Enhancing The Quality of College Decisions Through Decision Tree and Random Forest Models - Sitti Rahmah Jabir; Huzain Azis; St. Hajrah Mansyur (Journal of Embedded Systems, Security and Intelligent Systems, 2024)
One of the key indicators of the growth of a college is the number of students that are enrolled in the institution on an annual basis. Student enrollment is a crucial element in the growth of a college, particularly in the case of private institutions. When examining students' aspirations for higher education, several studies use data mining techniques to forecast the interests of students who will pursue college. Researchers adopt various ways to extract valuable information from data. Prior research has shown that the decision tree technique outperforms alternative methods. The random forest, in addition to the decision tree, is often used for predicting data mining tasks. Given the above background information, the author will conduct a study titled "Comparative Analysis of Decision Tree and Random Forest Algorithms in Predicting College Interests." According to the study findings, the decision tree outperforms the random forest in terms of outcomes. The accuracy of the decision tree model is 0.81, whereas the accuracy of the Random Forest model is 0.74. All in all, the Decision Tree approach will be used as the ultimate outcome for the implementation of Business Analytics.