利用朴素贝叶斯分类器研究基于毕业生示踪的毕业生档案分类

Junus Sinuraya, Friendly
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引用次数: 0

摘要

毕业生简介是学习计划或专业领域/工作领域的毕业生在完成学习计划的教育后所扮演的角色。学习计划毕业生概况的确定通常是根据利益相关者需求评估的结果进行的。根据研究、技术和高等教育部的数据,IT研究项目是印度尼西亚大学中最多的专业或研究项目之一,每年选择这个研究项目的爱好者人数最多。每年,信息技术专业的毕业生都有大量的毕业生,无论是职业院校还是非职业院校。信息技术毕业生人数众多,但他们的毕业生能力较低,甚至他们不具备信息技术领域的能力,因此他们的工作与所设计的毕业生形象不符。因此,有必要利用Naïve贝叶斯分类器方法对基于示踪研究数据的毕业生工作履历进行分类研究。本研究使用属性,即学习计划,价值标准,性别和工作领域,使用的标签是状态(线性和非线性)。利用Naïve贝叶斯分类器方法对毕业生个人资料进行分类的研究结果表明,校友不按毕业生个人资料工作的比例为73%,按毕业生个人资料工作的比例为23%,数据准确率为87%,属于良好分类类别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Graduate Profiles Based on Graduate Tracer Study Using Algorithm Naive Bayes Classifier
The graduate profile is the role of the graduate of the study program or field of expertise / field of work planned after completing education from the study program. The determination of the profile of study program graduates is generally carried out based on the results of the assessment of stakeholder needs. Based on data from the Ministry of Research, Technology and Higher Education, the IT study program is one of the most majors or study programs in Indonesian universities and the highest number of enthusiasts choose this study program each year. Each year, graduates of IT study programs have a large number of graduates, both vocational and non- vocational colleges. The number of IT graduates is large but they have low graduate competencies, even they do not have competencies in the IT field so that their work is not in accordance with the graduate profile that has been designed. Therefore, it is necessary to conduct research to classify the profile of graduates who have worked based on tracer study data using the Naïve Bayes Classifier method. This study uses attributes, namely study program, value criteria, gender and field of work and the labels used are status (Linear and Non-Linear). The results of the study on the classification of the profile of graduates using the Naïve Bayes Classfier method show that alumni work not according to the profile of graduates by 73% and according to the profile of graduates by 23%, with a data accuracy rate of 87% and are included in the good classification category.
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