Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
T495242021	T49524	T495242021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T495242021

Publisher

Inderalaya : Fakultas Ilmu komputer, Prodi Sitem Komputer., 2021

Collation

xiv, 75 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sitem Komputer
Pemerosesan Data-Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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