Authors :
Ahmad Sanmorino
Volume/Issue :
Volume 6 - 2021, Issue 7 - July
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/3iyH8Yu
Abstract :
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.
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.
