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Jurnal SINTA 2

PENERAPAN ALGORITMA WEIGHTED TREE SIMILARITY UNTUK PENCARIAN SEMANTIK

JUTI: Jurnal Ilmiah Teknologi Informasi · Vol. 7 No. 1 (2008) · pp. 39–46

DOI
10.12962/j24068535.v7i1.a60
ISSN
2406-8535, 1412-6389
Published
2008-01-01
Publisher
Institut Teknologi Sepuluh Nopember
Language
eng

Abstract

Full-text search and metadata-enabled search have weakness in the precision of the searched article. This research offers weighted tree similarity algorithm combined with cosine similarity method to count similarity in semantic search. In this method metadata is constructed based on the tree of labelled node, labelled and weighted branch. The structure of tree metadata is constructed based on semantic information like taxonomi, ontologi, preference, synonim, homonym and stemming. From testing result, the precision of search using weighted tree similarity algorithm is better that full-text search and metadata-enabled search.

Collaboration network of the authors

39 authors · 82 co-authorship links · 24 across different organisations

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  • Institut Teknologi Sepuluh Nopember
  • Politeknik Negeri Malang
  • Universitas Dian Nuswantoro
  • University of Brawijaya
  • Universiti Malaysia Terengganu
  • Mercu Buana University
  • Ghent University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Kelly Rossa SungkonoInstitut Teknologi Sepuluh Nopember4
Chastine FatichahInstitut Teknologi Sepuluh Nopember2
Imam GhozaliInstitut Teknologi Sepuluh Nopember2
Rachmad AbdullahInstitut Teknologi Sepuluh Nopember2
Tohari AhmadInstitut Teknologi Sepuluh Nopember1
Solichul HudaUniversitas Dian Nuswantoro1
Bilqis AmaliahInstitut Teknologi Sepuluh Nopember1
Margo PudjiantaraInstitut Teknologi Sepuluh Nopember1
Bayu PriyambadhaUniversity of Brawijaya1
Nurul Fajrin AriyaniInstitut Teknologi Sepuluh Nopember1
Sarwosri SarwosriInstitut Teknologi Sepuluh Nopember1
Alief Yoga PriyantoInstitut Teknologi Sepuluh Nopember1
I Gusti Agung Chintya Prema DewiInstitut Teknologi Sepuluh Nopember1
Muhammad Suzuri HitamUniversiti Malaysia Terengganu1
Ariadi Retno Tri Hayati RiridPoliteknik Negeri Malang1
Dyah Ayu IrawatiPoliteknik Negeri Malang1
Muhammad Aisamuddin Eka PramuditaPoliteknik Negeri Malang1
Rosa Andrie AsmaraPoliteknik Negeri Malang1
Cahya RahmadPoliteknik Negeri Malang1
Ngatmari NgatmariPoliteknik Negeri Malang1
Muhammad Bisri MusthafaPoliteknik Negeri Malang1
Fika Hastarita RachmanInstitut Teknologi Sepuluh Nopember1
Siti RochimahInstitut Teknologi Sepuluh Nopember1
Bambang JokonowoMercu Buana University1
Jan ClaesGhent University1
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember1
Anto Satriyo NugrohoBadan Pengkajian dan Penerapan Teknologi1
Umi SalamahSebelas Maret University1
Ismail Eko Prayitno RoziAffiliation unknown1
Puji Budi Setia AsihEijkman Institute for Molecular Biology1
Dedy Rahman WijayaTelkom University1
Muhammad Nezar MahardikaInstitut Teknologi Sepuluh Nopember1
Agus Tri HaryonoInstitut Teknologi Sepuluh Nopember1
Salsabila SalsabilaInstitut Teknologi Sepuluh Nopember1
Ahmad SaikhuInstitut Teknologi Sepuluh Nopember1
Hermawan Bin FauzanInstitut Teknologi Sepuluh Nopember1
Suhariyanto SuhariyantoInstitut Teknologi Sepuluh Nopember1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://juti.if.its.ac.id/index.php/juti/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">PENERAPAN ALGORITMA WEIGHTED TREE SIMILARITY UNTUK PENCARIAN SEMANTIK</dc:title>
	<dc:creator xml:lang="en">Sarno, Riyanarto</dc:creator>
	<dc:creator xml:lang="en">Rahutomo, Faisal</dc:creator>
	<dc:description xml:lang="en">Full-text search and metadata-enabled search have weakness in the precision of the searched article. This research offers weighted tree similarity algorithm combined with cosine similarity method to count similarity in semantic search. In this method metadata is constructed based on the tree of labelled node, labelled and weighted branch. The structure of tree metadata is constructed based on semantic information like taxonomi, ontologi, preference, synonim, homonym and stemming. From testing result, the precision of search using weighted tree similarity algorithm is better that full-text search and metadata-enabled search. </dc:description>
	<dc:publisher xml:lang="en">Institut Teknologi Sepuluh Nopember</dc:publisher>
	<dc:date>2008-01-01</dc:date>
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	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://juti.if.its.ac.id/index.php/juti/article/view/60</dc:identifier>
	<dc:identifier>10.12962/j24068535.v7i1.a60</dc:identifier>
	<dc:source xml:lang="en">JUTI: Jurnal Ilmiah Teknologi Informasi; Vol 7, No 1, Januari 2008; 39-46</dc:source>
	<dc:source>2406-8535</dc:source>
	<dc:source>1412-6389</dc:source>
	<dc:source>10.12962/j24068535.v7i1</dc:source>
	<dc:language>eng</dc:language>
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