Implementation Of Fuzzy-C4.5 Classification As a Decision Support For Students Choice Of Major Specialization

TL;DR

This paper presents a hybrid fuzzy-C4.5 model to assist students in choosing their major specialization based on academic data, combining rule-based decision trees with fuzzy logic for improved decision support.

Contribution

It develops a novel hybrid model integrating fuzzy Mamdani and C4.5 algorithms for student major selection analysis in university settings.

Findings

17 out of 126 students' choices aligned with the model's recommendations

The hybrid model effectively combines rule-based and fuzzy logic approaches

Demonstrates potential for decision support in academic major selection

Abstract

Determination of major specialization is important to lead the student to focus on areas of study that are of interest as well as in accordance with its academic credentials. Currently, the determination of major specialization is done by asking directly to students, regardless of academic outcomes that have been achieved in the previo us semester. This study discusses the development of a hybrid model from fuzzy Mamdani and C4.5 algorithm to analyze the determination of major specialization in informatics engineering courses of Universities Raya Serang, where C4.5 algorithm is used as a shaper rule (rule) which is used in the inference stage. Establishment of rules (decision tree) performed using Weka applications, while for the determination of the decision support analysis specialization majors using Mamdani fuzzy concept, the application is done using the help of MATLAB. The results showed that 17 of the 126 students who either choose according to variable concentrations used in this study.