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All Journal Jurnal Ekonomi Jurnal Media Wahana Ekonomika EQIEN - JURNAL EKONOMI DAN BISNIS MBIA Jemasi Jurnal Ekonomi Manajemen dan Akuntansi AKUNTABILITAS: Jurnal Penelitian dan Pengembangan Akuntansi BALANCE Jurnal Akuntansi dan Bisnis Jurnal Ilmiah Akuntansi dan Bisnis Accountia Journal (Accounting, Trusted, Inspiring and Authentic Journal) EJOIN : Jurnal Pengabdian Masyarakat Kajian Akuntansi SULUH ABDI : Jurnal Ilmiah Pengabdian Kepada Masyarakat Accounting Analysis Journal Jurnal Pengabdian Masyarakat, Akuntansi, Bisnis & Ekonomi (JPMABE) Balance : Jurnal Akuntansi dan Bisnis Fundamental and Applied Management Journal
Djuniar, Lis
Fakultas Ekonomi Dan Bisnis Universitas Muhammadiyah Palembang
Agriculture, Biological Sciences & Forestry Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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The Role of Data Mining, Machine Learning, Artificial Intelligence, and Digital Forensic in Indonesian Public-Sector Fraud Detection Betri; M. Amin Dwi Putra; Fenty Astrina; Lis Djuniar; M. Faris Afif; Rendra Bakti; Mizan; Dewi Puspasari; Rahmat Basuki
Fundamental and Applied Management Journal Vol. 4 No. 2 (2026): June
Publisher : Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/famj.v4i2.994

Abstract

The Role of Data Mining, Machine Learning, Artificial Intelligence, and Digital Forensic in Indonesian Public-Sector Fraud Detection in the Indonesian public-sector audit environment. The study aims to evaluate whether advanced analytical technologies improve auditors’ effectiveness in detecting fraudulent financial reporting and whether Digital Forensic strengthens or weakens the relationships between these technologies and fraud detection performance. This study employed a causal associative quantitative approach using primary data collected through questionnaires distributed to auditors at the Financial and Development Supervisory Agency (BPKP) representative offices across Sumatra, Indonesia. The population consisted of 290 auditors, and the sample was selected using a simple random sampling technique based on the Slovin formula. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The analysis included outer model evaluation, inner model assessment, hypothesis testing, and moderation analysis. The results indicate that Data Mining has a significant positive effect on Financial Statement Fraud Detection. In contrast, Machine Learning and Artificial Intelligence do not significantly influence fraud detection effectiveness. Furthermore, Digital Forensic does not strengthen the relationships between Data Mining, Machine Learning, Artificial Intelligence, and fraud detection. Instead, the moderating effects of Digital Forensic tend to weaken these relationships within the current audit environment. The structural model demonstrates satisfactory explanatory and predictive capability in explaining variations in fraud detection performance.
Co-Authors Abid Djazuli, Abid Afrida, Anggrelia Ambo Upek Bakti Setyadi Bambang Suryadi Betri Betri, Betri Buana, Adelia Satiara Citra Dewi Darma Yanti Dewi Puspasari Dewi Sari Dina Aziza Putri Ely Mayora EMAN SATRIA Fahira, Anisyah Okta Fenty Astrina Fitriah, Wani Hafidz, Ridho Herudiansyah, Gumar Ida Zuraida, Ida Ida Zuraidah Indah Purnama, Beauty Indra Satriawan Indriasari, Desi Ita Ningsih Kusumawaty, Mia Ladewi, Yuhanis M. Amin Dwi Putra M. Faris Afif Maidiana Astuti Handayani Melhanu, Sandy Pradana Mizan Nina Sabrina Nunung Nurhayati Rahmat Basuki RENDRA BAKTI Rosy Armaini Safitri, Ervita Sari Puspa Dewi Sirajuddin, Betri Sumita Sumita Sunardi Wani Fitriah Wani Wani, Wani Fitriah Welly, Welly Welly, Welly Welly, Welly Yanti, Darma Yeni, Mesri Yulian Sahri Yusnaini Yusnaini