Penerapan Artificial Intelligence, Kompetensi Auditor, dan Independensi Auditor terhadap Kualitas Audit

Abstract

This study aims to analyze the effect of Artificial Intelligence (AI) Implementation, Auditor Competence, and
Auditor Independence on Audit Quality in Public Accounting Firms (KAP) in East Jakarta. The approach
used is a quantitative explanatory with a cross-sectional design. Primary data were collected through a 5-
point Likert scale questionnaire distributed to auditors at 11 KAPs in East Jakarta, and successfully obtained
70 respondents who met the purposive sampling criteria. Data analysis was carried out using the Partial
Least Squares – Structural Equation Modeling (PLS-SEM) approach using SmartPLS version 4. The results
of the measurement model test showed that all indicators had outer loadings above 0.70 and Average
Variance Extracted (AVE) above 0.50, so that convergent validity was met. Discriminant validity was also
confirmed through the Fornell-Larcker criteria and the HTMT ratio. The model was able to explain 71.9%
of the variation in Audit Quality (R² Adjusted = 0.719). The bootstrapping results showed that the three
independent variables had a positive and significant effect on Audit Quality. Auditor Independence has the
highest path coefficient (β = 0.378; t = 4.296; p < 0.001), followed by Auditor Competence (β = 0.363; t =
5.010; p < 0.001), and AI Implementation (β = 0.271; t = 3.219; p = 0.001). This confirms that improving
audit quality requires comprehensive strengthening of both auditor competence and independence as well as
the adoption of AI technology. The research provides implications for Public Accounting Firms (KAP),
regulators, and auditors to continue developing competence, maintaining independence, and increasing
technological literacy