Optimasi Backward Elimination untuk Klasifikasi Kepuasan Pelanggan Menggunakan Algoritme k-nearest neighbor (k-NN) and Naive Bayes - Yunitasari; Hopipah, Hopi Siti; Mayasari, Rini (Technomedia Journal, 2021)
Maintaining customer satisfaction is a big challenge for companies. One effort that can be done is to provide the best service to customers based on the most influential aspects. In this study, the optimization of the Backward Elimination feature in the classification of customer satisfaction using the k-NN and Naïve Bayes algorithm. The use of the Backward Elimination feature aims to increase accuracy and reduce the number of less influential attributes. As a result, it can be seen that the best modeling without Backward Elimination is the Naïve Bayes algorithm with an accuracy of 99.04% and an AUC value of 1. While the application of Backward Elimination works more optimally on the k-NN algorithm with an increase of 33.74% to 97.28% with AUC 0.996. This shows that the performance of the Backward Elimination feature is effective in optimizing the classification of customer satisfaction and can reduce the less influential attributes.