Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms
In an era of collaboration, knowing someone's expertise is becoming increasingly necessary. Recognizing individuals' proficiency can be challenging because it requires considerable manual time. This study explores the expertise of lecturers from the Computer Science Department, Universitas...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2025-05-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6397 |
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| author | Lyla Ruslana Aini Evi Yulianti |
| author_facet | Lyla Ruslana Aini Evi Yulianti |
| author_sort | Lyla Ruslana Aini |
| collection | DOAJ |
| description | In an era of collaboration, knowing someone's expertise is becoming increasingly necessary. Recognizing individuals' proficiency can be challenging because it requires considerable manual time. This study explores the expertise of lecturers from the Computer Science Department, Universitas Indonesia (Fasilkom UI), based on scientific publications. The data were obtained from the Sinta journal website’s scrapping process, which includes Scopus, Garuda, and Google Scholar data sources. The approach used was keyword extraction using the adjusted TF-IDF. The resulting keywords were then mapped to the ACM classification class using cosine similarity calculations with various embedding models, including BERT, BERT multilingual, FastText, XLM Roberta, and SBERT. The experimental results highlighted that combining the adjusted TF-IDF with mapping to the ACM classes using SBERT is a promising approach for gaining the best expertise. The use of abstract data has proved to be better than that of full-text data. Using title-abstract-EN data achieved a score of 0.49 for both the P@1 and NDCG@1 metrics, whereas the title-abstract-ENID data attained a score of 0.75 for both metrics P@1 and NDCG@1. |
| format | Article |
| id | doaj-art-a3c4f1994042493080ea7c692d5cb381 |
| institution | DOAJ |
| issn | 2580-0760 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-a3c4f1994042493080ea7c692d5cb3812025-08-20T02:42:00ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602025-05-019349750510.29207/resti.v9i3.63976397Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification TermsLyla Ruslana Aini0Evi Yulianti1Badan Riset dan Inovasi NasionalUniversitas IndonesiaIn an era of collaboration, knowing someone's expertise is becoming increasingly necessary. Recognizing individuals' proficiency can be challenging because it requires considerable manual time. This study explores the expertise of lecturers from the Computer Science Department, Universitas Indonesia (Fasilkom UI), based on scientific publications. The data were obtained from the Sinta journal website’s scrapping process, which includes Scopus, Garuda, and Google Scholar data sources. The approach used was keyword extraction using the adjusted TF-IDF. The resulting keywords were then mapped to the ACM classification class using cosine similarity calculations with various embedding models, including BERT, BERT multilingual, FastText, XLM Roberta, and SBERT. The experimental results highlighted that combining the adjusted TF-IDF with mapping to the ACM classes using SBERT is a promising approach for gaining the best expertise. The use of abstract data has proved to be better than that of full-text data. Using title-abstract-EN data achieved a score of 0.49 for both the P@1 and NDCG@1 metrics, whereas the title-abstract-ENID data attained a score of 0.75 for both metrics P@1 and NDCG@1.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6397adjusted tf-idfacm classificationbertexpertisefasttextbert multilingualsbertxlm-roberta |
| spellingShingle | Lyla Ruslana Aini Evi Yulianti Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) adjusted tf-idf acm classification bert expertise fasttext bert multilingual sbert xlm-roberta |
| title | Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms |
| title_full | Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms |
| title_fullStr | Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms |
| title_full_unstemmed | Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms |
| title_short | Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms |
| title_sort | expertise retrieval using adjusted tf idf and keyword mapping to acm classification terms |
| topic | adjusted tf-idf acm classification bert expertise fasttext bert multilingual sbert xlm-roberta |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6397 |
| work_keys_str_mv | AT lylaruslanaaini expertiseretrievalusingadjustedtfidfandkeywordmappingtoacmclassificationterms AT eviyulianti expertiseretrievalusingadjustedtfidfandkeywordmappingtoacmclassificationterms |