PERBANDINGAN PERFORMA BAGGING DAN ADABOOST UNTUK KLASIFIKASI DATA MULTI-CLASS

Samuel Lukas, Osvaldo Vigo, Dion Krisnadi, Petrus Widjaja

Abstract


Salah satu teknik untuk meningkatkan performa algoritma Machine Learning adalah menggunakan Ensemble Learning. Ide teknik ini menggabungkan beberapa algoritma Machine Learning atau yang biasa disebut sebagai base learners. Tujuan penlitian ini adalah membandingkan dua performa algoritma Ensemble Learning yaitu metode Bootstrap Aggregating (Bagging) dan metode Adaptive Boosting (AdaBoost). Penelitian menggunakan sebelas dataset dengan klasifikasi multi-class yang independen terhadap karakteristik (proporsi data, jumlah data, dan masalah) serta jumlah kelas variabel target berbeda. Hasil penelitian menunjukkan bahwa akurasi dan F1 model yang dibentuk oleh metode Bagging cenderung menunjukkan performa nilai yang lebih baik dari metode AdaBoost pada metrik evaluasi dengan rata-rata nilai evaluasi sebesar 72,21% dan 61% untuk Bagging serta 66,25% dan 53,7% untuk AdaBoost. Namun hasil pengujian hipotesis memperlihatkan tidak cukup signifikan. Selain itu lama lama waktu komputasi untuk membentuk model Bagging dan model AdaBoost tidaklah berbeda.

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