Model Prediksi Kelulusan Mahasiswa Berbasis Machine Learning dengan Seleksi Fitur

Rizal Fauzi Bakri; Vina Dewi Uswatun

Authors

Rizal Fauzi Bakri Medan Area University Author
Vina Dewi Uswatun Medan Area University Author

Keywords:

Prediksi Kelulusan, Machine Learning, Seleksi Fitur, Information Gain, Decision Tree

Abstract

Kelulusan tepat waktu mahasiswa merupakan salah satu indikator penting dalam mengevaluasi kualitas penyelenggaraan pendidikan tinggi serta mendukung pengambilan keputusan akademik berbasis data. Penelitian ini bertujuan mengembangkan model prediksi kelulusan tepat waktu mahasiswa menggunakan algoritma Decision Tree, Random Forest, dan Naïve Bayes, serta menganalisis pengaruh seleksi fitur berbasis Information Gain terhadap performa model. Penelitian menggunakan pendekatan eksperimen komputasional dengan dataset simulasi yang terdiri atas 850 rekaman mahasiswa dan sembilan atribut akademik serta demografis. Tahapan penelitian meliputi preprocessing data, seleksi fitur menggunakan Information Gain, pemodelan machine learning, dan evaluasi menggunakan 10-fold cross-validation dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Random Forest memberikan performa terbaik dengan akurasi sebesar 89,4% sebelum seleksi fitur dan meningkat menjadi 91,2% setelah penerapan Information Gain. Seleksi fitur berhasil mereduksi atribut prediktor dari sembilan menjadi lima fitur utama, yaitu IPK, nilai rata-rata semester, jumlah SKS ditempuh, kehadiran, dan status beasiswa, sehingga meningkatkan efisiensi komputasi tanpa mengurangi kualitas prediksi. Temuan ini menunjukkan bahwa kombinasi Random Forest dan Information Gain berpotensi diterapkan sebagai bagian dari early warning system pada Sistem Informasi Akademik untuk mengidentifikasi mahasiswa yang berisiko mengalami keterlambatan kelulusan secara lebih dini dan akurat.

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