Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest
The number of published academic papers has been increasing rapidly from year to year. However, this increase in publications must be linear with an emphasis on quality. To ensure that academic papers meet the required quality standard, the peer review process is necessary. The main objective of the...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2023-08-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4900 |
| _version_ | 1827362133769191424 |
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| author | Adi Setyo Nugroho Ahmad Saikhu Ratih Nur Esti Anggraini |
| author_facet | Adi Setyo Nugroho Ahmad Saikhu Ratih Nur Esti Anggraini |
| author_sort | Adi Setyo Nugroho |
| collection | DOAJ |
| description | The number of published academic papers has been increasing rapidly from year to year. However, this increase in publications must be linear with an emphasis on quality. To ensure that academic papers meet the required quality standard, the peer review process is necessary. The main objective of the assignment of reviewers is to find the appropriate reviewer who can conduct a review based on their field of research. However, there are potential obstacles when there is a conflict of interest in the process. This study aims to develop a method for assigning reviewers that overcomes such obstacles. Our approach involves combining the Latent Dirichlet Allocation (LDA), Classification, and Link Prediction methods. LDA is used to find topics from the research data of prospective reviewers to ensure that the assigned reviewers are well suited to the submitted article. These data were used as training data for classification using Random Forest. Finally, link prediction implemented to make reviewer recommendations. We evaluated and compared our proposed method with previous research that used cosine similarity as the last step in recommendation, using Mean Average Precision (MAP). Our proposed method achieved a MAP value of 0.87, which was an improvement compared to the previous approach. These results suggest that our approach has the potential to improve the effectiveness of academic peer review. |
| first_indexed | 2024-03-08T07:20:38Z |
| format | Article |
| id | doaj.art-af2469017887445aacc185cd4acdbdcb |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T07:20:38Z |
| publishDate | 2023-08-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-af2469017887445aacc185cd4acdbdcb2024-02-02T23:44:04ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602023-08-017483784410.29207/resti.v7i4.49004900Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of InterestAdi Setyo Nugroho0Ahmad Saikhu1Ratih Nur Esti Anggraini2Institut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberThe number of published academic papers has been increasing rapidly from year to year. However, this increase in publications must be linear with an emphasis on quality. To ensure that academic papers meet the required quality standard, the peer review process is necessary. The main objective of the assignment of reviewers is to find the appropriate reviewer who can conduct a review based on their field of research. However, there are potential obstacles when there is a conflict of interest in the process. This study aims to develop a method for assigning reviewers that overcomes such obstacles. Our approach involves combining the Latent Dirichlet Allocation (LDA), Classification, and Link Prediction methods. LDA is used to find topics from the research data of prospective reviewers to ensure that the assigned reviewers are well suited to the submitted article. These data were used as training data for classification using Random Forest. Finally, link prediction implemented to make reviewer recommendations. We evaluated and compared our proposed method with previous research that used cosine similarity as the last step in recommendation, using Mean Average Precision (MAP). Our proposed method achieved a MAP value of 0.87, which was an improvement compared to the previous approach. These results suggest that our approach has the potential to improve the effectiveness of academic peer review.http://jurnal.iaii.or.id/index.php/RESTI/article/view/4900reviewerdblp datasetlatent dirichlet allocation (lda)random forestlink predictionconflict of interest (coi) |
| spellingShingle | Adi Setyo Nugroho Ahmad Saikhu Ratih Nur Esti Anggraini Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) reviewer dblp dataset latent dirichlet allocation (lda) random forest link prediction conflict of interest (coi) |
| title | Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest |
| title_full | Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest |
| title_fullStr | Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest |
| title_full_unstemmed | Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest |
| title_short | Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest |
| title_sort | development of reviewer assignment method with latent dirichlet allocation and link prediction to avoid conflict of interest |
| topic | reviewer dblp dataset latent dirichlet allocation (lda) random forest link prediction conflict of interest (coi) |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4900 |
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