KOMPARASI NAÏVE BAYES, SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR UNTUK MENGETAHUI AKURASI TERTINGGI PADA PREDIKSI KELANCARAN PEMBAYARAN TV KABEL

Abstract

One obstacle of the default payment is the lack of analysis in the new customer acceptance process which is only reviewed from the form provided at registration, as for the purpose of this study to find out the highest accuracy results from the comparison of Naïve Bayes, SVM and K-NN Algorithms. It can be seen that the Naïve Bayes algorithm which has the highest accuracy value is 96%, while the K-Neural Network algorithm has the highest accuracy at K = 3 which is 92%, while Support Vector Machine only gets accuracy of 66%. The ROC Curve results show that Naïve Bayes achieved the best AUC value of 0.99. Comparison between data mining classification algorithms namely Naïve Bayes, K-Neural Network and Support Vector Machine for predicting smooth payment using multivariate data types, Naïve Bayes method is an accurate algorithm and this method is also very dominant towards other methods. Based on Accuracy, AUC and T-tests this method falls into the best classification category.