APPLICATION OF RANDOM FOREST METHOD TO PREDICTION OF STUDENT CANDIDATES ACCEPTED IN THE SNMPTN PATHWAY (CASE STUDY AT UNIVERSITAS LAMBUNG MANGKURAT)

Abstract

Academic planning is an essential aspect that needs to be carried out to plan the teaching and learning process in a campus, such as the admission of new students through the SNMPTN. At Lambung Mangkurat University, it is known that the partici- pants who took the SNMPTN during the 2021 admission period amounted to 7,703. It is recognized that selecting candidates is not easy due to many prospective students passing the selection but choosing to withdraw. Therefore, a system is needed to predict the graduation of prospective students through the SNMPTN. This research utilizes the Random Forest method to predict the graduation of prospective students through the SNMPTN. The data will be divided into 90% for training data and 10% for testing data, then using classification parameters with 300 n-estimators. The research yielded a precision value of 0.72, recall value of 0.46, and system accuracy of 89.3% For further research recommendations, other prediction methods can be explored to forecast the graduation rate of students through the SNMPTN, or a comparison of methods can be con- ducted to determine which method is more effective.