W. Robert (2012) in Supriadi (2019) explained that although research on audit quality has been carried out widely, there is no agreed definition regarding audit quality. According to Hay & As quoted in the book Audit Quality and Corruption Detection by Purba & Umar (2021), several accounting researchers have identified various definitions of audit quality. There are four definitions of audit quality, namely: Definition by De Angelo (1981), Audit quality is the probability of the market value that the financial statements contain material errors International Journal of Human Capital Management, Vol. 7 (1), June 2023 9 | P a g e and the auditor finds and reports these material errors. Audit quality is the probability that an auditor finds and reports a violation in his client's accounting system. A large Public Accounting Firm (KAP) will try to present a higher audit quality compared to a small KAP. Definition by Lee, Liu and Wang (1999), According to them, audit quality is the probability that the auditor will not report an audit report with an unqualified opinion for financial statements that contain material errors. Definition by Titman and Trueman (1986), Beatty (1986), Krinsky and Rotenberg (1989), and Davidson and Neu (1993), Audit quality is measured by the accuracy of information reported by the auditor. Definition by Wallace (1987), Audit quality is determined from the audit ability to reduce noise and bias and increase the purity (fineness) of accounting data. To be able to meet good audit quality, the auditor in carrying out his profession as an examiner must be guided by the accountant's code of ethics, professional standards and financial accounting standards that apply in Indonesia. The Government Accountability Office (GAO) defines audit quality as adherence to professional standards and contractual ties while carrying out audits Auditing standards serve as guidance and a measure of the quality of auditor performance (Messier, Glover, & Prawitt, 2016 and Lowensohn et al. 2005 in Purba & Umar, 2021). Within the scope of public sector audits, Regulation of the Minister of State for Administrative Reform Number PER/05/M.PAN/03/2008 concerning Standards for Auditing Government Internal Supervisory Apparatuses explains that audit quality measurement of LK must refer to the State Financial Audit Standards/SPKN (Purba & Omar, 2021). According to Djamil (2007) in Landarica & Arizqi (2020), that steps that can be taken to improve audit quality are: The need to continue professional education for an audit team, In relation to audit assignments, In carrying out audits and preparing reports, Planning work audit as well as possible and if an assistant is used, supervision is carried out properly. METHODOLOGY The research object is 4 (four) variables, namely the Influence of E-Audit, Audit Judgment and Corruption Detection on Audit Quality. The selected method is a statistical descriptive analysis method with a quantitative approach. While the research design is causality. The data used in this study are primary data such as answers from respondents' questionnaires, and secondary data support such as literature/references containing an overview, strategic plans, organizational structure, employee lists (bezetting), operating standards. procedures, internal documentation/reports as well as the results of similar previous studies. The location of the analysis unit in this study is at the Umar Wirahadikusumah Building – Floor VI BPK RI Head Office Jalan Jenderal Gatot Subroto Kav 31 Central Jakarta, DKI Jakarta Province. with a research implementation period from December 2021 to January 2022. The population in this study were IB Auditorate employees at BPK RI with PNS status (auditors/auditors) as of August 2 2021 (employee bezzeting) of 55 people consisting of 11 intermediate expert examiners / middle, 31 junior / junior expert examiners, and 13 first / first expert examiners. Respondents were selected using purposive sampling, namely as many as 40 people based on auditor position criteria. Collecting research data using survey methods and documentation studies. Data analysis used descriptive statistical methods, tested data quality, tested classical assumptions, and tested the research hypothesis B in BPK RI with the status of civil servants (auditors/auditors) as of August 2 2021 (bezzeting employees) as many as 55 people consisting of 11 intermediate/middle expert examiners, 31 junior/junior expert examiners, and 13 first/first expert examiners. Respondents were selected using purposive sampling, namely as many as 40 people based on auditor position criteria. Collecting research data using survey methods and documentation studies. Data analysis used descriptive statistical methods, tested data quality, tested classical assumptions, and tested the research hypothesis B in BPK RI with the status of civil servants (auditors/auditors) as of August 2, 2021 (bezzeting employees) as many as 55 people consisting International Journal of Human Capital Management, Vol. 7 (1), June 2023 10 | P a g e of 11 intermediate/middle expert examiners, 31 junior/junior expert examiners, and 13 first/first expert examiners. Respondents were selected using purposive sampling, namely as many as 40 people based on auditor position criteria. Collecting research data using survey methods and documentation studies. Data analysis used descriptive statistical methods, tested data quality, tested classical assumptions, and tested the research hypothesis ie as many as 40 people based on the criteria for auditor position levels. Collecting research data using survey methods and documentation studies. Data analysis used descriptive statistical methods, tested data quality, tested classical assumptions, and tested the research hypothesis ie as many as 40 people based on the criteria for auditor position levels. Collecting research data using survey methods and documentation studies. Data analysis used descriptive statistical methods, tested data quality, tested classical assumptions, and tested the research hypothesis. RESULT AND DISCUSSION Results of Data Collection Out of a total of 55 active status examiner/auditor employees, the number of valid questionnaires used in this study was 40 questionnaires with details of position levels as shown in the following table: Table 4 Comparison between Population and Respondents (Sample) in Research No Position Level Populat ion Respondents (Sample) % 1 Intermediate/Madya Expert Examiner 11 6 54, 54 2 Young/Young Expert Examiner 31 24 77, 41 3 First/First Expert Examiner 13 10 76, 92 Amount 55 40 72, 72 Source: processed data Table 5 Respondent Demographics in Research Information Number of people) Percentage Gender Man 33 82.50% Woman 7 17.50% Age < 35 years 12 30.00% 35 - 50 Years 25 62.50% > 50 Years 3 7.50% Years of service < 10 Years 10 25.00% 10 - 15 Years 8 20.00% 15 - 20 Years 17 42.50% > 20 Years 5 12.50% Last education Bachelor Degree (S-1) 31 77.50% International Journal of Human Capital Management, Vol. 7 (1), June 2023 11 | P a g e Master Degree (S-2) 9 22.50% Explanation of descriptive statistics for each variable is as follows: Table 7 Descriptive statistics for X1 variable data (E-Audit) Variable Min Max Means Standard Deviation mode Median X1 (E-Audt) 1 4 3,050 0.604 3 3 Data processing sources The table above presents that the statements in variable X1 have a minimum value of 1 and a maximum of 4. The most answers are at value 3 (Agree), with the standard deviation value being 0.604. The mode and median values are at value 3. This means that all statements that in variable X1 the average answer is "agree". Table 8 Descriptive statistics for X2 variable data (Audit Judgment) Variable Min Max Means Standard Deviation mode Median X2 (Audit Judgment) 2 4 3,364 0.531 3 3 Data processing sources table above presents that the statements in variable X2 have a minimum value of 1 and a maximum of 4. The most answers are at value 3 (Agree), with the standard deviation value being 0.531. The mode and median values are at value 3. This means that all statements that in variable X2 the average answer is "agree". Table 9 Descriptive statistics for X3 variable data (Corruption Detection) Variable Min Max Means Standard Deviation mode Median X3 1 4 3,214 0.570 3 3 Data processing sources The table above presents that the statements in variable X3 have a minimum value of 1 and a maximum of 4. The most answers are at value 3 (Agree), with the standard deviation value being 0.570. The mode and median values are at value 3. This means that all statements that in variable X3 the average answer is "agree". International Journal of Human Capital Management, Vol. 7 (1), June 2023 12 | P a g e Table 10 Y data descriptive statistics (Audit Quality) Variable Min Max Means Standard Deviation mode Median Y 2 4 3,392 0.500 3 3 Data processing sources The table above presents that the statements in variable Y have a minimum value of 1 and a maximum of 4. The most answers are at value 3 (Agree), with the standard deviation value being 0.500. The mode and median values are at value 3. This means that all statements that in variable Y the average answer is "agree". Data Quality Test Results Data Validity Test Validity testing was carried out using the Pearson Product Moment correlation test. The test is carried out by correlating the scores of respondents' answers from each question item. The results of validity testing are as follows: Table 11 Validity Test Results Variable Question Items R count R table Ket E-Audits 1. E-Audit is easy to learn or there are no serious obstacles/obstacles in understanding and applying e-audit in the audit work being carried out. 0.834 0.312 Valid 2. As an Auditor, I understand all modules, features/menus and the purpose of using the e- audit system. 0839 0.312 Valid 3. In the past year, I often use the e-audit system to carry out tasks (audit process) at BPK RI. 0.787 0.312 Valid 4. With the e-audit application software (consolidating agent), I can retrieve, process, and analyze data precisely and accurately. 0.777 0.312 Valid 5. The device that I use is capable of storing large amounts of data (storage/size) or I have no shortage of file storage media when using the e- audit system/application. 0.798 0.312 Valid 6. The availability of server infrastructure, telecommunication network/internet connection to support the e-audit application/system is adequate. 0.860 0.312 Valid 7. The e-audit system produces audit results that are accurate, relevant, clear, easy to understand, and up to date. 0892 0.312 Valid 8. The e-audit system makes it easier for the auditor to obtain auditee data/information before conducting a field test. 0.830 0.312 Valid International Journal of Human Capital Management, Vol. 7 (1), June 2023 13 | P a g e Variable Question Items R count R table Ket 9. With the e-audit system, the auditor can capture data indicating the existence of fraud or error, so that the auditor can determine the appropriate scope of the audit, and determine which parts require a more in-depth audit. 0.808 0.312 Valid Audit Judgment 1. Your previous academic education/formal educational background influences the decision making of the audit judgment. 0.772 0.312 Valid 2. An understanding of procedures, guidelines (PMP), inspection standards (SPKN), provisions/regulations is very necessary to achieve the objectives and expectations of the audit assignment. 0.863 0.312 Valid 3. Auditors need to understand generally accepted accounting principles (PABU), especially Government Accounting Standards and related Technical Bulletins to support audit judgment in LK examinations. 0.836 0.312 Valid 4. The ability to communicate effectively with other parties, especially the auditee, also influences the audit judgment. 0.735 0.312 Valid 5. An auditor must be able to work together in a team to support judgment making. 0.741 0.312 Valid 6. The auditor's ability to carry out and develop audit techniques/procedures in the field will determine the judgment regarding the evaluation of audit evidence (adequacy and appropriateness of evidence). 0.770 0.312 Valid 7. The auditor's experience also influences his professional judgment in considering materiality, audit risk and assessing the fairness of the presentation of LK. 0.707 0.312 Valid 8. Audit assignment experience influences accuracy in analyzing a case/problem in the field. 0.806 0.312 Valid 9. An auditor who has audited more entities with a variety of business processes/complexity has a better audit judgment than an auditor with minimal experience. 0.728 0.312 Valid Corruption Detection 1. As an auditor, I have understood the differences in the 3 typologies of characteristics/types of fraud including misuse of assets, manipulation of financial statements (LK) and corruption. 0.630 0.312 Valid 2. The auditor must understand the forms/types of acts of corruption in accordance with laws and regulations. 0.632 0.312 Valid 3. The auditor must understand the causes/factors that encourage fraud (corruption) according to several existing theories, such as the fraud triangle, both fraud diamond, fraud pentagon, and fraud star. 0.634 0.312 Valid International Journal of Human Capital Management, Vol. 7 (1), June 2023 14 | P a g e Variable Question Items R count R table Ket 4. Before carrying out an audit, I must understand the entity's internal control structure and business processes to identify the risks of irregularities/fraud in the examined entity. 0.598 0.312 Valid 5. An auditor must be able to predict what forms of fraud (corruption) may occur. 0.574 0.312 Valid 6. As an auditor, I must be able to identify parties that can commit fraud (corruption). 0.586 0.312 Valid 7. Ineffective audit methods and procedures can result in failure in efforts to detect fraud (corruption). 0.687 0.312 Valid 8. I compare the statements and records submitted by the auditee with supporting evidence and empirical conditions to ascertain whether or not there are indications of irregular practices (corruption) in the implementation and accountability of the auditee's activities/programs. 0.560 0.312 Valid 9. The auditor is responsible for being able to detect corruption in the LK he audits and can communicate to interested parties, if he finds indications of fraud in the LK. 0.449 0.312 Valid Audit Quality 1. Audit planning is one of the important stages in LK audit activities. 0.746 0.312 Valid 2. The audit plan will determine the appropriate audit strategy and approach so that the audit objectives will be achieved effectively and efficiently. 0697 0.312 Valid 3, Audit planning includes technical preparations and inspection support according to the Audit Management Guidelines (PMP). 0.830 0.312 Valid 4. Collection of relevant, competent, sufficient, and material evidence has been documented in the inspection working papers (KKP) and supports the use of e-KKP. 0.686 0.312 Valid 5. Preparation of inspection findings and recommendations must refer to the SPKN. 0.686 0.312 Valid 6. The preparation of the inspection report (LHP) complies with the format specified in the report writing guidelines. 0.717 0.312 Valid 7. I follow the supervision carried out by the team leader for team members, technical controller for the audit team, quality controller/person in charge of the audit team, technical controller and deputy in charge as well as supervision at BPK RI members/leadership meetings for all assignments. 0.805 0.312 Valid 8. I immediately followed up the results of the supervision and tiered review in order to ensure the conformity of the audit process carried out with the SPKN. 0.653 0.312 Valid International Journal of Human Capital Management, Vol. 7 (1), June 2023 15 | P a g e Variable Question Items R count R table Ket 9. Every process of supervising, monitoring, consulting and reviewing during the audit process needs to be written down or documented in an orderly manner. 0.747 0.312 Valid The results of the correlation calculation for the validity test of measuring instruments are worth more than 0.312. Items that have a value above 0.312 are categorized as valid items, while items with a value below 0.312 are categorized as invalid which will then be set aside from further analysis. From the results of the above calculations, it can be concluded that all question items for each variable are valid. Reliability Test The reliability test in this study used the Cronbach's Alpha coefficient method. The Cronbach's Alpha coefficient is used to describe the variation of items such as the Likert scale. Reliability less than 0.6 is not good, while reliability 0.7 is acceptable, and above 0.8 is good. The results of the reliability test for each variable are as follows: Table 12 Variable Reliability Test Results (X1)/E-Audit Reliability Statistics Cronbach's Alpha N of Items 0941 9 Based on the results of the calculation above, the Cronbach's Alpha value was 0.941 for the E-Audit variable instrument (X1) because the Cronbach's Alpha value was above 0.9, it can be concluded that this study was very reliable. Table 13 Variable Reliability Test Results (X2) Auditt Judgment Reliability Statistics Cronbach's Alpha N of Items 0914 9 Based on the results of the calculation above, the Cronbach's Alpha value was 0.914 for the Audit Judgment variable instrument (X2) because the Cronbach's Alpha value was above 0.9, it can be concluded that this study was very reliable. Table 14 Variable Reliability Test Results (X3)/Corruption Detection Reliability Statistics Cronbach's Alpha N of Items 0.768 9 International Journal of Human Capital Management, Vol. 7 (1), June 2023 16 | P a g e Based on the calculation results above, the Cronbach's Alpha value was 0.768 for the Corruption Detection variable instrument (X3) because the Cronbach's Alpha value was above 0.7 , it can be concluded that the study was quite reliable. Table 15 Variable (Y)/Audit Quality Reliability Test Results Reliability Statistics Cronbach's Alpha N of Items 0.890 9 Furthermore, calculations on the variable Audit Quality (Y) obtained Cronbach's Alfa value of 0.890. This value is above 0.8, it is concluded that the research instrument is very reliable. Classical Assumption Test Results Normality test In this study, researchers used the Eviews Version 10 program to test whether the data were normally distributed or not. Observe the table below: Source: Eviews Data Processing Version 10 It can be seen in the table that the Sig. for Standardized residual is = 0.5189 then the value of Sig > 0.05 so it can be concluded that the variable data comes from a normally distributed population. Multicollinearity Test If multicollinearity is detected in the analysis, the estimated value of the regression coefficient obtained has a value that does not match the substance, so it can mislead interpretation. In addition, the standard error value for each regression coefficient can be infinite. To find out whether or not multicollinearity exists between the independent variables, it can be seen in the following table: International Journal of Human Capital Management, Vol. 7 (1), June 2023 17 | P a g e Table 16 Multicollinearity Test Results with TOL and VIF Methods Variance Inflation Factors Date: 01/16/22 Time: 14:40 Samples: 1 40 Included observations: 40 coefficient Uncentered Centered Variables Variances VIF VIF X1 0.008291 101.9978 2.598423 X2 0.014238 210.6670 3.024194 X3 0.015663 210.7352 2.222897 C 6.354175 101.1021 NA Source: Eviews Data Processing Version 10 The model is said to be free from multicollinearity if the VIF value is <10 or the tolerance value is getting closer to 1. From the table above it can be seen that the calculation of the Variance Inflation Factor (VIF) value shows that none of the independent variables has a VIF value of more than 10, besides that neither there is a Tolerance value <0.10 so it can be concluded that there is no multicollinearity between the independent variables (independent variables). Heteroscedasticity Test The heteroscedasticity test is used to test the regression model whether there is an inequality of variance from the residual one observation to another observation (Widarjono, 2018). If the significance value is > 0.05, then the regression model is declared to have no heteroscedasticity (Ghozali, 2018), which can be seen in the following table: Table 17 Heteroscedasticity Test Results Heteroskedasticity Test: Glejser F-statistics 0.360742 Prob. F (3,36) 0.7817 Obs*R-squared 1.167379 Prob. Chi-Square (3) 0.7608 Scaled explained SS 1.047909 Prob. Chi-Square (3) 0.7897 Source: Eviews Data Processing Version 10 Based on the table above, the results of the independent variable Glejser test have a Prob value of F0.7817 > 0.05, it is said that X1, X2 and X3 do not occur heteroscedasticity, meaning that there is no similarity of variance from the residual one observation to another and this research is good/feasible to use for study. Research Hypothesis Testing Multiple Regression Analysis Multiple regression analysis is used to determine whether variable (Y) can be caused by variable (X). The formula used is: Y = a + b1 X1 + b2 X2 + b3 X3 International Journal of Human Capital Management, Vol. 7 (1), June 2023 18 | P a g e As for making it easier to do calculations, researchers used Eviews 10 software. The following is the workmanship using Eviews: Table 18 Regression Coefficient Dependent Variable: Y Method: Least Squares Date: 01/16/22 Time: 14:43 Samples: 1 40 Included observations: 40 Variables coefficient std. Error t-Statistics Prob. X1 0.248556 0.091054 2.729764 0.0097 X2 0.296027 0.119323 2.480890 0.0179 X3 0.344193 0.125153 2.750177 0.0093 C 4.784126 2.520749 1.897899 0.0658 Source: Eviews Data Processing Version 10 Based on the table above, the regression equation is Y = 4.784 + 0.2485 X1 + 0.2960 X2 + 0.3441 X3 The multiple linear regression analysis equation above can be explained as follows  The constant of 4,784 states that if the value of the independent variables X1, X2 and X3 is ignored or has a value of 0, then the value of Y is fixed at 4,784.  The coefficient of the regression equation (X1) of 0.2485 states that for every increase in the value of X1, 1 unit, the value of Y will increase by 0.2485 units.  The coefficient of the regression equation (X2) of 0.2960 states that for every increase in the value of X2, 1 unit, the value of Y will increase by 0.2960 units.  The coefficient of the regression equation (X3) of 0.3441 states that for every increase in the value of X3, 1 unit, the value of Y will increase by 0.3441 units. Statistical T Test This test was conducted to test the significance of the coefficients of each independent variable partially to the dependent variable. Namely the independent variable X to the dependent variable (Y) with the hypothesis: H0: There is no significant effect of X on Y H1: There is a significant influence of X on Y The decision-making rules used are as follows: If sig < 0.05, then reject Ho means significant If sig > 0.05, then accept Ho means it is not significant The results of the t test calculations can be seen in the following table: International Journal of Human Capital Management, Vol. 7 (1), June 2023 19 | P a g e Table 19 T-test results Dependent Variable: Y Method: Least Squares Date: 01/16/22 Time: 14:43 Samples: 1 40 Included observations: 40 Variables coefficient std. Error t-Statistics Prob. X1 0.248556 0.091054 2.729764 0.0097 X2 0.296027 0.119323 2.480890 0.0179 X3 0.344193 0.125153 2.750177 0.0093 C 4.784126 2.520749 1.897899 0.0658 R-squared 0.787540 Mean dependent var 30.52500 Adjusted R-squared 0.769835 SD dependent var 3.304911 SE of regression 1.585548 Akaike info criterion 3.854377 Sum squared residue 90.50269 Schwarz criterion 4.023265 Likelihood logs -73.08754 Hannan-Quinn criter. 3.915442 F-statistics 44.48120 Durbin-Watson stat 1.336392 Prob(F-statistic) 0.000000 Source: Eviews Data Processing Version 10 Table 4.10 above shows the partial effect of variable X on variable Y. By comparing the value of Sig. to α = 0.05 it can be explained:  There is a significant influence between variable X1 and variable Y due to a significance value of 0.0097 <0.05 so that H0 is rejected and H1 is accepted.  There is a significant influence between variable X2 and variable Y due to a significance value of 0.0179 <0.05 so that H0 is rejected and H1 is accepted.  There is a significant influence between variable X3 and variable Y due to a significance value of 0.0093 <0.05 so that H0 is rejected and H1 is accepted. Statistical F test Testing the significance using Eviews can be seen in table 4.10 with the hypothesis: H0: There is no significant simultaneous effect of X1, X2 and X3 on Y H1: there is a significant simultaneous effect of X1, X2 and X3 on Y The decision-making rules used are as follows: If Sig. < 0.05, then the double effect is significant. If Sig. > 0.05, then the double effect is not significant. In the table above there is a sig column which is the significance value of a multiple effect between variables or the simultaneous effect of the regression model, the Sig value is obtained. = 0.000. Based on the calculation results, the result is that the research sig is 0.000 <0.05, thus H0 (There is no significant simultaneous effect X1, X2 and X3 on Y) is rejected and H1 is accepted because there is a significant simultaneous effect X1, X2 and X3 on Y . Test R2 (Coefficient of Determination) A small R² value indicates that the ability of the independent variable to explain the dependent variable is very limited. The calculation results obtained the value of the International Journal of Human Capital Management, Vol. 7 (1), June 2023 20 | P a g e coefficient of determination, it can be seen in Table 4.14 it is known that the R-Square number is 0.787. This figure states that 78% of the variable Y is influenced by X1, X2 and X3 together, while the other 22% is determined by other variables not explained in this study. DISCUSSION E-Audit Has a Positive Effect on Audit Quality In the t test or partial test the e-audit variable has a significant value of prob. 0.0097 <0.05 (because it is significantly less than 0.05) so the e-audit variable has a positive and significant effect on audit quality. This means that if the e-audit is good, the audit quality will also increase. The research results of Amerthajaya & Aryani (2016) show that TABK (e-audit) has a positive and significant effect on audit quality at BPK RI Representative for Bali Province. This means that the more often the auditor conducts inspections and carries out the audit process using TABK (e-audit), the better the resulting audit quality. The use of TABK (e-audit) results in precision and accuracy and saves an auditor's time compared to using the manual. Audit Jugement Positive Influence on Audit Quality In the t test or partial test the e-audit variable has a significant value of prob. 0.0179 < 0.05 (because it is significantly less than 0.05) then the audit judgment variable has a positive and significant effect on quality. This means that if the audit judgment is good, the audit quality will also increase. The results of this study are supported by the research of Sulistyawati (2019) that audit judgment is attached to every stage in the FI audit process, starting from accepting audit engagements, planning audits, implementing audit testing and audit reporting. The results of this study are supported by the research of Sulistyawati (2019) that audit judgment is attached to every stage in the FI audit process, starting from accepting audit engagements, planning audits, implementing audit testing and audit reporting. Audit judgment is needed in the audit process because the audit is not carried out on all evidence of transactions within the company, but only an adequate sample. Based on this sample of evidence, the auditor will provide an assessment of LK, so that it can be said that audit judgment greatly determines the quality of the audit. The results of this study are in line with the research of Pektra & Kurnia (2015) and Liburd (2015) in Gracea et al. (2017) that in making a judgment, the auditor must evaluate the evidence collected, then the evidence must be valid and relevant. so that it can be said that audit judgment greatly determines the quality of the implementation of the audit. The results of this study are in line with the research of Pektra & Kurnia (2015) and Liburd (2015) in Gracea et al. (2017) that in making a judgment, the auditor must evaluate the evidence collected, then the evidence must be valid and relevant. so that it can be said that audit judgment greatly determines the quality of the implementation of the audit. The results of this study are in line with the research of Pektra & Kurnia (2015) and Liburd (2015) in Gracea et al. (2017) that in making a judgment, the auditor must evaluate the evidence collected, then the evidence must be valid and relevant. Corruption Detection Has a Positive Impact on Audit Quality In the t test or partial test the e-audit variable has a significant value of prob. 0.0093 < 0.05 (because it is significantly less than 0.05) then the corruption detection variable has a positive and significant effect on quality. This means that if the detection of corruption is good, the quality of the audit will also increase. The results of this study are in line with the research of Alim, et al (2007) and Marcellina (2009) in Toufiq (2017) proving that one indication of good audit quality is if fraud (corruption) in the audit can be detected. the auditor's ability to detect fraud (corruption) will be better after fraud (corruption) is detected, the auditor is not involved in securing these fraudulent (corruption) practices. This is consistent with the research of Indriyani & Hakim (2021) that the ability to detect fraud (corruption) is a manifestation of the quality of an auditor. In the International Journal of Human Capital Management, Vol. 7 (1), June 2023 21 | P a g e relationship between variables with audit quality as independent variables and fraud detection (corruption) as the dependent variable with research sampling on BPK RI auditors, it is known that both have a positive correlation/relationship as in the research of Maryulianti (2015), Purba (2021) and Purba & Omar (2021). This research proves that audit quality has a positive effect on fraud (corruption) detection. it is known that both of them have a positive correlation/relationship as in the research of Maryulianti (2015), Purba (2021) and Purba & Umar (2021). This research proves that audit quality has a positive effect on fraud (corruption) detection. it is known that both of them have a positive correlation/relationship as in the research of Maryulianti (2015), Purba (2021) and Purba & Umar (2021). This research proves that audit quality has a positive effect on fraud (corruption) detection. CONCLUSION The conclusion of this study is that there is a significant influence between e-audit on audit quality. The results of the analysis also show that the regression coefficient indicates that e-audit has a positive influence on audit quality. This means that if the e-audit is getting better, it will improve audit quality. There is a significant influence between audit judgment on audit quality. The results of the analysis also show that the regression coefficient indicates that audit judgment has a positive influence on audit quality. This means that if the audit judgment is getting bet ter, it will improve audit quality. There is a significant influence between the detection of corruption on audit quality. The results of the analysis also show that the regression coefficient indicates that corruption detection has a positive influence on audit quality. This means that the better the detection of corruption, the better the quality of the audit. The practical implications of this research are to provide input to the BPK RI, so that each auditor can increase his/her ability to master the e-audit system, improve the quality of human resources through further study policies for auditors, and increase the ability and professional skepticism in detecting indications of corruption in the audit process of LK. The limitations of this study are in the scope or sample in the research and are limited to the method of distributing questionnaires (surveys) to collect data. The researcher's suggestions for future researchers are expected to broaden the scope or sample used, so that the research results can provide results with a higher/accurate level of generalization. And should not only use a questionnaire in collecting data, but accompanied by a combination of other primary research, such as observation and interviews. REFERENCES ACFE Indonesia Chapter. 2020. Indonesia Fraud Survey 2019. Jakarta. ACFE Indonesia Chapter. Audit Board of the Republic of Indonesia. 2016. Performance Report 2016. Jakarta. Audit Board of the Republic of Indonesia. 2017. Performance Report 2017. Jakarta. Audit Board of the Republic of Indonesia. 2018. Performance Report 2018. Jakarta. Audit Board of the Republic of Indonesia. 2019. Performance Report 2019. Jakarta. Audit Board of the Republic of Indonesia. 2020. 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