Comparison of SMOTE and ADASYN in Optimizing Random Forest Model for Imbalanced Financial Ratio Bankruptcy Prediction - Novanda Rizky Ramadhana; Fuad Muhajirin Farid; Yeni Rahkmawati (Universitas Teknokrat Indonesia, 2026)
Classification is a data analysis process that can predict classes based on predefined characteristics. In the era of big data, classification can be performed using machine learning. The problem of machine learning in classification analysis is imbalance data which often affect model performance. SMOTE and ADASYN are oversampling techniques to solve this problem. This study aims to evaluate the effectiveness of SMOTE and ADASYN in improving the performance of the Random Forest model on imbalanced data in the case of company bankruptcy using financial ratios. Models were built using training data with various splitting data and oversampling techniques. Then, the resulting models will be tested using testing data. The results show that the best model was achieved with a combination of splitting data 70:30 using SMOTE technique, which produced the highest f1-score of 40.57%, compared to ADASYN technique with 36.11% (a decrease of 4.46%), and without oversampling techniques with 19.51% (a decrease of 21.06%). The findings indicate SMOTE and ADASYN can identify minority values which are the main problem of imbalance data, with SMOTE showing better performance compared to ADASYN.