Classification of Firm External Audit Using Ensemble Support Vector Machine Method

Dewiani, Dewiani and Lawi, Armin and Sarro, Muhammad Idris Rifai and Aziz, Firman (2019) Classification of Firm External Audit Using Ensemble Support Vector Machine Method. In: ICOST 2019, 2-3 May 2019, Makassar, Indonesia.

[img]
Preview
Text (PDF)
eai.2-5-2019.2284605.pdf - Published Version

Download (223kB) | Preview

Abstract

Financial fraud is an important problem because it can detrimental firm in the modern business world. An audit is carried out to prevent and be responsible for detecting fraud. External audit is one of the audit practices conducted outside of the firm internal audit by visiting firms in carrying out the work of financial report audit data. The application of machine learning can be used as a solution in the use of data analysis methods needed to solve these problems. This study proposes a Support Vector Machine (SVM) method by combining the Ensemble Bagging model to improve single classification performance. Data comes from 14 different corporate sectors with 777 records. The results showed that the Ensemble Bagging model could improve the accuracy of classification performance from the Support Vector Machine (SVM) method and achieved the highest accuracy of 89.95%. Based on the results of the accuracy obtained, the Support Vector Machine (SVM) method with the Ensemble Bagging model can be used to detect fraud in the firm.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: classification external audit fraudulent support vector machine (svm) ensemble bagging
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 01 Oct 2021 13:48
Last Modified: 01 Oct 2021 13:48
URI: https://eprints.eudl.eu/id/eprint/7358

Actions (login required)

View Item View Item