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KLASIFIKASI AUTHOR MATCHING PADA DATA BIBLIOGRAFI MENGGUNAKAN METODE RECURRENT NEURAL NETWORK

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In bibliographic data analysis, accurate author matching is crucial for various applications such as citation analysis and research profiling. This research focuses on the classification of author matching in bibliographic data using RNNs method. By leveraging the power of RNNs, the authors aim to improve the accuracy of author matching classification in two specific scenarios: homonym and synonym cases. The authors utilize parameters such as Author, Co-Author, Title, Year, and Venue. In the homonym case, the authors achieved an excellent accuracy rate of 96.19% using the best-performing model. This high accuracy demonstrates the effectiveness of the RNNs method in distinguishing between authors with similar names. Furthermore, the authors also investigated the synonym case, where authors may have different names but refer to the same entity. In this scenario, they obtained a good accuracy rate of 9.81%, indicating the potential of RNNs in identifying synonymous authors in bibliographic data. Overall, this research highlights the important role of RNNs in author matching classification in bibliographic data. Despite using non-tabular data, the results demonstrate the effectiveness of this method in distinguishing between authors with similar names, both in homonym and synonym cases.

Availability
Inventory Code Barcode Call Number Location Status
2307001917T105878T1058782023Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1058782023
Publisher
Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xiv, 53 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
004.650 7
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Jurusan Sistem Komputer
Jaringan Komunikasi Komputer
Specific Detail Info
-
Statement of Responsibility
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No other version available

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