Lecturer Research Performance Model Evaluation using Machine Learning Approach


Authors : Ahmad Sanmorino

Volume/Issue : Volume 6 - 2021, Issue 7 - July

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/3iyH8Yu

Through this study, the author evaluates the lecturer's research performance model built on previous research. This model consists of seven independent variables and one dependent variable. The seven independent variables that construct the model are Scientific Article, H-Score, College Type, Journal Cluster, Research Grant, Research Collaboration, Research Interest, while the dependent variable is research performance. Based on the results of the evaluation using the machine learning approach, a good accuracy score was obtained for each classifier, for Random Forest at 93 percent, Multi-layer Perceptron at 90 percent, Decision Tree at 97 percent, and Linear Discriminant Analysis at 93 percent. The results of this evaluation show that the proposed research performance model of the lecturer meets the author's expectations and is relevant to the conditions of higher learning institutions.

Keywords : Research Performance Model; Lecturer; Evaluation; Machine Learning.

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