基于多变量自适应回归样条的Covid-19疫苗感知分类

Rizki Fitri Ananda, L. Harsyiah, Muhammad Rijal Alfian
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引用次数: 0

摘要

印度尼西亚是感染covid-19病毒的国家之一。政府的努力之一是covid-19疫苗接种。然而,由于许多人拒绝接种疫苗,covid-19疫苗接种引起了一些人的争议。根据印度尼西亚卫生部关于covid-19疫苗接受度的调查,公众对covid-19疫苗的看法可分为积极和消极两种,这可能受到许多因素的影响。了解这些因素对于提高对covid-19的接受度很重要。多元自适应样条回归(MARS)。本研究的目的是确定公众对covid-19疫苗认知的分类模型及其影响因素。本研究使用的方法是多元自适应样条回归(MARS)。结果表明,当BF= 24, MI =3, MO= 1, GCV=0.07340546时,可以得到最优的火星模型。分类水平为91.5%,影响因素有性别(x_1)、年龄(x_2)、末受教育程度(x_4)、接种意愿(x_6)、受教育程度(x_8)。根据得到的结果,政府可以考虑这些因素进行社会化
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification Of Perceptions Of The Covid-19 Vaccine Using Multivariate Adaptive Regression Spline
Indonesia is one of the countries infected with the covid-19 virus. One of the government's efforts is the covid-19 vaccination. However, the covid-19 vaccination caused controversy for some people because many people refused to be vaccinated.  Public perception of the covid-19 vaccine can be categorized into two, namely positive and negative, based on survey from Indonesia ministry of health about acceptance of covid-19 vaccine state that this can be influenced by many factors. These factors are important to know as an effort to increase acceptance of covid-19. Multivariate Adaptive Regression Splines (MARS). The purpose of this study is to determine the classification model of public perception of the covid-19 vaccine and the factors that influence it. The method used in this study is Multivariate Adaptive Regression Splines (MARS). This method is appropriate classification method to be applied to categorical response variable data,  The outcomes demonstrate that the optimum mars model is produced by combining BF= 24, MI =3, MO= 1, and GCV=0.07340546. The resulting classification level is 91.5% with influencing factors yaitu gender (x_1), age (x_2), last education (x_4), willingness to vaccinate (x_6), education (x_8).  Based on the results obtained, the government can consider these factors for socialization
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