Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal
International Journal of artificial intelligence research
View Publication Info| Field | Value | |
| Title |
Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal
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| Creator |
Nurfadila, Piska Dwi
Wibawa, Aji Prasetya Zaeni, Ilham Ari Elbaith Nafalski, Andrew |
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| Subject |
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| 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.
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| Publisher |
STMIK Dharma Wacana
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| Contributor |
—
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| Date |
2019-12-30
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| Type |
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
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| Identifier |
http://ijair.id/index.php/ijair/article/view/99
10.29099/ijair.v3i2.99 |
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| Source |
International Journal of Artificial Intelligence Research; Vol 3, No 2 (2019): December
2579-7298 10.29099/ijair.v3i2 |
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| Language |
en
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| Rights |
Copyright (c) 2019 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0 |
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