Perbandingan Algoritma naive bayes dan C4.5 pada klasifikasi penyakit tuberculosis

Abstract

Mycobacterium Tuberculosis bacteria cause tuberculosis. This disease is usually spread through splashes such as coughing or sneezing. Pulmonary Tuberculosis and Extra Pulmonary Tuberculosis are two classifications of tuberculosis. Indonesia is the third country with the highest number of tuberculosis cases. Therefore, efforts need to be made to reduce the number of tuberculosis cases in Indonesia. One effort that can be made is to classify tuberculosis data in medical settings. This can help doctors in diagnosing the disease and early detection of tuberculosis, allowing faster healing for those suffering from this disease. One type of classification algorithm is Naive Bayes and C4.5. The first is used to predict future opportunities based on previous experience by looking for the largest opportunity from several possible classifications and looking at the frequency of each classification in the training data. C4.5 was chosen because it is a benchmark that is often used in comparison with the Naive Bayes algorithm. The results obtained by the C4.5 algorithm have a higher level of accuracy with a percentage of 85% than the naïve bayes algorithm which has an accuracy of 83%, which means the C4.5 algoritm is better when applied to the tuberculosis disease classification process