Kajian Algoritme Super Learner sebagai Metode Ensemble dalam Kasus Klasifikasi

Abstract

Classification is a statistical approach used when the response variable is categorical. The classification process generally consists of two phases: model training and model testing. The Super Learner is an ensemble method that integrates multiple candidate algorithms into a single predictive model by using V-fold cross-validation to determine the optimal weighted combination of base learners. Although numerous studies in the Department of Statistics at IPB University have applied various classification techniques and achieved satisfactory average accuracy, misclassification remains an issue that could potentially be reduced through model optimization. This study investigates whether the Super Learner ensemble can improve classification accuracy relative to single-model approaches previously applied. In addition, the study examines the characteristics of the resulting Super Learner models and evaluates the conditions under which performance gains are most pronounced.