@thesis{thesis,
		author={Nashrulloh  Khoiruzzaman},
		title ={Analisis Komparasi Hasil Klasifikasi Algoritma Backpropagation Dan K-Nearest Neighbor Pada Dataset Cardiovascular Disease},
		year={2020},
		url={https://repository.ittelkom-pwt.ac.id/6044/},
		abstract={Cardiovascular disease is a disease caused by abnormalities that occur in the heart organ. Cardivascular disease can affect humans from young to old age, there are 13 factors that influence it, namely Age, Sex, Chest Pain, Trestbps, Chol, Fbs, Restecg, Thalach, Exang, Oldpeak, Slope, Ca, and Thal. Cardiovascular disease various types, including oronary heart disease, heart failure, high blood pressure, low blood pressure and others. Therefore, this study aims to classify cardiovascular disease. In this study using the backpropagation algorithm and the K-nearest neighbor algorithm. First step to do is the euclidean distance calculation process in K-NN to find the closest k distance to get the category based on the most frequent frequencies of the specified k value and look for new weights for the backpropagation algorithm to get new weights used to get 
values that are as expected. This system testing consists of testing the accuracy value with the K value, the K-fold X validation test and the hidden layer effect. The results of this study that the backpropagation algorithm produces an accuracy value of 64%, a precision of 62%, a recall of 64% 
and a K-nearest neighbor algorithm produce an accuracy value of 66%, a precision of 61% and a recall of 66%.
Keywords: algorithm, backpropagation, k-nearest neighbor, cardiovascular disease, 
confusion matrix, neural network.}
		}