Pemanfaatan Latent Semantic Indexing untuk Mengukur Potensi Kerjasama Jurnal Ilmiah Lintas Universitas
Main Article Content
Abstract— This paper presents a cooperation recommendation strategy between higher education institution. The recommendation is based on the contents of journals published in a university journal portal. As a case study, we concentrate our approach for the journals with information technology themes. All journals from 10 reputed universities will be compared by using keywords and the contents of the journal themselves. A partnering recommendation list is built by utilizing Latent Semantic Indexing (LSI). LSI technique is used to reduce the curse of dimensionality from the original data set and to generate topical analysis from all journals as semantic representation for each journals. Topic modeling is used to calculate the categorical similarity in the data set of each university journal and a search query. After all categorical similarities have been calculated, an average value of journal topics coherence is used to construct the final recommendation of partner candidates. This approach ensure that the final recommendation is based on the interest of each university rather than the frequencies of matched keywords in each journal.
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How to Cite
E. H. Fernando and H. Toba, “Pemanfaatan Latent Semantic Indexing untuk Mengukur Potensi Kerjasama Jurnal Ilmiah Lintas Universitas”, JuTISI, vol. 6, no. 3, Dec. 2020.
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial used, distribution and reproduction in any medium.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.