Keywords
scraping
tf-idf
vector space model
searching
How to Cite
Abstract
One of the main feature of digital library is a search engine which depends on keywords submitted by a user. However, in the traditional algorithm, the computation performance, searching speed, significantly relies on the number of journal articles stored in the databases. Some irrelevant search results also increase the speed of article searching process. To solve the problem, in this paper we propose vector space model (VSM) algorithm to search for relevant journal articles. The VSM algorithm considers a term frequency - inversed document frequency (TF-IDF). The VSM algorithm will be compared to the baseline algorithm namely traditional algorithm. Both algorithms will be evaluated using combination of keywords which can be a synonym, phrase, error typography, or suffix and prefix. By using the data consist of 635 journal articles, both algorithms are compared in terms of 11 evaluation criteria. The results show that VSM algorithm is able to obtain the intended journal at 5th rank on average as compared to the traditional algorithm which can obtain the intended journal at rank of 171st on average. Therefore, our proposed algorithm can improve the performance to accurately sort the journal articles based on the submitted keywords as compared to traditional algorithm.
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