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

FAHREZA, IRVAN and Firdaus, Firdaus (2021) PERANCANGAN MODEL DEEP NEURAL NETWORK UNTUK KLASIFIKASI AUTHOR PADA DATA PUBLIKASI INDONESIA. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Author Name Disambiguation, Synonym, Homonym, Bibliographic Data, Deep Neural Network.
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Irvan Fahreza
Date Deposited:	03 Sep 2021 03:45
Last Modified:	03 Sep 2021 03:45
URI:	http://repository.unsri.ac.id/id/eprint/52947

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