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Comparison of C4.5 method based optimization algorithm to determine eligibility of beneficiaries of direct community assistance (Case study: Kelurahan Cicurug)

机译:基于C4.5方法的优化算法确定直接社区援助的受益人资格的比较(案例研究:Kelurahan Cicurug)

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The Provisional Direct Assistance to the Community (BLSM) is a program of providing cash assistance to Target Household (RTS), which is a Poor Household (RTM), which is stipulated by the government in addition to the fuel price hike. This study is based on the case that the distribution of BLSM is not the right target and subjective interests. This BLSM is for the poor who can not afford economically, but still many rich people who also receive it specially in Kelurahan Cicurug. Decision Support System (DSS) to determine the community direct assistance beneficiaries in Kelurahan Cicurug with C4.5 method is one of the above theme solutions. Data mining method is chosen because it can generate models and criteria that easily interpreted by the classification of Data Training and Data Testing with Genetic Algorithm (GA) algorithm so that it can be a better form of the method. Damp concluded with method C4.5 with accreditation value 92,92% from training data and 84,21% from data testing, Method C4.5 based on PSO have value 97,35% from training data and 98,68% from data testing, and GA-based C4.5 method has accreditation data of 94,69% from training data and 90,67% from data testing. Can be concluded C4.5 based on PSO is very good.
机译:临时向社区提供直接援助(BLSM)是一项向目标家庭(RTS)提供现金援助的计划,该目标家庭是除燃油价格上涨外,政府还规定的贫困家庭(RTM)。这项研究是基于BLSM的分布不是正确的目标和主观利益的情况。该BLSM适用于无法承受经济负担的穷人,但也有许多富有的人,他们也特别在Kelurahan Cicurug领取了它。上述主题解决方案之一是使用C4.5方法确定Kelurahan Cicurug社区直接援助受益人的决策支持系统(DSS)。选择数据挖掘方法是因为它可以生成模型和准则,而这些模型和准则很容易通过遗传算法(GA)进行数据训练和数据测试的分类来解释,因此它可能是该方法的更好形式。 Damp以方法C4.5得出结论,其训练数据的认可度值为92.92%,数据测试的认可度为84.21%,基于PSO的方法C4.5的训练数据为97.35%,数据测试为98.68% ,并且基于GA的C4.5方法从培训数据中获得了94.69%的认证数据,从数据测试中获得了90.67%的认证数据。可以得出结论,基于PSO的C4.5很好。

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