Handling Imbalanced Data Sets Using SMOTE and ADASYN to Improve Classification Performance of Ecoli Data Sets

In this digital era, machine learning is a technology that in demand by organizations and individuals. the age of data information, ability to process efficiently needed. As amount grows, there are various problems learning. One them with increasing data, class imbalance also often found. Class condition where dominates another class, one example case when positive value has less number than negative class. The categorized as minority while dataset called majority can affect classification performance bad way, so handling imbalanced classes needed improve results. Classification using Random Forest satisfactory results, well implementing SMOTE ADASYN sampling methods because they highly popular easy implement. best model produced study applies oversampling on 10% IR balanced accuracy 98.75%, result applying 13% 99.03%.