Performance Comparison K-Nearest Neighbors and Random Forest on Predicting The Performance New Polimedia Student Admissions

Abstract

New student admissions are at the forefront of the school's operational process. the success of each college's input stems from this. Polimedia always conducts new student admissions every year with various strategies used. Polimedia has 23 study programmes that can enable it to move in the creative industry that can be utilised by the community. in this study, a strategy using a prediction algorithm is used to be able to see the possible opportunities that occur if implemented in the coming year. with a dataset of 3738 data received by new students, an analysis will be carried out on prospective students who have re-registered or who have not re-registered. The classification model with 2 classes will be used. by conducting a data analysis process using exploratory data analysis (EDA) and also performing data cleansing so that the data modelling process runs well. The method used uses the main model of K-Nearest Neighbors by comparing with other machine learning models such as decision tree and random forest. It is expected that this research can produce high accuracy values 86.90% with powerful machine learning model comparisons. This research is also expected to be a reference for other studies that also conduct performance testing processes with machine learning models using various objects.