Classification of Zakat Fitrah Recipients Using Naïve Bayes Method

R. Adityo
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Abstract

Indonesia is a country with a majority Muslim population. In the daily life of the Indonesian population, it is inseparable from the influences of Islamic teachings. In life in this world there are many commands of Allah that must be carried out, including the order to pay zakat. One of them is when Eid al-Fitr is required to pay zakat fitrah for each of its citizens. In grouping the distribution of zakat fitrah using the Naïve Bayes classification method. Naïve Bayes classification itself is a classification method that can be applied in classification. The classification system used to classify categories of zakat fitrah recipients. From each test results using test data and training data randomly, and each test using training data which increased 37 pieces of data in each test. It can be concluded that the more training data the level of accuracy decreases. The determination of the amount of training data and test data is very influential on the final results of calculations using the Naïve Bayes method. Class determination also affects the final results of calculations using this Naïve Bayes method.
利用Naïve贝叶斯方法对天课受助人进行分类
印度尼西亚是一个穆斯林人口占多数的国家。在印尼人的日常生活中,它与伊斯兰教义的影响是分不开的。在这个世界的生活中,有许多安拉的命令必须执行,包括支付天课的命令。其中之一是要求开斋节为每个公民支付天课。采用Naïve贝叶斯分类方法对天课的教规分布进行分组。Naïve贝叶斯分类本身就是一种可以应用于分类的分类方法。分类系统用于划分天课受助者的类别。每次测试结果随机使用测试数据和训练数据,每次测试使用训练数据,每次测试增加37条数据。结果表明,训练数据越多,准确率越低。训练数据量和测试数据量的确定对使用Naïve贝叶斯方法计算的最终结果影响很大。类的确定也会影响使用Naïve贝叶斯方法计算的最终结果。
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