Algoritma K-Nearest Neighbors dan Synthetic Minority Oversampling Technique dalam Prediksi Pemesanan Tiket Pesawat

Abstract

This study applies the Synthetic Minority Oversampling Technique to improve the performance of the K-Nearest Neighbors method in predicting the unbalanced data class. Most classification algorithms implicitly assume that the processed data has a balanced distribution, so that the standard classifier is more inclined towards data with a dominant class number (majority class). The use of Synthetic Minority Oversampling Technique can improve the performance of the K-Nearest Neighbors method for flight ticket booking data. Although in terms of accuracy, Synthetic Minority Oversampling Technique with K-Nearest Neighbors is lower at 79.65% compared to K-Nearest Neighbors without using Synthetic Minority Oversampling Technique, which is 97.81%, the suggested technique did not improve but from other performance, The proposed method can outperform K-Nearest Neighbors by using Synthetic Minority Oversampling Technique in terms of precision, recall, and F1-Score when applied to the Airline Ticket Booking dataset. Precision increased 18.00% from 62.00% to 80.00%, recall increased 28.00% from 52.00% to 80.00%, and F1-Score increased 27.00% from 53.00% to 80 ,00% on the flight ticket booking dataset.