Open AccessKomparasi Algoritma Naive bayes dan SVM Untuk Memprediksi Keberhasilan Imunoterapi Pada Penyakit Kutil
Author(s) -
Adi Supriyatna,
Wida Prima Mustika
Publication year - 2018
Publication title -
j-sakti (jurnal sains komputer dan informatika)
Language(s) - English
Resource type - Journals
eISSN - 2549-7200
pISSN - 2548-9771
DOI - 10.30645/j-sakti.v2i2.78
Subject(s) - naive bayes classifier , support vector machine , artificial intelligence , computer science , machine learning , boosting (machine learning) , bayes' theorem , pattern recognition (psychology) , bayesian probability
Warts is a skin health problem that is generally characterized by the appearance of small, rough-textured lumps on the skin surface caused by a virus that is human papilloma virus (HPV). One technique of treatment of wart disease is immunotherapy, this method is a treatment by boosting the immune system to overcome the disease of warts. Naive bayes and Support Vector Machine (SVM) is a method of data mining algorithm used to classify. The aim of this study was to compare the Naive bayes algorithm with Support Vector Machine (SVM) in predicting the success of immunotherapy treatment method in the treatment of wart disease. Tests conducted using the method of Naive bayes and Support Vector Machine (SVM) using the R programming language, then the results are used to do the comparison. The results of this study revealed that the Naive bayes method has superior prediction capability compared to Support Vector Machine (SVM) because Naive bayes can predict all class instances correctly with the accuracy level of 1.