ANALISIS PERBANDINGAN METODE FEATURE SELECTION BACKWARD METHOD DAN STEPWISE METHOD
Keywords:
Perbandingan, Feature Selection, Backward Method, Stepwise MethodAbstract
Feature selection is an important process in the development of machine learning models to identify the most informative and relevant features in a dataset. Two commonly used methods for feature selection are the forward method and the backward method. In this research, a Data Mining feature selection technique is applied to compare the two Feature Selection methods, namely the Backward Method and the Stepwise Method, based on accuracy values. The results obtained from the comparison of accuracy values of Feature Selection, namely Backward Method and Stepwise Method, using the Students Performance dataset, show that both models are comparable. They are considered comparable because, based on their accuracy values, both the Backward Method and Stepwise Method have the same accuracy of 0.61 or 61%.
Keywords: Comparison, Feature Selection, Backward Method, Stepwise Method.
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