Predicting Tuition Fees for University of Raharja Students Using Artificial Intelligence

A university hopes that every semester students are able to pay tuition fees properly and smoothly. It is hoped that the institution will be able to maintain monthly cash flow so that operational and maintenance costs can be met. Therefore, this research was conducted to predict and fulfill institutional cash-in from the SPP payment method either in cash, installments or sometimes late payments every semester. In predicting the method of payment of tuition fees, using student profile data (name, name, study program) and grade point average for each semester for the past 5 semesters and the method of payment (cash and installments). Using the Naive Bayes (NB) algorithm, Support Vector Machine (SVM), and Deep Learning, this research aims to predict college costs. Classification Prediction Model with Naive Bayes used in the research Method, Confusion Matrix Performance NB produced by SVM and Deep Learning, Confusion Matrix Performance SVM produced by SVM and Deep Learning, and Confusion Matrix Performance Deep Learning produced by Deep Learning.