Comparison of Decision Tree and Random Forest in Book Loan Classification for Universitas Esa Unggul Bekasi

Abstract

This study compares the performance of Decision Tree and Random Forest algorithms in classifying the status of book loans at the Library of Universitas Esa Unggul Bekasi Campus. The objective of this research is to build a predictive model capable of identifying potential late returns as a basis for more proactive decision-making. The dataset used consists of 1,210 historical book loan records from the period of January to May 2025. Preprocessing stages included data cleaning, feature engineering, encoding of categorical variables, and handling class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE). Classification models were built and evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics. Test results showed that the Random Forest algorithm had superior and more stable performance compared to Decision Tree, especially in detecting the minority class of late loans. After hyperparameter tuning, Random Forest achieved higher F1-score and recall values without a significant drop in precision. These findings indicate that Random Forest is more effective for handling imbalanced and complex loan data. Therefore, the Random Forest algorithm is recommended as a decision support system to improve service efficiency, collection availability, and library management quality.