@inproceedings{7476e94be9da4a4682f080020d1ac14f,
title = "Opinion detection of public sector financial statements using K-nearest neighbors",
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.",
keywords = "Financial statement, KNN, Opinion detection, Original features, Public sector",
author = "Arianto, {Ahmad Dwi} and Achmad Affandi and Nugroho, {Supeno Mardi Susiki}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 4th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2017 ; Conference date: 19-09-2017 Through 21-09-2017",
year = "2017",
month = dec,
day = "22",
doi = "10.1109/EECSI.2017.8239163",
language = "English",
series = "International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)",
publisher = "Institute of Advanced Engineering and Science",
editor = "Hatib Rahmawan and Mochammad Facta and Riyadi, {Munawar A.} and Deris Stiawan",
booktitle = "Proceedings - 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2017",
}