基于优化算法的电力补贴比较数据挖掘

D. Gustian, Falentino Sembiring, R. Amelia, Eneng Nurhasanah, Siti Waelah, Nia Anggraeni
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摘要

电力补贴是印尼政府通过PT. PLN (Persero)向贫困社区提供的项目。希望这个项目可以减轻由于经济影响而产生的社会负担。在分配过程中出现了一个问题,没有达到目标,这肯定造成了政府和人民的财政损失,他们没有得到政府的援助。本研究采用基于优化算法的比较数据挖掘分类方法来获得最佳模型。一些解决办法正在出现,预计将有助于政府减少在交付过程中的财政损失,并为政府妥善处理分配目标提供好处。研究结果表明,采用遗传算法(GA)优化的C4.5方法比粒子群算法(PSO)和差分进化算法(DE)的准确率更高,达到92.28%。
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Comparison Data Mining based on Optimization Algorithms in Receiving Electricity Subsidies
Electricity subsidy is the Indonesian Government program through PT. PLN (Persero) to the underprivileged community. Hopefully this program can ease the burden of society due to the economic impacts that occur. There is a problem in the distribution process that has been happening is not on target, it certainly resulted in a financial loss that has been issued by the Government and people who do not feel government assistance. This research uses a comparison of Data Mining classification method based on optimization algorithm to get the best model. Solutions that are expected to assist the Government in reducing financial losses in the delivery process are occurring as well as providing benefits for the government to properly process the distribution of targets. Research provides results that the C4.5 method with the Genetic Algorithm (GA) optimization is a better level of accuracy than Particle Swarm Optimization (PSO) and Differential Evolutin (DE) with a value of 92.28% accuracy.
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