@inproceedings{4ef3439b2efc42a1b9b2781e2a3c3949,
title = "Performance analysis of one-dimensional Na{\"i}ve bayes as a data imputation method for car insurance problems",
abstract = "Machine learning methods are very widely used in helping human work. Not all data is as we expected. Some data have missing values. Data that has a missing value must be handled first at the pre-processing stage, one of which is by the imputation of the missing value. This study is comparing the imputation method of missing value uses mode and One-Dimensional Na{\"i}ve Bayes Classifier (1DNBC) to determine the performance analysis by using Support Vector Machine (SVM) for the prediction of car insurance participation. A better method is seen from the accuracy. Based on the simulation is obtained the same results for imputation using mode and One-Dimensional Na{\"i}ve Bayes are 1.00, which when examined further turns out to be the imputation of each missing value with the mode and prediction of imputation with One-Dimensional Na{\"i}ve Bayes are the same.",
author = "Yuwanti, {Natalia Aji} and Hendri Murfi",
note = "Publisher Copyright: {\textcopyright} 2020 American Institute of Physics Inc.. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 International Conference on Science and Applied Science, ICSAS 2020 ; Conference date: 07-07-2020",
year = "2020",
month = nov,
day = "16",
doi = "10.1063/5.0030542",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Budi Purnama and Nugraha, {Dewanta Arya} and Fuad Anwar",
booktitle = "International Conference on Science and Applied Science, ICSAS 2020",
}