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Jurnal SINTA 2

Pengembangan Metode Klasifikasi Berdasarkan K-Means Dan LVQ

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 1 No. 1 (2014) · pp. 1–4

DOI
10.25126/jtiik.20141197
ISSN
2528-6579, 2355-7699
Published
2014-07-12
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
en

Abstract

AbstrakPada penelitian ini dikembangkan metode klasifikasi berdasarkan pengelompokan K-Means dan LVQ. Metode-metode klasifikasi yang telah ada jika ada data dengan frekuensi kecil cenderung tidak digunakan dalam pengujian kelas, padahal dimungkinkan data tersebut sangat bermanfaat. Langkah untuk melakukan pengelompokan adalah: melakukan pengelompokkan dengan K-Means. Pengelompokan terus dilakukan sampai mencapai threshold (batasan tertentu). Jika threshold sudah dicapai dan pada satu cluster masih terdapat kelas yang berbeda maka dilakukan pembelajaran dengan menggunakan LVQ. Akurasi gabungan K-Means dan LVQ lebih baik daripada dengan K-Means murni. Untuk akurasi rata-rata tertinggi K-Means dan LVQ didapatkan 92%, sedang untuk K-Means murni 82%.Kata kunci: klasifikasi, pengelompokan, K-Means, LVQAbstractThis research will develop methods of classification based on K-Means clustering. Grouping method used is a combination of K-Means and LVQ. Classification methods that have been there if there is a small frequency data tend to be used in the test class, but it is possible they are very useful. Steps to perform grouping is doing the K-Means clustering. Grouping is continues until it reaches the threshold. If the threshold has been reached and there are cluster of different classes then performed using LVQ learning. Accuracy combined K-Means and LVQ is better than with pure K-Means. For the highest average accuracy of K-Means and LVQ gained 92%, while for the K-Means only 82%.Keywords: classification, grouping, K-Means, LVQ

Collaboration network of the authors

40 authors · 87 co-authorship links

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

  • University of Brawijaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Indriati .Affiliation unknown2
Nanang Yudi SetiawanUniversity of Brawijaya2
Galang Gilang RamadhanUniversity of Brawijaya2
Imam CholissodinUniversity of Brawijaya1
Daneswara JauhariUniversity of Brawijaya1
Muhammad SyafiqUniversity of Brawijaya1
Achmad Jafar Al KadafiUniversity of Brawijaya1
Rizka Husnun ZakiyyahUniversity of Brawijaya1
Wanda Athira LuqyanaUniversity of Brawijaya1
Putri Nur FadilaUniversity of Brawijaya1
Zikfikri Yulfiandi RachmadUniversity of Brawijaya1
Achmad ArwanUniversity of Brawijaya1
Mahendra DataUniversity of Brawijaya1
Ibnu AqliUniversity of Brawijaya1
Suherni PrahestiUniversity of Brawijaya1
Heru NurwasitoUniversity of Brawijaya1
Sutrisno SutrisnoUniversity of Brawijaya1
Rozaq AkbarUniversity of Brawijaya1
Alland Rifqy PutraAffiliation unknown1
Alland Rifqy PutraUniversity of Brawijaya1
Muhammad Fariz FirdausUniversity of Brawijaya1
Dinar Fairus SalsabillahUniversity of Brawijaya1
Bayu RahayudiUniversity of Brawijaya1
Tsania DzulkarnainUniversity of Brawijaya1
Novanto YudistiraUniversity of Brawijaya1
Asmuni HarisAffiliation unknown1
Mahrus SholehAffiliation unknown1
Asmuni HarisUniversity of Brawijaya1
Mahrus SholehUniversity of Brawijaya1
Budi Darma SetiawanUniversity of Brawijaya1
Galih Restu BaihaqiAffiliation unknown1
Galih Restu BaihaqiUniversity of Brawijaya1
Satrio Hadi WijoyoUniversity of Brawijaya1
Eka Bayu SatriawanAffiliation unknown1
Eka Bayu SatriawanAffiliation unknown1
Raihan WinurputraAffiliation unknown1
Raihan WinurputraUniversity 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 Okt 2026, last seen 27 Okt 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="id">Pengembangan Metode Klasifikasi Berdasarkan K-Means Dan LVQ</dc:title>
	<dc:creator xml:lang="en">Eka Ratnawati, Dian</dc:creator>
	<dc:creator xml:lang="en">., Marji</dc:creator>
	<dc:creator xml:lang="en">Muflikhah, Lailil</dc:creator>
	<dc:description xml:lang="id">AbstrakPada penelitian ini dikembangkan metode klasifikasi berdasarkan pengelompokan K-Means dan LVQ. Metode-metode klasifikasi yang telah ada jika ada data dengan frekuensi kecil cenderung tidak digunakan dalam pengujian kelas, padahal dimungkinkan data tersebut sangat bermanfaat. Langkah untuk melakukan pengelompokan adalah: melakukan pengelompokkan dengan K-Means. Pengelompokan terus dilakukan sampai mencapai threshold (batasan tertentu). Jika threshold sudah dicapai dan pada satu cluster masih terdapat kelas yang berbeda maka dilakukan pembelajaran dengan menggunakan LVQ. Akurasi gabungan K-Means dan LVQ lebih baik daripada dengan K-Means murni. Untuk  akurasi rata-rata tertinggi K-Means dan LVQ didapatkan 92%, sedang untuk K-Means murni 82%.Kata kunci: klasifikasi, pengelompokan, K-Means, LVQAbstractThis research will develop methods of classification based on K-Means clustering. Grouping method used is a combination of K-Means and LVQ. Classification methods that have been there if there is a small frequency data tend to be used in the test class, but it is possible they are very useful.  Steps to perform grouping is doing the K-Means clustering. Grouping is continues until it reaches the threshold. If the threshold has been reached and there are cluster of different classes then performed using LVQ learning. Accuracy combined K-Means and LVQ is better than with pure K-Means. For the highest average accuracy of K-Means and LVQ gained 92%, while for the K-Means only 82%.Keywords: classification, grouping, K-Means, LVQ</dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2014-07-12</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jtiik.ub.ac.id/index.php/jtiik/article/view/97</dc:identifier>
	<dc:identifier>10.25126/jtiik.20141197</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 1 No 1: April 2014; 1-4</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 1 No. 1: April 2014; 1-4</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.201411</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://jtiik.ub.ac.id/index.php/jtiik/article/view/97/pdf</dc:relation>
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