%0 Thesis
%9 Skripsi
%A Rizka Febri Suryani, NIM. 16650009
%B FAKULTAS SAIN DAN TEKNOLOGI
%D 2020
%F digilib:39860
%I UIN SUNAN KALIJAGA YOGYAKARTA
%K labor, data mining, classificaion, Naïve Bayes, KNN, Confusion Matrix
%P 88
%T ANALISIS PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN NAÏVE BAYES DALAM MENENTUKAN KLASIFIKASI PERSALINAN  (STUDI KASUS: PKU MUHAMMADIYAH YOGYAKARTA)
%U https://digilib.uin-suka.ac.id/id/eprint/39860/
%X The development of technology can be used to facilitate many matters. One of them is in the medical.One of the fields in the medical is childbirth. Childbirth is the process of removing the fetus for months with help or without. There are several methods of labor that can be done. The determination of the labor is based on many factors. Determination of labor must be in accordance with the conditions of pregnant patient. Therefore it is necessary to have an automatic classification to determine the labor in data mining that has a good degree of accuracy.  Classification is done using data on pregnant patients at PKU Muhammadiyah Yogyakarta hospital. The data used are patient data from 2018-2019 to house 1255 data with 23 attributes of the patient’s condision to determine the labor then eliminated using the Extra Trees Classifier algorithm to 10 attributes. Classification is done using the K-Nearest Neighbour (KNN) anda Naïve Bayes methods which will then compare the value of precision, recall, and accuracy of each method to get the highest value.  Based on the research, it was cpncluded that Naïve Bayes method has higher accuracy, precision and recall being 90.6%, 88%, and 91% in the coparison of the test data 20% and training data 80% using prprocessing Linear Discriminant Analysis. Thus, the use of Naïve Bayes method to classify data on labor patient at PKU Muhammadiyah Yogyakarta hospital is better than the K-Nearest Neighbor (KNN) method.
%Z Muhammad Didik Rahmad Wahyudi,M.Kom