K-Means and K-NN Methods For Determining Student Interest

Guslendra Guslendra, Sarjon Defit, Ramesh Bastola
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引用次数: 1

Abstract

Putra Indonesia University 'YPTK' Padang's Department of Information Systems, Faculty of Computing Science has three specializations, namely Information Technology Management, Business Information Systems, and Industrial Information Systems. In the fifth semester, the acquisition of specializations takes place. In the next semester, the selection of specialist programs will be determined. The option of the degree is adapted to students' needs and capacities. The acquisition of results generated in the previous semester can be seen. The objective of this survey is to provide students with suggestions for the collection of degrees. The study was performed using K-Means and K-Nearest Neighbor methods to obtain the classification of students and the correlation between recent cases and past cases. This analysis uses 13 characteristics, of which 12 are predictors and 1 is the option. The test results can be used as a way to suggest the student preferences based on preset attributes through the K-Means and K-NN methods.
确定学生兴趣的K-Means和K-NN方法
印尼普特拉大学巴东分校计算机科学学院信息系统系设有信息技术管理、商业信息系统和工业信息系统三个专业。在第五学期,学习专业知识。下学期将确定专业项目的选择。学位的选择是适应学生的需要和能力。上学期取得的成绩可以看出。这项调查的目的是为学生提供学位收集的建议。本研究使用K-Means和K-Nearest Neighbor方法来获得学生的分类以及最近病例和过去病例之间的相关性。该分析使用13个特征,其中12个是预测因子,1个是选项。通过K-Means和K-NN方法,测试结果可以作为基于预设属性建议学生偏好的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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