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

DOI: https://doi.org/10.29099/ijair.v3i2.99
Piska Dwi Nurfadila(1*), Aji Prasetya Wibawa(2), Ilham Ari Elbaith Zaeni(3), Andrew Nafalski(4),


(1) Electrical Engineering Department, Universitas Negeri Malang
(2) Scopus ID : 56012410400; Dept Electrical Engineering, State University of Malang, Malang
(3) Electrical Engineering Department, Universitas Negeri Malang
(4) University of South Australia
(*) Corresponding Author

Abstract


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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DOI: https://doi.org/10.29099/ijair.v3i2.99

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