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Automatic Assessment of Technology Readiness Level Using LLDA-Helmholtz for Ranking University

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@article{IJASEIT14525,
   author = {Bagus Setya Rintyarna and Riyanarto Sarno and Eko Putro Fitrianto and Anugrah Yulindra Satyaji},
   title = {Automatic Assessment of Technology Readiness Level Using LLDA-Helmholtz for Ranking University},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {6},
   year = {2021},
   pages = {2416--2421},
   keywords = {Technology readiness level; labeled latent Dirichlet allocation; Helmholtz principle; bloom taxonomy; Pearson correlation.},
   abstract = {

The assessment process of Technology Readiness Level using the questionnaire-based tool for Indonesian university's academic papers is considered to be labor-intensive. This paper introduces a new method of determining the TRL of an academic paper based on a text mining technique. The content of the research paper represented by their abstract published by university lecturers is justified to represent the technology maturity of research. Abstracts of papers were collected from the nine most reputable universities in Indonesia. By utilizing Labelled Latent Dirichlet Allocation, the abstracts were categorized into 1 of 9 levels of TRL. To determine the prior label of LLDA, we built a corpus of keywords representing each TRL level based on Bloom Taxonomy. Beforehand, Helmoltz principle was utilized to select the text feature. Since Bloom Taxonomy has only six levels, we split the keywords into 9 level. Afterward, the reputation score is calculated using our formula. Lastly, the university ranking is generated according to the extracted academic reputation score. To evaluate the proposed method, we compare our rank with QS’s. We calculate the ranking gap and Pearson correlation to evaluate the result. Helmholtz has successfully pruned 86% of features. The utilization of Helmholtz significantly improves the Pearson correlation of our proposed method. In short, the new insight of university ranking introduced in this work is promising. For all indicator experiments, LLDA-Helmholtz performed better results indicated by 0.95 Pearson correlation between two rankings, while for LLDA without Helmhotz, the correlation is 0.78.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14525},    doi = {10.18517/ijaseit.11.6.14525} }

EndNote

%A Rintyarna, Bagus Setya
%A Sarno, Riyanarto
%A Putro Fitrianto, Eko
%A Yulindra Satyaji, Anugrah
%D 2021
%T Automatic Assessment of Technology Readiness Level Using LLDA-Helmholtz for Ranking University
%B 2021
%9 Technology readiness level; labeled latent Dirichlet allocation; Helmholtz principle; bloom taxonomy; Pearson correlation.
%! Automatic Assessment of Technology Readiness Level Using LLDA-Helmholtz for Ranking University
%K Technology readiness level; labeled latent Dirichlet allocation; Helmholtz principle; bloom taxonomy; Pearson correlation.
%X 

The assessment process of Technology Readiness Level using the questionnaire-based tool for Indonesian university's academic papers is considered to be labor-intensive. This paper introduces a new method of determining the TRL of an academic paper based on a text mining technique. The content of the research paper represented by their abstract published by university lecturers is justified to represent the technology maturity of research. Abstracts of papers were collected from the nine most reputable universities in Indonesia. By utilizing Labelled Latent Dirichlet Allocation, the abstracts were categorized into 1 of 9 levels of TRL. To determine the prior label of LLDA, we built a corpus of keywords representing each TRL level based on Bloom Taxonomy. Beforehand, Helmoltz principle was utilized to select the text feature. Since Bloom Taxonomy has only six levels, we split the keywords into 9 level. Afterward, the reputation score is calculated using our formula. Lastly, the university ranking is generated according to the extracted academic reputation score. To evaluate the proposed method, we compare our rank with QS’s. We calculate the ranking gap and Pearson correlation to evaluate the result. Helmholtz has successfully pruned 86% of features. The utilization of Helmholtz significantly improves the Pearson correlation of our proposed method. In short, the new insight of university ranking introduced in this work is promising. For all indicator experiments, LLDA-Helmholtz performed better results indicated by 0.95 Pearson correlation between two rankings, while for LLDA without Helmhotz, the correlation is 0.78.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14525 %R doi:10.18517/ijaseit.11.6.14525 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 6 %@ 2088-5334

IEEE

Bagus Setya Rintyarna,Riyanarto Sarno,Eko Putro Fitrianto and Anugrah Yulindra Satyaji,"Automatic Assessment of Technology Readiness Level Using LLDA-Helmholtz for Ranking University," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 6, pp. 2416-2421, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.6.14525.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Rintyarna, Bagus Setya
AU  - Sarno, Riyanarto
AU  - Putro Fitrianto, Eko
AU  - Yulindra Satyaji, Anugrah
PY  - 2021
TI  - Automatic Assessment of Technology Readiness Level Using LLDA-Helmholtz for Ranking University
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 6
Y2  - 2021
SP  - 2416
EP  - 2421
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Technology readiness level; labeled latent Dirichlet allocation; Helmholtz principle; bloom taxonomy; Pearson correlation.
N2  - 

The assessment process of Technology Readiness Level using the questionnaire-based tool for Indonesian university's academic papers is considered to be labor-intensive. This paper introduces a new method of determining the TRL of an academic paper based on a text mining technique. The content of the research paper represented by their abstract published by university lecturers is justified to represent the technology maturity of research. Abstracts of papers were collected from the nine most reputable universities in Indonesia. By utilizing Labelled Latent Dirichlet Allocation, the abstracts were categorized into 1 of 9 levels of TRL. To determine the prior label of LLDA, we built a corpus of keywords representing each TRL level based on Bloom Taxonomy. Beforehand, Helmoltz principle was utilized to select the text feature. Since Bloom Taxonomy has only six levels, we split the keywords into 9 level. Afterward, the reputation score is calculated using our formula. Lastly, the university ranking is generated according to the extracted academic reputation score. To evaluate the proposed method, we compare our rank with QS’s. We calculate the ranking gap and Pearson correlation to evaluate the result. Helmholtz has successfully pruned 86% of features. The utilization of Helmholtz significantly improves the Pearson correlation of our proposed method. In short, the new insight of university ranking introduced in this work is promising. For all indicator experiments, LLDA-Helmholtz performed better results indicated by 0.95 Pearson correlation between two rankings, while for LLDA without Helmhotz, the correlation is 0.78.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14525 DO - 10.18517/ijaseit.11.6.14525

RefWorks

RT Journal Article
ID 14525
A1 Rintyarna, Bagus Setya
A1 Sarno, Riyanarto
A1 Putro Fitrianto, Eko
A1 Yulindra Satyaji, Anugrah
T1 Automatic Assessment of Technology Readiness Level Using LLDA-Helmholtz for Ranking University
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 6
YR 2021
SP 2416
OP 2421
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Technology readiness level; labeled latent Dirichlet allocation; Helmholtz principle; bloom taxonomy; Pearson correlation.
AB 

The assessment process of Technology Readiness Level using the questionnaire-based tool for Indonesian university's academic papers is considered to be labor-intensive. This paper introduces a new method of determining the TRL of an academic paper based on a text mining technique. The content of the research paper represented by their abstract published by university lecturers is justified to represent the technology maturity of research. Abstracts of papers were collected from the nine most reputable universities in Indonesia. By utilizing Labelled Latent Dirichlet Allocation, the abstracts were categorized into 1 of 9 levels of TRL. To determine the prior label of LLDA, we built a corpus of keywords representing each TRL level based on Bloom Taxonomy. Beforehand, Helmoltz principle was utilized to select the text feature. Since Bloom Taxonomy has only six levels, we split the keywords into 9 level. Afterward, the reputation score is calculated using our formula. Lastly, the university ranking is generated according to the extracted academic reputation score. To evaluate the proposed method, we compare our rank with QS’s. We calculate the ranking gap and Pearson correlation to evaluate the result. Helmholtz has successfully pruned 86% of features. The utilization of Helmholtz significantly improves the Pearson correlation of our proposed method. In short, the new insight of university ranking introduced in this work is promising. For all indicator experiments, LLDA-Helmholtz performed better results indicated by 0.95 Pearson correlation between two rankings, while for LLDA without Helmhotz, the correlation is 0.78.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14525 DO - 10.18517/ijaseit.11.6.14525