Peptida Anti Kanker Membranolitik (ACPS) Pada Kanker Paru – Paru Menggunakan Metode Naïve Bayes Dan K-Nearest Neighbor

Abstract

Lung cancer is one of the most common types of disease. In this study the methods used are Naive Bayes (NB) and K-Nearest Neighbor (K-NN), which include algorithms that work well for classifying datasets. The purpose of this research is to compare the classification algorithms of Naive Bayes and K-NN, both of which are quite effective for analyzing cancer data. Rapid Miner is used together with Naive Bayes and K-NN algorithms for decision making. After the research was carried out, the results of the study using the Naive Bayes method found that lung cancer had an inactive-virtual class of 750 with a precision of 100%, an inactive-exp of 52, a precision class of 56.45%, a mod-active 75 class of 57.69% precision, and a precision of class 37 which is very active. 17% with an accuracy of all 86.56% which is included in the good classification category. While the K-nearest neighbor algorithm method, it turns out that ACPs lung cancer which has inactive-virtual totals 750 class precision 100% inactive-exp 52, class precision 56.45%, mod-active 75 class precision 57.69%, very-active 37 class precision 17% with all 93.01% accuracy, which is included in the excellent classification category. It can be seen from the two methods that we analyze, the K-nearest neighbor algorithm method, displays a classification that has a greater percentage than the Naive Bayes method, in other words the use of the K-NN method on ACPs lung cancer data is more accurate than the Naïve Bayes method.