Sistem Rekomendasi Program Studi Sarjana Berbasis Machine Learning Untuk Model Klasifikasi Calon Mahasiswa Baru

A. Akbar, Yogi, Ananto, Suprayuandi Pratama
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引用次数: 0

Abstract

The Recommendation System can produce system requirements data that is more descriptive and easy to implement into software. Recommendation methods are combined to form more comprehensive recommendations based on fermat points. The education sector has never been indifferent to new technologies, and eventually switched to using the Internet. Prior to conducting this research, a review was carried out on various previous research results related to the lack of the role of knowledge space and user preferences in the data visualization recommendation system [3]. Broadly speaking, this study uses the method of recommending the Analysis and Discussion system. One of the main elements that must be considered in the system analysis stage is software problems, because the software used must be in accordance with the problem to be solved. In this stage, the search and collection of data and knowledge obtained by the expert system are carried out. A machine learning-based recommendation system for undergraduate study programs can help prospective new students choose the right study program according to their interests and talents. The classification model used can produce fairly high accuracy in recommending undergraduate study programs to prospective new students.
为未来大学生的分类模式设计基于机器学习的本科学习计划推荐系统
推荐系统可以生成更具描述性和易于在软件中实现的系统需求数据。将推荐方法结合起来,形成基于费马点的更全面的推荐。教育部门从未对新技术漠不关心,并最终转向使用互联网。在进行本研究之前,我们对以往的各种研究结果进行了回顾,这些研究结果涉及到在数据可视化推荐系统中缺乏知识空间和用户偏好的作用[3]。从广义上讲,本研究采用了推荐分析与讨论系统的方法。在系统分析阶段必须考虑的主要因素之一是软件问题,因为所使用的软件必须符合要解决的问题。在这一阶段,对专家系统获得的数据和知识进行搜索和收集。基于机器学习的本科学习项目推荐系统可以帮助未来的新生根据自己的兴趣和才能选择合适的学习项目。所使用的分类模型在向未来的新生推荐本科学习项目时可以产生相当高的准确性。
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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