Using Bayesian Network for Determining The Recipient of Zakat in BAZNAS Pekanbaru

Akbarizan, R. Kurniawan, M. Nazri, S. Abdullah, Sri Murhayati, Nurcahaya
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引用次数: 4

Abstract

The National Amil-Zakat Agency (Baznas) in Pekanbaru has the function to collect and distribute zakat in Pekanbaru city. Baznas Pekanbaru should be able to determine Mustahik properly. Mustahik is a person eligible to receive zakat. The Baznas committee interviews and observes every Mustahik candidates to decide whom could be receive the zakat. Current Mustahik determination process could lead to be subjective assessment, due to large number of zakat recipient applicants and the complexity of rules in determining a Mustahik. Therefore, this study utilize artificial intelligence in determining Mustahik. The Bayesian Network method is appropriate to apply as an inference engine. Based on the experimental results, we found that Bayesian network produces a good accuracy 93.24% and effective to use in data set have an uneven class distribution. In addition, based on experiments by setting an alpha estimator’s values, at 0.6 to 1.0 can increase the accuracy of a Bayesian Network to 95.95%.
用贝叶斯网络确定北干巴鲁地区天课受赠人
位于北干巴鲁的国家天课机构(Baznas)负责在北干巴鲁市收集和分发天课。Baznas Pekanbaru应该能够正确地判断Mustahik。穆斯塔希克是一个有资格接受天课的人。巴兹纳斯委员会会见并观察每一个穆斯塔希克候选人,以决定谁可以接受天课。目前确定穆斯塔希克的程序可能导致主观评价,因为天课接受者申请人数量众多,确定穆斯塔希克的规则也很复杂。因此,本研究利用人工智能来确定Mustahik。贝叶斯网络方法适合作为推理引擎。基于实验结果,我们发现贝叶斯网络产生了良好的准确率93.24%,并且有效地用于类分布不均匀的数据集。另外,根据实验,通过设置alpha估计器的值,在0.6 ~ 1.0可以将贝叶斯网络的准确率提高到95.95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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