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Optimasi Suffix Tree Clustering dengan Wordnet dan Named Entity Recognition untuk Pengelompokan Dokumen

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 4 No. 4 (2017) · pp. 263–267

DOI
10.25126/jtiik.201744400
ISSN
2528-6579, 2355-7699
Published
2017-12-31
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
en

Abstract

AbstrakSemakin meningkatnya jumlah dokumen teks di dunia digital mempengaruhi banyaknya jumlah informasi dan menyebabkan kesulitan dalam proses temu kembali informasi (information retreival). Clustering dokumen merupakan suatu bidang text mining yang penting dan dapat digunakan untuk mengefisienkan dalam pengelolaan teks serta peringkasan teks. Namun beberapa permasalahan muncul dalam clustering dokumen teks terutama dalam dokumen berita seperti ambiguitas dalam content, overlapping cluster, dan struktur unik yang terdapat dalam dokumen berita. Penelitian ini mengusulkan metode baru yaitu optimasi Suffix Tree Clustering (STC) dengan WordNet dan Named Entity Recognition (NER) untuk pengelompokan dokumen. Metode ini memiliki beberapa tahap, yaitu prepocessing dokumen dengan mengekstraksi named entity serta melakukan deteksi sinonim berdasarkan WordNet. Tahap kedua adalah pembobotan term dengan tfidf dan nerfidf. Tahap ketiga adalah melakukan clustering dokumen dengan menggunakan Suffix Tree Clustering. Berdasarkan pengujian didapatkan rata-rata nilai precision sebesar 79.83%, recall 77.25%, dan f-measure78.30 %.Kata kunci: Clustering dokumen, Named Entity Recognition, Suffix Tree Clustering, WordNetAbstractThe increasingnumber oftext documentsin the internet, influence on the number of information and lead to difficulties in the process of information retrieval. Documents clustering is main field of text mining and can be used to stream line the management of text and summarization of text. However, some problems a risein documents clustering, especially in news documents such as ambiguity in the content, overlapping clusters, and theuniquestructure ofthe news thatcontained inthe document. Inthisresearch, we proposea newmethodfor documents clustering, optimization Suffix Tree Clustering (STC) with WordNet and Named Entity Recognition (NER). In this method there are several step, step one is prepocessing documents with named entity extraction and synonym detection based on WordNet. Step two is term weighting with tfidf and nerfidf. For the last step is document clustering using Suffix Tree Clustering. Based on testingwe obtained 79.83% for precision, 77.25% for recall, and78.30% for F-measureKeywords: Documents Clustering, Named Entity Recognition, Suffix Tree Clustering, WordNet

Keywords

Collaboration network of the authors

40 authors · 91 co-authorship links · 14 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Brawijaya
  • Institut Teknologi Sepuluh Nopember
  • Universitas Muhammadiyah Malang
  • Universitas Muhammadiyah Gresik
  • State University of Semarang
  • Politeknik Negeri Malang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Aditya RachmadiUniversity of Brawijaya5
Satrio Agung WicaksonoUniversity of Brawijaya5
Muhsin MuhsinAffiliation unknown4
Diana PurwitasariInstitut Teknologi Sepuluh Nopember4
Faizatul AmaliaUniversity of Brawijaya4
Yusi Tyroni MursityoUniversity of Brawijaya3
Mochamad Chandra SaputraUniversity of Brawijaya3
Yufis AzharUniversitas Muhammadiyah Malang3
Farid SukmanaUniversitas Muhammadiyah Gresik2
Niken SubektiState University of Semarang2
Heru SetyantoAffiliation unknown2
Sonika MaulanaAffiliation unknown2
Rifa’atunnisa Rifa’atunnisaAffiliation unknown2
Juhadi JuhadiAffiliation unknown2
Hendi PratamaAffiliation unknown2
Dwi Anggani Linggar BharatiAffiliation unknown2
Abdurrahman FaridiAffiliation unknown2
Joko WidodoAffiliation unknown2
Heri YantoAffiliation unknown2
Rusdarti RusdartiAffiliation unknown2
Cindy Felita Nur AlimahUniversity of Brawijaya2
Chastine FatichahInstitut Teknologi Sepuluh Nopember1
Wahyu Nur HidayatPoliteknik Negeri Malang1
Raden Arief SetyawanUniversity of Brawijaya1
Issa ArwaniUniversity of Brawijaya1
Kartika UtamiUniversity of Brawijaya1
Rahmana Ilmi HakimUniversity of Brawijaya1
Nurur RohmahUniversity of Brawijaya1
Ridho FadhlurrahmanUniversity of Brawijaya1
Savira FahrunisaUniversity of Brawijaya1
Azri Putri RahmatikaUniversity of Brawijaya1
Dinar Indah Dwi UtamiUniversity of Brawijaya1
Safira Widya HapsariUniversity of Brawijaya1
Muhammad Rosyid MubarokUniversity of Brawijaya1
Anisah IkhsawiyanthiUniversity of Brawijaya1
Dian Eka RatnawatiUniversity of Brawijaya1

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://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Oct 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id">Optimasi Suffix Tree Clustering dengan Wordnet dan Named Entity Recognition untuk Pengelompokan Dokumen</dc:title>
	<dc:creator xml:lang="en">Wijoyo, Satrio Hadi</dc:creator>
	<dc:creator xml:lang="en">Herlambang, Admaja Dwi</dc:creator>
	<dc:creator xml:lang="en">Rozi, Fahrur</dc:creator>
	<dc:creator xml:lang="en">Isanta, Septiyan Andika</dc:creator>
	<dc:subject xml:lang="en">Clustering dokumen</dc:subject>
	<dc:subject xml:lang="en">Named Entity Recognition</dc:subject>
	<dc:subject xml:lang="en">Suffix Tree Clustering</dc:subject>
	<dc:subject xml:lang="en">WordNet</dc:subject>
	<dc:subject xml:lang="id">Clustering dokumen</dc:subject>
	<dc:subject xml:lang="id">Named Entity Recognition</dc:subject>
	<dc:subject xml:lang="id">Suffix Tree Clustering</dc:subject>
	<dc:subject xml:lang="id">WordNet</dc:subject>
	<dc:description xml:lang="id">AbstrakSemakin meningkatnya jumlah dokumen teks di dunia digital mempengaruhi banyaknya jumlah informasi  dan menyebabkan kesulitan dalam proses temu kembali informasi (information retreival). Clustering dokumen merupakan suatu bidang text mining yang penting dan dapat digunakan untuk mengefisienkan dalam pengelolaan teks serta peringkasan teks. Namun beberapa permasalahan muncul dalam clustering dokumen teks terutama dalam dokumen berita seperti ambiguitas dalam content, overlapping cluster, dan struktur unik yang terdapat dalam dokumen berita. Penelitian ini mengusulkan metode baru yaitu optimasi Suffix Tree Clustering (STC) dengan WordNet dan Named Entity Recognition (NER) untuk pengelompokan dokumen. Metode ini memiliki beberapa tahap, yaitu prepocessing dokumen dengan mengekstraksi named entity serta melakukan deteksi sinonim berdasarkan WordNet. Tahap kedua adalah pembobotan term dengan tfidf dan nerfidf. Tahap ketiga adalah melakukan clustering dokumen dengan menggunakan Suffix Tree Clustering. Berdasarkan pengujian didapatkan rata-rata nilai precision sebesar 79.83%, recall 77.25%, dan f-measure78.30 %.Kata kunci: Clustering dokumen, Named Entity Recognition, Suffix Tree Clustering, WordNetAbstractThe increasingnumber oftext documentsin the internet, influence on the number of information and lead to difficulties in the process of information retrieval. Documents clustering is main field of text mining and can be used to stream line the management of text and summarization of text. However, some problems a risein documents clustering, especially in news documents such as ambiguity in the content, overlapping clusters, and theuniquestructure ofthe news thatcontained inthe document. Inthisresearch, we proposea newmethodfor documents clustering, optimization Suffix Tree Clustering (STC) with WordNet and Named Entity Recognition (NER). In this method there are several step, step one is prepocessing documents with named entity extraction and synonym detection based on WordNet. Step two is term weighting with tfidf and nerfidf. For the last step is document clustering using Suffix Tree Clustering. Based on testingwe obtained 79.83% for precision, 77.25% for recall, and78.30% for F-measureKeywords: Documents Clustering, Named Entity Recognition, Suffix Tree Clustering, WordNet</dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2017-12-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://jtiik.ub.ac.id/index.php/jtiik/article/view/400</dc:identifier>
	<dc:identifier>10.25126/jtiik.201744400</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 4 No 4: Desember 2017; 263-267</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 4 No. 4: Desember 2017; 263-267</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.201744</dc:source>
	<dc:language>en</dc:language>
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	<dc:rights xml:lang="en">Copyright (c) 2017 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
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