Abstract

The identification of ethical violations committed by the auditor is very difficult to do. Artificial intelligence offers anomaly detection as an alternative method for detecting the opinion anomaly which can be an early indicator of the opinion trading occurrence. This paper proposes the use of original features from public sector rather than the use of modified features from the private sector to be applied in opinion detection in public sector. By using 60% Holdout validation, 1-NN classification showed that original featured from the public sector outperformed the modified featured from the private sector by 5.82% through 13.10% under F-Measure Criterion and by 4.22% through 9.56% under AUC criterion.

Original languageEnglish
Title of host publicationProceedings - 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2017
EditorsHatib Rahmawan, Mochammad Facta, Munawar A. Riyadi, Deris Stiawan
PublisherInstitute of Advanced Engineering and Science
ISBN (Electronic)9781538605486
DOIs
Publication statusPublished - 22 Dec 2017
Event4th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2017 - Yogyakarta, Indonesia
Duration: 19 Sept 201721 Sept 2017

Publication series

NameInternational Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
Volume2017-December
ISSN (Print)2407-439X

Conference

Conference4th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2017
Country/TerritoryIndonesia
CityYogyakarta
Period19/09/1721/09/17

Keywords

  • Financial statement
  • KNN
  • Opinion detection
  • Original features
  • Public sector

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