Text

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

---

Penilaian

---

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.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2207002762	T75918	T759182022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T759182022

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xiii, 43 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

005.707

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Jurusan Sistem Komputer
Data dalam sistem-sistem komputer

Specific Detail Info

-

Statement of Responsibility

SEPTA

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment