基于C.45算法与粒子群优化方法的初中生备考分类

bit-Tech Pub Date : 2020-08-31 DOI:10.32877/BT.V2I3.133
Didi Kurnaedi, A. Suherman, Sofian Lusa, R. Darmawan
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引用次数: 1

摘要

这些研究与学生在全国考试中毕业率下降的趋势背道而驰。这是因为学生准备面对国家考试的方式是不准确的。本文研究了混合方法c4算法。5、群粒子优化,产生班级准备的学生具有高和准确的准确性。本研究表明,混合方法c4.5和粒子群优化的准确率为97.13%,精密度为96.6%,召回率为58%,召回率为100%。然后使用javascript语言编程,通过基于web的原型应用程序实现
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Junior Class Preparedness Classification Faces A National Exam Using A C.45 Algorithm With A Particle Swarm Optimization Approach
These studies are counter to a trend of falling students' graduation rates on the national exam. This is because of the way students prepare their readiness to face national tests is inaccurate. On this study the hybrid method c4 algorithm.5 and the swarm particle optimization to produce a class readiness of students with high and accurate accuracy. This research suggests that by using hybridmethodC4.5 andParticle Swarm Optimizationgenerates accuracy as 97.13 %, Precisionas 96,58 %, andRecallas 100 %. Then implemented through a web-based prototype application using programming javascriptlanguage
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