<![CDATA[The issue of class imbalance often poses a challenge in data analysis, where the number of instances in the majority class is significantly higher than that in the minority class. This can lead classification models to be biased towards predicting the majority class, resulting in low accuracy in identifying the minority class. This research aims to implement the Logistic Regression (LR) algorithm combined with the Clustering Based Undersampling (CBU) method as an undersampling technique, feature selection, and optimization using Genetic Algorithm (GA) in classifying KIP-College scholarship data at Muhammadiyah University of East Kalimantan. In addition, this research also evaluates the performance of the model with 10-Fold Cross Validation and Confusion Matrix techniques as accuracy metrics and aims to overcome the problem of class imbalance in the data of scholarship recipients (KIP) at Muhammadiyah University of East Kalimantan. The data used consists of 1075 records with 37 features related to the socio-economic factors of scholarship recipients. The results from the application of the CBU method indicate an increase in the accuracy of the Logistic Regression model from 62.51% to 67.68%. Furthermore, the combination of GA and CBU has providing more stable results in classifying minority classes. It is hoped that this research can make a significant contribution to the development of a more accurate and efficient scholarship recipient selection system, as well as serve as a reference for future studies in the fields of data mining and machine learning.]]>
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