Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Owner : Riset dan Jurnal Akuntansi
Chalissa, Andrea Titania
Unknown Affiliation
Economics, Econometrics & Finance

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Mendeteksi Faktor-faktor Pressure Terhadap Kecurangan Laporan Keuangan Menggunakan Artificial Neural Network Chalissa, Andrea Titania; Suryani , Elly
Owner : Riset dan Jurnal Akuntansi Vol. 8 No. 1 (2024): Artikel Riset Januari 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/owner.v8i1.1895

Abstract

Fraudulent financial statements are the result of misstatements resulting from intentional acts or omissions, which could materially mislead readers of the financial statements. The focus in this research is to determine the most important pressure factors in detecting fraudulent financial statements. Pressure is one of the fraud risk factors in the fraud triangle. Pressure is a condition felt by management due to incentives to commit fraud, consisting of: financial stability by proxy (GPM, ACHANGE, SCHANGE, CATA, SALAR, SALTA, INVSAL), external pressure (LEV, FINANCE, FREEC), personal financial need (OSHIP), and financial target (ROA). Data collection method using secondary data on the manufacturing sector firms that are publicly listed on the Indonesia Stock Exchange in 2017-2021. The research method used is quantitative and the sampling method uses a purposive sampling technique, obtained 137 sample companies with 685 total data observed. Data were analyzed using an Artificial Neural Network. The findings indicated that the gross profit margin (GPM), cash flow from operating to total assets (CATA), demand for financing (FINANCE), leverage (LEV) and return on total assets (ROA) is the most important proxy in detecting fraudulent financial statement, while other proxies are not too important in detecting fraudulent financial statements.
Co-Authors Suryani , Elly