Analisis Komparatif Linear Regression dan Decision Tree untuk Prediksi Skor QS World University Rankings 2025
DOI:
https://doi.org/10.62712/juktisi.v5i1.1094Keywords:
Decision Tree, Machine Learning, QS Rankings, Regresi Linear, Supervised LearningAbstract
Sistem perangkingan universitas dunia telah menjadi tolak ukur global yang krusial dalam mengukur kualitas institusi pendidikan tinggi, produktivitas riset, dan keunggulan akademik. Dataset QS World University Rankings 2025 menyediakan seperangkat indikator evaluasi yang komprehensif, mencakup reputasi akademik, reputasi pemberi kerja, rasio dosen-mahasiswa, sitasi per fakultas, serta berbagai indikator internasionalisasi. Penelitian ini melakukan studi komparatif regresi machine learning untuk memprediksi Overall Score universitas berdasarkan indikator-indikator tersebut. Dua model supervised learning diterapkan, yaitu Regresi Linear dan Decision Tree Regressor. Dataset yang terdiri dari 1.503 entri dan 28 kolom diproses melalui tahapan preprocessing menyeluruh, meliputi penanganan nilai hilang dengan imputasi median, deteksi outlier menggunakan metode IQR, pengkodean variabel kategorikal dengan LabelEncoder, dan normalisasi fitur menggunakan StandardScaler. Data dibagi dengan rasio 80:20 untuk pelatihan dan pengujian. Metrik evaluasi yang digunakan mencakup Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa Regresi Linear secara signifikan mengungguli Decision Tree, dengan capaian R² sebesar 0,9985, MAE sebesar 0,3662, dan RMSE sebesar 0,7427. Validasi silang 5-fold mengonfirmasi stabilitas model Regresi Linear dengan R² rata-rata 0,9374 ± 0,0668. Analisis feature importance mengidentifikasi Academic Reputation Score sebagai prediktor paling berpengaruh terhadap Overall Score, konsisten dengan temuan analisis korelasi (r = 0,90).
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