Department Recommendation System Using Web Based Naive Bayes Gaussian Algorithm

Abstract

This study aims to develop a web-based department recommendation system using the Gaussian Naive Bayes algorithm to address the issue of student confusion in selecting majors at STIKI Malang. Limited career guidance and information pose challenges for high school graduates in making informed decisions about their suitable majors based on interests and potentials. In this research, training data from 107 active students and graduates are utilized to provide recommendations based on various attributes such as gender, current major, skills, hobbies, reasons for pursuing higher education, program selection motives, interest in mathematics, and interest in English. The Gaussian Naive Bayes method successfully classifies continuous data with an accuracy of 87,85%, effectively dealing with the uncertainty in major selection. It is hoped that this system will assist high school graduates in choosing appropriate majors, reducing major selection errors, and optimizing potential.