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Examiners Recommendation System at Proposal Seminar of Undergraduate Thesis by Using Content- based Filtering

机译:基于内容过滤的本科毕业论文选拔考试考官推荐系统

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Undergraduate thesis is a student scientific activity which is accountable and also needs supervision and examination from lecturers to make sure it has a good quality. Therefore, supervisor and examiner should be the person that expert in a specific theme of undergraduate thesis. The purpose of this research is to build the examiners recommendation system on proposal seminar of undergraduate thesis. The method that applied is Content-based Filtering. Content-based filtering is applied because this research focuses in using the content of document. Undergraduate thesis report document is used as reference in this recommendation system. Undergraduate thesis report document is grouped based on the theme by using K-Means Clustering. The closeness of undergraduate thesis proposal is calculated from every centroid produced. The system will recommend which lecturers are in the cluster of nearest centroid. System testing is performed by measuring system performance using Ordered Analysis with Euclidean distance. The result of recommendation system has error value 0.385 which means the recommendation system has average level in the range of scoring 0-1. The accuracy of subset between recommendation result and actual data is 85%.
机译:本科论文是一项负责任的学生科学活动,还需要得到讲师的监督和检查,以确保其质量良好。因此,督导员和考官应该是本科论文特定主题的专家。本研究的目的是在本科生的应聘研讨会上建立考官推荐系统。应用的方法是基于内容的筛选。之所以应用基于内容的过滤,是因为该研究集中于使用文档的内容。本推荐系统以大学生论文报告文件为参考。使用K-Means聚类,基于主题将本科毕业论文报告文档进行分组。本科生论文提案的接近度是根据产生的每个质心来计算的。系统将建议哪些讲师在最近的形心中。通过使用带欧氏距离的有序分析来测量系统性能来执行系统测试。推荐系统的结果具有0.385的误差值,这意味着推荐系统的平均级别在得分0-1范围内。推荐结果与实际数据之间的子集的准确度为85%。

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