Efforts of Performance Optimization: The Experiment on Ten Accounting Datasets - Karya Saputra Domas, Zico; Rizkiawan, M.; Rakhmadi, Roby (Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 2022)
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 optimization s 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.