Research Article
Classification of Firm External Audit Using Ensemble Support Vector Machine Method
@INPROCEEDINGS{10.4108/eai.2-5-2019.2284605, author={Dewiani Dewiani and Armin Lawi and Muhammad Idris Rifai Sarro and Firman Aziz}, title={Classification of Firm External Audit Using Ensemble Support Vector Machine Method}, proceedings={1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia}, publisher={EAI}, proceedings_a={ICOST}, year={2019}, month={6}, keywords={classification external audit fraudulent support vector machine (svm) ensemble bagging}, doi={10.4108/eai.2-5-2019.2284605} }- Dewiani Dewiani
Armin Lawi
Muhammad Idris Rifai Sarro
Firman Aziz
Year: 2019
Classification of Firm External Audit Using Ensemble Support Vector Machine Method
ICOST
EAI
DOI: 10.4108/eai.2-5-2019.2284605
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.
