TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Analyzing Factors Contributing to Research Performance using Backpropagation Neural Network and Support Vector Machine |
| 영문제목(English Title) |
Analyzing Factors Contributing to Research Performance using Backpropagation Neural Network and Support Vector Machine |
| 저자(Author) |
Ermatita
Ahmad Sanmorino
Samsuryadi
Dian Palupi Rini
|
| 원문수록처(Citation) |
VOL 15 NO. 12 PP. 0153 ~ 0172 (2021. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In this study, the authors intend to analyze factors contributing to research performance using Backpropagation Neural Network and Support Vector Machine. The analyzing factors contributing to lecturer research performance start from defining the features. The next stage is to collect datasets based on defining features. Then transform the raw dataset into data ready to be processed. After the data is transformed, the next stage is the selection of features. Before the selection of features, the target feature is determined, namely research performance. The selection of features consists of Chi-Square selection (U), and Pearson correlation coefficient (CM). The selection of features produces eight factors contributing to lecturer research performance are Scientific Papers (U: 154.38, CM: 0.79), Number of Citation (U: 95.86, CM: 0.70), Conference (U: 68.67, CM: 0.57), Grade (U: 10.13, CM: 0.29) , Grant (U: 35.40, CM: 0.36), IPR (U: 19.81, CM: 0.27), Qualification (U: 2.57, CM: 0.26), and Grant Awardee (U: 2.66, CM: 0.26). To analyze the factors, two data mining classifiers were involved, Backpropagation Neural Networks (BPNN) and Support Vector Machine (SVM). Evaluation of the data mining classifier with an accuracy score for BPNN of 95 percent, and SVM of 92 percent. The essence of this analysis is not to find the highest accuracy score, but rather whether the factors can pass the test phase with the expected results. The findings of this study reveal the factors that have a significant impact on research performance and vice versa. |
| 키워드(Keyword) |
Factors contributing to research performance
selection of features
backpropagation
support vector machine
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| 파일첨부 |
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