Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal 

International Journal of artificial intelligence research

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Field Value
 
Title Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal 
 
Creator Nurfadila, Piska Dwi
Wibawa, Aji Prasetya
Zaeni, Ilham Ari Elbaith
Nafalski, Andrew
 
Subject

 
Description Classification of economic journal articles has been done using the VSM (Vector Space Model) approach and the Cosine Similarity method. The results of previous studies are considered to be less optimal because Stopword Removal was carried out by using a dictionary of basic words (tuning). Therefore, the omitted words limited to only basic words. This study shows the improved performance accuracy of the Cosine Similarity method using frequency-based Stopword Removal. The reason is because the term with a certain frequency is assumed to be an insignificant word and will give less relevant results. Performance testing of the Cosine Similarity method that had been added to frequency-based Stopword Removal was done by using K-fold Cross Validation. The method performance produced accuracy value for 64.28%, precision for 64.76 %, and recall for 65.26%. The execution time after pre-processing was 0, 05033 second.
 
Publisher STMIK Dharma Wacana
 
Contributor
 
Date 2019-07-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Identifier http://ijair.id/index.php/ijair/article/view/99
10.29099/ijair.v3i2.99
 
Source International Journal of Artificial Intelligence Research; Vol 3, No 2 (2019): In Press
2579-7298
10.29099/ijair.v3i2
 
Language en
 
Rights Copyright (c) 2019 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0
 

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