Klasifikasi Penyakit Kelamin Pada Wanita Dengan Menggunakan Kombinasi Metode K-Nearest Neighbor Dan Naive Bayes Classifier - Dimas Angga Nazaruddin; Fitra Abdurrachman Bachtiar; Ratih Kartika Dewi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Venereal or Sexually Transmitted Disease (STD) are still a public health problem in developed and developing countries. Expert stated that health problems caused by venereal disease are higher in women. symptoms experienced have similarities between one and other venereal disease. Lack of knowledge possessed by patients can cause more severe. Therefore, to reduce the level of problems in self-examination, research is needed to classifying female veneral disease to find out the types of infectious diseases. Various methods can be used in classification. including using K-Nearest Neighbor (KNN) and Naive Bayes Classifier. The combination of these two methods has advantages that include no need to discretize more on continuous variables. So that in this study the KNN and Naive Bayes Classifier method will be combined to classify venereal diseases, especially for women because both of these methods have a high degree of accuracy in studying a disease so it is expected to predict probabilities based on testing data. In this study the accuracy test of the combination of the K-Nearest Neighbor and Naive Bayes Classifier methods was 97.5% using an average accuracy and 99.17% using the confusion matrix for the nearest number of neighbors as K = 5.