基于Naïve贝叶斯分类方法的数据挖掘在2024年美国大选公众利益参与预测中的应用

Marcelina Novi Zarti, Eka Sahputra, Anisya Sonita, Yovi Apridiansyah
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引用次数: 0

摘要

使用数据挖掘技术的大数据处理过程,将在数据挖掘应用的研究中使用Naïve贝叶斯分类来预测公共利益在2024年选举中的参与。这些数据是从选举委员会获得的。使用Naïve贝叶斯分类方法与Weka工具和7个预定属性对数据进行测试。该数据集对11,406个训练数据(即2014年大选数据)和11,908个测试数据(即2020年大选数据)的采集率分别高达96.67%和99.90%。根据2020年的参与者数据和2014年的选举结果,中央明古鲁县2024年选举的参与者人数可能会增加3.23%,从每11,406名参与者的96.67%增加到每11,908名参与者的99.90%,结果预测可能会增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application Of Data Mining Using The Naïve Bayes Classification Method To Predict Public Interest Participation In The 2024 Elections
A big data processing process using a Data Mining technique that will be used in a study in the Application of Data Mining Using the Naïve Bayes classification to predict the participation of the Public Interest in the 2024 election. The data was obtained from the General Election Commission (KPU). The data was tested using the Naïve Bayes classification method with Weka Tools and 7 predetermined attributes. The dataset was taken as much as 96.67% of 11,406 training data, namely 2014 election data and 99.90% of 11,908 testing data, namely 2020 election data. Results It is known that the number of participants in Central Bengkulu Regency for the 2024 election based on participant data in 2020 and the 2014 election results is likely to increase by up to 3.23%, from 96.67% per 11,406 participants to 99.90% per 11,908 participants and the results predictions are likely to increase.
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