Skripsi

PERANCANGAN MODEL DEEP NEURAL NETWORK UNTUK KLASIFIKASI AUTHOR PADA DATA PUBLIKASI INDONESIA

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Penilaian

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Author Name Disambiguation (AND) is a problem that occurs when a set of publications contains ambiguous names of authors, i.e. the same author may appear with different names (synonyms) in other published papers, or authors who may be different who may have the same name (homonym). In the final project, we will design a model with a Deep Neural Network (DNN). The dataset used in this final project uses primary data sourced from the Scopus website. This research focuses on integrating data from Indonesian authors. Parameters accuracy, sensitivity, and precision are standard benchmarks to determine the performance of the methods used to solve AND problems. The best DNN classification model achieves Accuracy 99.9936%, Sensitivity 93.1433%, Precision 94.3733%. Then for the highest performance measurement, the Non Synonym-Homonym case has 99.9967% Accuracy, 96.7388% Sensitivity, and 97.5102% Precision.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002174	T52947	T529472021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T529472021

Publisher

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

Collation

xv, 55 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

003.307

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)

Model dan simulasi komputer
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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