A Study Concentration Selection With a C4.5 Algorithm, KNN, and Naive Beyes

Muhammad Busyro; Tri  Astuti; Deuis Nur  Astrida

doi:10.52436/1.jutif.2025.6.4.4157

Authors

Muhammad Busyro Technical Informatics, Faculty of Computer Science, Universitas Amikom Purwokerto, Indonesia
Tri Astuti Departement of Informatics, Faculty of Computer Science, Amikom Purwokerto University, Indonesia
Deuis Nur Astrida Departement of Informatics, Faculty of Computer Science, Amikom Purwokerto University, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.4.4157

Keywords:

Course Selection, Decision Tree, K-Neareset Naighbors, Naïve Beyes

Abstract

The course of concentration is a crucial aspect for students at the university amikom purwokerto.This decision doesn't just affect their academic journey., but also determine their readiness in the face of the working world.Various factors that affect the concentration selection, the challenges that students face, as well as solutions to help them choose concentrations that fit their interests and career goals.There are still many students who have been confused in deciding which courses best fit their interests and career goals..This confusion is often caused by a lack of adequate information and proper guidance. This study attempts to analyze the lecture amikom purwokerto concentration of students in the universities of the use of the method c4.5 algorithm 3, k-neareset naighbors and naïve beyes. Academic student data used as the basis analysis to determine the dominance in the lecture concentration.Of the result of the research uses phon 60,24 % decision is, there are using k-neareset naighbors 75.36 % and use naïve beyes 100,00 % there are, the prediction could be the basis for deciding the lecture the concentration by mainstream student.The result is expected to help the university in recommended it to students study concentration related to the election.

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