KLASIFIKASI AUTHOR PADA DATA BIBLIOGRAFI MENGGUNAKAN DEEP NEURAL NETWORK DAN SUPPORT VECTOR MACHINE
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Institusion
Universitas Sriwijaya
Author
TIO ARTHA NUGRAHA (STUDENT ID : 09011181520027)
Firdaus Firdaus (LECTURER ID : 0221017801)
Firdaus Firdaus (LECTURER ID : 0221017801)
Subject
Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Datestamp
2020-01-06 08:31:10
Abstract :
Author Name Disambiguation (AND) is a problem of name ambiguity to the publication in a Digital Library (DL) database caused by the Homonymity and Synonymity 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.
Author Name Disambiguation (AND) is a problem of name ambiguity to the publication in a Digital Library (DL) database caused by the Homonymity and Synonymity 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.
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RAMA_56201_09011181520027.pdf
RAMA_56201_09011181520027_TURNITIN.pdf
RAMA_56201_09011181520027_ 0221017801_01_front_ref.pdf
RAMA_56201_09011181520027_ 0221017801_02.pdf
RAMA_56201_09011181520027_ 0221017801_03.pdf
RAMA_56201_09011181520027_ 0221017801_04.pdf
RAMA_56201_09011181520027_ 0221017801_05.pdf
RAMA_56201_09011181520027_ 0221017801_06_ref.pdf
RAMA_56201_09011181520027_ 0221017801_07_lamp.pdf
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