Prediksi Dropout Mahasiswa Menggunakan Metode Machine Learning: Tinjauan Komparatif Ensemble, Decision Tree, Dan Neural Network
Keywords:
Dropout Mahasiswa, Machine Learning, Ensemble Learning, Decision Tree, Neural NetworkAbstract
Dropout mahasiswa merupakan permasalahan serius yang berdampak terhadap kualitas pendidikan tinggi secara global. Penelitian ini menyajikan tinjauan komparatif terhadap berbagai metode machine learning yang telah digunakan untuk memprediksi risiko dropout mahasiswa, meliputi metode ensemble (AdaBoost, XGBoost, Random Forest, voting heterogen), decision tree (C4.5), dan Artificial Neural Network (ANN). Studi ini mengkaji beberapa penelitian terdahulu yang mencakup konteks pembelajaran blended, pendidikan teknik, hingga pendidikan dasar dan menengah. Hasil kajian menunjukkan bahwa metode ensemble lanjutan seperti AdaBoost dan XGBoost konsisten menghasilkan akurasi tertinggi (hingga 97%), diikuti ANN (77,3%), Random Forest (75,5%), dan Decision Tree (70,1%-86,8%). Fitur yang paling signifikan dalam prediksi dropout meliputi nilai akademik, frekuensi interaksi dengan sistem LMS, kesehatan mental, hubungan mahasiswa-dosen, dan faktor sosial-ekonomi. Penelitian ini menyimpulkan bahwa pemilihan metode perlu disesuaikan dengan karakteristik dataset, ketersediaan fitur, serta kebutuhan interpretabilitas model. Pendekatan explainable machine learning (XAI) sangat direkomendasikan agar prediksi dapat dimanfaatkan secara efektif oleh institusi pendidikan.
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Copyright (c) 2026 Yudistira Dwi Permana (Author)
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