In the big data and digitalization era, fast-accurate decision-making has become a basic need, so data mining has a crucial role. The decision tree algorithm is quite commonly applied for classification functions, but performance level must always be evaluated for optimizing accuracy rate. Several optimization methods to accommodate these objectives include GA-bagging, PSO-bagging, forward selection, backward elimination, SMOTE, under-sampling, GA-Adaboost, and ABSMOTE-WIGFS. The results of the decision tree experiment on ten types of accounting-finance datasets used in this study obtained results with an average accuracy of 83.46%, an average precision of 65.64%, and an average AUC of 71.9%, while the majority of various optimizations are proven in improving the performance of decision tree algorithm where the application of ABSMOTE-WIGFS method is proven in providing the best rate with an average accuracy 87.71%, an average precision 87.09%, and an average AUC 84.87%, so it can be concluded that various optimization efforts are worth to be applied in case of accounting-finance themes for increasing the performance rate. Furthermore, the next research can prove these methods in other fields outside of accounting cases.  

Several prominent reports have highlighted the unsatisfactory level of anti-corruption transparency for the private sector in Indonesia. Hence, the anti-corruption vision is still an aspect that deserves to be campaigned for to form an advanced and just civilization. This study aims to obtain a pattern of knowledge in predicting the level of transparency of disclosure of fraud violations based on a data mining approach. The classification function algorithm in this study is a decision tree which is then compared with other classification function algorithms, naive Bayes, and k-nn. The sample in this study is 141 companies combined in the construction, mining, and banking sectors, which are on the IDX for the 2019 period. As a result, the decision tree algorithm provides the second-best performance in predicting the level of corporate transparency, namely an accuracy of 70.92% and an AUC level of 0.740. Then in terms of different tests, the decision tree algorithm is in the same cluster as the algorithm with the best performance because the t-test results show no significant difference. Based on the pattern generated by the decision tree algorithm, the elements of opportunity, pressure, and arrogance are considered key factors in predicting the level of transparency of disclosure of fraud violations. One of them can be interpreted that a company that is supervised by a minimum of four independent commissioners means company tends to be predicted to be more daring in disclosing anti-corruption information in its annual report to the wider public. This study also recommends that every authorized institution in Indonesia can apply a data mining algorithm approach in utilizing the advantages of each agency's internal data volume to map anti-corruption cultural socialization strategies in private sector companies.

This study analyzes the influence of Fraud Hexagon and Corporate Governance on fraudulent financial reporting. Independent variables in this study are Fraud Hexagon and corporate governance while dependent variable is fraudulent financial reporting. Samples in this study are state-owned and affiliated entities listed on Indonesia Stock Exchange. Data were analyzed using logistic regression analysis. Test results show that opportunity and rationalization have a very significant effect on fraudulent financial reporting, pressure and capability have a significant effect on fraudulent financial reporting, and collusion has a quite significant effect on fraudulent financial reporting, while arrogance and corporate governance has no effect.