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首页> 外文会议>International Conference on Quality in Research >Majority vote technique based on multi rough set for multi attributes decision-making system: Case study classifying job competency for civil servants' functional works in ministry of religious affairs of Republic of Indonesia
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Majority vote technique based on multi rough set for multi attributes decision-making system: Case study classifying job competency for civil servants' functional works in ministry of religious affairs of Republic of Indonesia

机译：基于多粗糙集的多属性决策制度的多数投票技术：案例研究课程课程课程职业职能作品在印度尼西亚宗教事务部

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摘要

In the government agencies, civil servants are required to have competence or ability to finish the work effectively and efficiently. In fact, the decision-making system for determining position and assignment of civil servants' functional works is still performed manually, so it takes a longer time. Moreover, the results are not totally accurate in terms of their competency. Rough set, hereinafter called Single Rough Set, is a common method to solve this problem, but the process may be very complex and still has the unclassified result. In this research, Multi Rough Set and Majority Vote technique are proposed to enhance system performance of single rough set with multi attributes of job competency. It obtains accuracy rate with 5-fold cross-validation that is 83.67% better than a Single Rough Set and it has 0.947 Area Under Curve (AUC) derived from Receiver Operator Characteristic (ROC). Thus, it can be said that the system performance of Multi Rough Set can be considered excellent in classifying job competency for civil servants' functional works.
机译：在政府机构中，公务员必须有能力或能够有效和有效地完成工作。事实上，用于确定公务员功能作品的职位和分配的决策系统仍然是手动进行的，因此需要更长时间。此外，结果在其能力方面并不完全准确。粗糙集，在下文中称为单粗糙集，是解决这个问题的常用方法，但过程可能非常复杂，仍然具有未分类的结果。在这项研究中，提出了多粗糙集和多数投票技术，以提高单一粗略集合的系统性能。它获得了5倍交叉验证的精度速率，比单个粗糙集优于83.67 ％，它在源自接收器操作员特征（ROC）下的曲线（AUC）下有0.947个区域。因此，可以说，多粗糙集的系统性能可以被认为是公务员功能作品的课程能力的课程能力。

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