过程类专业学生分类数据挖掘的比较

D. Gustian, A. Rahmawati, Titin, Rian Rama Putra, Putri Anisa
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引用次数: 5

摘要

新生的录取过程,特别是在职业学校,是学校根据所选专业选择未来学生的第一步。但有时专业的选择对学生来说是自讨苦吃,因为在部门收到后,他们在学习过程中失败,例如在获得课程时与所选专业不匹配,学生的学习兴趣降低,学术成就的价值降低,如果学生毕业,学生会发现很难找到适合自己兴趣的工作,甚至辍学。这当然是学校管理维持这些学生的一个主要问题,它影响了已经发布的教育成本,无论是家长还是补贴学校的政府都是无用的,因为学生没有足够的能力来选择专业。为了在数据训练、测试和验证过程中获得最佳的准确率值,本研究对几种数据挖掘分类方法进行了比较。本研究可以为学生选择专业时最佳的分类方法提供解决方案,可以为同类案例选择最佳的分类方法提供参考,使学生的专业选择更加准确。这样有助于学校可以根据每个学生的兴趣和才能做出适当的决定来确定学生的专业,从而最大限度地减少学生在确定专业时的错误,从而直接导致学生的学业成绩。结果表明,KNN和ID3方法在数据测试和验证的准确率均为100%,C4.5分别为98.61%和100%,Naïve Bayes分别为99.31%和96.45%,Random Forest分别为92.36%和89.92%。Software Quality Assurance的值为80。这意味着该应用程序用于协助专业管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Classification Data Mining in Process Majors Students
The process of admission of new students, especially in vocational school is the first step of school in selecting the prospective students in accordance with the chosen majors. But sometimes the selection of majors becomes boomerang for the students because after received in the department, they fail in the process of studying such as not match with the chosen majors when he has obtained the lessons, students learning interest is reduced so that the values of academic achievement decrease and if the student graduate, the student will find difficulty in finding a job that suits for their interests, even to the drop out. This is certainly a major problem for school management in maintaining these students and it impacts on the costs of education that have been issued, either by parents or by the government who subsidize the school to be useless, because the students do not have sufficient ability for the chosen majors. This study conducted a comparison of several methods of data mining classification in order to obtain the best accuracy value in the process of data training, testing and validation. This research can provide solution on the best classification method in the selection of majors, so it can be used as a reference in choosing the best method and suitable in similar cases, which will result in the students majors to be more accurate. So as to contribute to the school can make an appropriate decision to determine the students majors in accordance with the interests and talents of each student, thus minimizing students wrong in determining the majors that can result directly with the academic achievement of the student. The results obtained that KNN and ID3 method tops in accuracy of both data testing and validation with a value of 100%, C4.5 about 98.61% and 100%, Naïve Bayes 99.31% and 96.45% and the last Random Forest 92.36% and 89.92%. Software Quality Assurance obtained the value of 80. This means that the application is used to assist the management of majors.
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