C4.5算法在楼层施工方案选择中的辅助实现

Aditya Roval Lendra, Diky Firdaus
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引用次数: 0

摘要

印度尼西亚是一个发展中国家。开发过程和业务的改进表明了这一点。因此,将会出现许多开发项目。随着行业电子政务治理的不断变化,项目数据不再是纸质的。随着数据的出现,许多公司需要在确定项目战略时进行项目管理活动,以免影响确定项目实施的最终结果。然后对建筑界出现的项目数据进行大量研究。本研究使用的方法是C4.5算法,它是数据挖掘过程的现代算法之一。C4.5算法也被称为决策树(decision tree),它是具有树结构表示的分类方法之一。这个概念是收集数据,并根据获得结果所需的规则将其制成决策树。所获得的模式和结果将用于确定公司将采取哪些项目的建议。使用Rapidminer生成的值的准确率为97.18%,准确率为100%,召回率为94.31%。结果,1205个建议楼层建筑项目被采纳,784个项目被建议不被采纳。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IMPLEMENTATION OF C4.5 ALGORITHM TO ASSIST IN THE SELECTION OF FLOOR CONSTRUCTION PROJECTS
The country of Indonesia is a developing country. This is indicated by the improvement of the development process and business. So, many development projects will appear. With the changing world in the industry with e-Government governance, project data is no longer in paper form. With the emergence of data - a lot of companies need to do project management activities in determining the project strategy to be taken so as not to affect the final results in determining the taking of the project. Then do a lot of research on the project data that appears in the construction world. The method used for this research is the C4.5 Algorithm which is one of the modern algorithms for data mining processes. C4.5 algorithm is also called a decision tree (decision tree) which is one of the classification methods with a tree structure representation. The concept is to collect data and be made into a decision tree based on the rules needed to get an outcome. The pattern and results obtained will be used for recommendations in determining which projects the company will take. The values generated using Rapidminer are Accuracy 97.18%, precision 100%, and recall 94.31%. With the result, 1205 recommended floor construction projects were taken and 784 projects were recommended not to be taken.
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