PENERAPAN KNOWLEDGE-BASED TEXT SIMILARITY UNTUK MENINGKATKAN AKURASI KLASIFIKASI KESESUAIAN PENULIS PADA DATA BIBLIOGRAFI

HARAHAP, ANNISA KARIMA R and Firdaus, Firdaus (2021) PENERAPAN KNOWLEDGE-BASED TEXT SIMILARITY UNTUK MENINGKATKAN AKURASI KLASIFIKASI KESESUAIAN PENULIS PADA DATA BIBLIOGRAFI. Undergraduate thesis, Sriwijaya University.

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Abstract

In research using data in the form of text, a problem theme can be drawn called Author Name Disambiguation (AND). The theme can include a case of synonymy and polysemy that occurs when classifying authors in bibliographic data. This research will then use Deep Neural Network (DNN) as a classifier used to classify authors. Before the classification process is carried out, the data must enter the pre-processing stage. For feature extraction, the level of similarity will be calculated after combining data using cosine similarity for the author name, author list, and venue attributes as well as absolute reduction for the year attribute. Specifically for the title attribute, the knowledge-based text similarity method will be used to calculate the level of similarity which is carried out using two approaches and wordnet as the ontology. Furthermore, the results will be evaluated using performance measurement as a reference for its success. Accuracy obtained reached 99% for both knowledge-based text similarity approaches, namely path and wu palmer.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Author Name Disambiguation, Sinonim, Polisemi, Data Bibliografi, Deep Neural Network (DNN), Knowledge-Based Text Similarity, Cosine Similarity
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:	Annisa Karima R. Harahap
Date Deposited:	26 Aug 2021 02:02
Last Modified:	26 Aug 2021 02:02
URI:	http://repository.unsri.ac.id/id/eprint/52713

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