PENERAPAN PENGUKUR KESAMAAN ATRIBUT JUDUL BERBASIS STRING PADA DATA BIBLIOGRAFI UNTUK MENINGKATKAN KINERJA KLASIFIKASI KESAMAAN PENULIS

QILIQSANDY, MUHAMMAD EL and Firdaus, Firdaus (2021) PENERAPAN PENGUKUR KESAMAAN ATRIBUT JUDUL BERBASIS STRING PADA DATA BIBLIOGRAFI UNTUK MENINGKATKAN KINERJA KLASIFIKASI KESAMAAN PENULIS. Undergraduate thesis, Sriwijaya University.

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Abstract

Author Name Disambiguation (AND) is a problem of name ambiguity to the publication in a Digital Library (DL) database caused by the Homonymy and Synonymy of the author's name. The proposed method is a Deep Neural Network (DNN) and Support Vector Machine (SVM) to classify data. The DBLP Labeled Data dataset by Jinseok Kim, et. al. is used for the classification task. This study concerned with processing data with the techniques of normalization and transformation data to create an effective feature for classification. The performance evaluation of the research conducted is accuracy, precision, and recall. The parameters are important to evaluate the AND classification process, especially the identification of the author. For the result, DNN achieves accuracy, precision, and recall, which is 99.98%, 97.71%, and 97.83%, respectively. In addition, SVM produces accuracy, precision, and recall 99.98%, 95.33%, 95.09%, respectively. From the comparison of the two classification methods, DNN outperformed SVM for data classification and author identification.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kecerdasan Buatan, Text Processing
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mohammad El Qiliqsandy
Date Deposited:	05 Aug 2022 02:04
Last Modified:	11 Aug 2022 07:16
URI:	http://repository.unsri.ac.id/id/eprint/75918

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