This study aims to develop an accurate predictive model for identifying students at risk of academic dropout using Decision Tree and Random Forest algorithms. The research utilizes a publicly available dataset sourced from Kaggle, which includes academic and demographic features such as GPA, attendance, credit load, financial aid status, and exam scores. The methodology involves several stages: data collection, preprocessing (handling missing values, encoding categorical variables, and feature scaling), model training, and evaluation using performance metrics such as Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. Results show that the Random Forest algorithm outperforms Decision Tree in terms of accuracy and robustness, with notable feature importance on math, reading, and writing scores. The findings highlight the potential of machine learning in early detection of dropout risks and provide actionable insights for academic institutions to design timely interventions. This research contributes to the growing field of educational data mining and supports data-driven decision-making processes in higher education management.

With the times, cash transactions that used to use cash are now turning to credit cards. However, the increasing use of credit cards presents challenges, especially in maintaining customer loyalty. Customer churn is the loss of customers within a certain period for various reasons. Logistic Regression is a machine learning algorithm that studies the relationship between a dependent variable and several independent variables and Extreme Gradient Boosting (XGBoost) is a Gradient tree-boosting algorithm that offers out-of-core learning and sparsity awareness.The purpose of this study is to compare the performance between Logistic Regression and Extreme Gradient Boosting (XGBoost) algorithms in predicting customer churn in credit card services using evaluation metrics such as accuracy, precision, recall, and F1-score. Based on the research results, it can be concluded that XGBoost has better performance in all evaluation metrics, both in terms of precision, recall, F1-score, and accuracy. Based on the research, XGBoost shows superior performance compared to Logistic Regression in all evaluation metrics.