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Image of KLASIFIKASI AUTHOR MATCHING PADA DATA BIBLIOGRAFI MENGGUNAKAN METODE COST-SENSITIVE DEEP NEURAL NETWORK (CSDNN)

Skripsi

KLASIFIKASI AUTHOR MATCHING PADA DATA BIBLIOGRAFI MENGGUNAKAN METODE COST-SENSITIVE DEEP NEURAL NETWORK (CSDNN)

Lestari, Suci Dwi - Personal Name;

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Penilaian anda saat ini :  

Author Name Ambiguity is an issue that occurs when publication records contain ambiguous or ambiguous author names, i.e. the same author may appear under different names, or different authors may have similar names. The method proposed in this research is Cost-Sensitive Deep Neural Network (CSDNN). The bibliographic dataset used is the DBLP Dataset by Jinseok Kim, et al. This research focuses on the use of classification methods, namely CSDNN and DNN. The main parameters of the research carried out are accuracy, precision, specificity, recall, and error rate, which are important parameters to determine the success rate of the method used in overcoming problems AND in particular finding the similarities of the authors. The CSDNN classification resulted achieves accuracy, precision, specificity, recall, and error rate which is 99.94%, 96.60%, 99.97%, 96.90%, and 0.000515. DNN classification resulted achieves accuracy, precision, specificity, recall, and error rate which is 99.94%, 99.97%, 96.90%, 96.78%, and 0.000501.


Availability
Inventory Code Barcode Call Number Location Status
T495242021T49524T495242021Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T495242021
Publisher
Inderalaya : Fakultas Ilmu komputer, Prodi Sitem Komputer., 2021
Collation
xiv, 75 hlm,: ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
004.07
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Prodi Sitem Komputer
Pemerosesan Data-Ilmu Komputer
Specific Detail Info
-
Statement of Responsibility
MURZ
Other version/related

No other version available

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  • KLASIFIKASI AUTHOR MATCHING PADA DATA BIBLIOGRAFI MENGGUNAKAN METODE COST-SENSITIVE DEEP NEURAL NETWORK (CSDNN)
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