Analisa Prediksi Kelulusan Mahasiswa Menggunakan Metode Machine Learning: Penelitian

Abdul Khalik Walya; Hasbi Rizki Sulistyo; Ibnu Agustian Pratama; Sifatul Akmal; Imam Budiawan; Desmulyati Desmulyati

doi:10.31004/jerkin.v4i3.4959

Authors

Abdul Khalik Walya Universitas Bina Sarana Informatika
Hasbi Rizki Sulistyo Universitas Bina Sarana Informatika
Ibnu Agustian Pratama Universitas Bina Sarana Informatika
Sifatul Akmal Universitas Bina Sarana Informatika
Imam Budiawan Universitas Bina Sarana Informatika
Desmulyati Desmulyati Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.31004/jerkin.v4i3.4959

Keywords:

Machine Learning, Graduation Prediction, Classification, Logistic Regression, Random Forest, KNN

Abstract

Student graduation prediction is an important issue in higher education as it is closely related to the evaluation of academic success. Various machine learning algorithms have been applied to predict student graduation based on academic data. This study conducts a comparative analysis of three classification algorithms, namely Logistic Regression, Random Forest, and K-Nearest Neighbor, using a simulated dataset consisting of 200 student records with attributes including age, department, GPA, and graduation year. The research stages include data preprocessing, data splitting, model training, and performance evaluation using classification metrics. Experimental results indicate that Logistic Regression and Random Forest achieve the best performance with an accuracy of 100%, while the K-Nearest Neighbor algorithm attains an accuracy of 80%. These findings highlight that data characteristics and algorithm selection significantly affect the accuracy of student graduation prediction.

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