An Opinion Anomaly Detection Using K-Nearest Neighbours on Public Sector Financial Reports

Abstract

The Main Inspectorate (Itama) as internal auditor of BPK RI is obliged to protect the credibility and the honor of its institution. The opinion of financial statements is one of the BPK RI's products that become popular because of frequent bribery cases related to it. Typically, the bribe was given to change the opinion of the financial statements from an examined entity. The anomaly detection method becomes one of the alternative methods for filtering out reports with "problem" opinions to be examined more deeply by Itama. KNN, SVM-RBF Kernel, and J48 method were used for the classification of 150 data of local government financial statements. The validation used in this paper was 60% hold-out validation (60% data for test data and the rest for training data). This paper showed that the KNN classifier (AUC=61.11%) was superior compared to another classifier, but still classified as "poor classification"