期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：6
页码：184
DOI：10.14569/IJACSA.2021.0120620
出版社：Science and Information Society (SAI)
摘要：The purpose of this study is to build a framework for improving research productivity in higher education institutions. The research begins by collecting data and defining candidate variables. The next process is to determine the selected variable from the candidate variable. Variable selection is carried out in three stages, univariate selection, feature importance, and correlation matrix. After the variable selection stage, eight input variables and one target variable were obtained. The eight input variables are Article (C), Conference (CO), Grant (GT), Research Grantee (RG), Rank (R), Degree (D), IPR, and Citation (C). The target variable is Research Productivity (RP). This selected variable is used to build the framework. The next step is to test the framework that has been built. The testing process involves four data mining classifiers, Support Vector Machine, Decision Tree, K-Nearest Neighbor, and Naïve Bayes. The classification results are tested using confusion matrix-based testing, accuracy, precision, sensitivity, and f-measure. The testing results show the proposed framework is able to obtain high accuracy scores for each classification algorithm. It means the proposed framework is relevant to use.
关键词：Framework; research productivity; variable selection; data mining classifier
