Penerapan Metode C4.5 dan K-Nearest Neighbor untuk Klasifikasi Kelulusan Mahasiswa Berdasarkan Data Akademik

Dina Amalia Putri; Naza Sefti Prianita; Elkin Rilvani

doi:10.61132/jupiter.v3i4.1032

Authors

Dina Amalia Putri Universitas Pelita Bangsa
Naza Sefti Prianita Universitas Pelita Bangsa
Elkin Rilvani Universitas Pelita Bangsa

DOI:

https://doi.org/10.61132/jupiter.v3i4.1032

Keywords:

C4.5, Data Mining, Graduation Prediction, K-Nearest Neighbor, Student Graduation

Abstract

The issue of determining the number of students' graduation times is one of the important indicators in transmitting the quality and effectiveness of the higher education process in universities. The rate of on-time graduation not only impacts accredited institutions, but also becomes a concern for campus management in designing learning strategies and academic guidance. This study aims to apply and compare two classification algorithms in data mining, namely C4.5 and K-Nearest Neighbor KNN, in predicting the accuracy of students' graduation times. Predictions are made based on academic attributes such as Grade Point Average GPA, number of credits that have been achieved, and Semester Grade Point Average IPS as input variables. The method used in this study is Knowledge Discovery in Database KDD which includes data selection, preprocessing, transformation, data mining, and evaluation of results. The study was conducted using the RapidMiner tool, with a dataset of 279 Informatics Study Program students from the 2015 to 2019 intake. The data was classified into two categories: "graduated on time" and "not graduated on time". The test results showed that the KNN algorithm provided better performance compared to C4.5. KNN produced an accuracy of 76.08%, with a precision of 73.11% and a recall of 41.92%. Meanwhile, the C4.5 algorithm produced an accuracy of 73.49%, with a precision of 64.62% and a recall of 41.89%. This difference in accuracy indicates that KNN is more effective in capturing patterns in the data and providing more accurate predictions in this context. Thus, the KNN algorithm can be considered a more optimal method to assist universities in predicting potential student admissions in a timely manner, thus enabling early intervention for students at risk of late graduation. This research also contributes to the development of data mining-based academic decision support systems in higher education.

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