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Open access Jurnal SINTA 2

Perbandingan Algoritma Nearest Neighbour, C4.5 dan LVQ untuk Klasifikasi Kemampuan Mahasiswa

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 7 No. 2 (2013) · pp. 145–154

DOI
10.22146/ijccs.3353
ISSN
2460-7258, 1978-1520
Published
2013-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

AbstrakPada pelaksanaan acara perkuliahan atau saat proses balajar mengajar, dosen sering terkendala dengan kemampuan mahasiswa pada suatu matakuliah di satu kelas yang tidak merata. Oleh karena itu dosen terlebih dulu mengetahui kemampuan setiap mahasiswanya dengan salah satu caranya adalah dengan melihat karakteristik setiap mahasiswa dan kemudian dibandingkan dengan mahasiswa-mahasiswa sebelumnya dalam menyelesaian suatu mata kuliah.Pada penelitian ini, akan dilakukan perbandingan tingkat akurasi antara tiga algoritma, yaitu: Nearest Neighbour, C45 dan LVQ, pada kasus klasifikasi kemampuan mahasiswa untuk keperluan penentuan pembagian kelas mahasiswa baru. Selain itu, juga akan diperbandingkan tingkat kecepatan setiap algoritma dalam mendapatkan kelas kasus lama yang paling mirip dengan kasus baru yang dimasukkan.Kesimpulan yang didapatkan setelah sistem dibangun dan kemudian membandingkan ketiga algoritma tersebut adalah algoritma nearest neighbour dapat menghasilkan akurasi tertinggi. Kata kunci— nearest neighbour, c45, jaringan syaraf tiruan, lvq, mahasiswa AbstractDuring the lecture or in teaching-learning process, a lecturer sometimes finds that heterogeneous classroom as the obstacles due to the differences in students’ performances. Thus, the lecturer should be aware of this phenomenon and one way to overcome this is to find out the characteristics of each student in order to compare them with the previous students while completing their study. In this research, the three algorithms are compared, they are namely; Nearest Neighbor, C45, and LVQ, this is done to classify the students’ ability and to decide the class for the new students. In addition, the speed level of each algorithm is compared by means of getting the nearest previous case study to the new class. Finally, the ideas coming up as the conclusion for this research is that after the system is developed and those three algorithms are being compared, the result shows that there is nearest neighbour algorithm can produce the highest accuracy. Keywords—nearest neighbour, c45, artificial neural network, LVQ, students’ performances.

Collaboration network of the authors

40 authors · 54 co-authorship links · 7 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.

  • Universitas Gadjah Mada
  • Udayana University
  • Institut Teknologi dan Sains Mandala
  • Jenderal Soedirman University
  • Politeknik Gorontalo
  • Universitas Maritim Raja Ali Haji
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sri T. HartatiUniversitas Gadjah Mada6
Aina MusdholifahUniversitas Gadjah Mada4
Edi WinarkoUniversitas Gadjah Mada4
Khabib MustofaUniversitas Gadjah Mada4
Edhy SutantaUniversitas Gadjah Mada4
Agus HarjokoUniversitas Gadjah Mada2
Rahmat BudiarsaUniversitas Gadjah Mada2
Bangun WijayantoUniversitas Gadjah Mada1
Decky HendarsyahUniversitas Gadjah Mada1
Awaluddin AwaluddinAffiliation unknown1
Putri Elfa Mas’udiaAffiliation unknown1
Rina PramitasariUniversitas Gadjah Mada1
Albert Dian SanoAffiliation unknown1
Abdul WahidInstitut Teknologi dan Sains Mandala1
Nur HasanahAffiliation unknown1
Muhamad Munawar YusroJenderal Soedirman University1
Peggi Sri AstutiUdayana University1
Rahman EramaPoliteknik Gorontalo1
Christian Dwi SuhendraUniversitas Gadjah Mada1
Nola RithaUniversitas Maritim Raja Ali Haji1
Ria AstriratmaUniversitas Gadjah Mada1
Made Arya BudhiAffiliation unknown1
Elizabeth Paskahlia GunawanAffiliation unknown1
Ulfi Saidata AesyiAffiliation unknown1
Anny Kartika SariUniversitas Gadjah Mada1
Ida Bagus Gede SarasvanandaUdayana University1
Indra PratisthaUniversitas Gadjah Mada1
Beta Yudha MahindartaUniversitas Gadjah Mada1
Dian MustikaningrumUniversitas Gadjah Mada1
Susilo RomadhonUniversitas Gadjah Mada1
Muhammad MukharirUniversitas Gadjah Mada1
Amelia Dita HandayaniUniversitas Gadjah Mada1
Nikolas Adhi PrasetyoUniversitas Gadjah Mada1
Susi Eva Maria PurbaUniversitas Gadjah Mada1
Kharis Suryandaru PratamaUniversitas Gadjah Mada1
Ahmad AshariUniversitas Gadjah Mada1
Bernard Renaldy SutejaAffiliation unknown1
Suryo GuritnoAffiliation unknown1

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://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en-US">Perbandingan Algoritma Nearest Neighbour, C4.5 dan LVQ untuk Klasifikasi Kemampuan Mahasiswa</dc:title>
	<dc:creator>Fakhrurrifqi, Muhammad</dc:creator>
	<dc:creator>Wardoyo, Retantyo</dc:creator>
	<dc:description xml:lang="en-US">AbstrakPada pelaksanaan acara perkuliahan atau saat proses balajar mengajar, dosen sering terkendala dengan kemampuan mahasiswa pada suatu matakuliah di satu kelas yang tidak merata. Oleh karena itu dosen terlebih dulu mengetahui kemampuan setiap mahasiswanya dengan salah satu caranya adalah dengan melihat karakteristik setiap mahasiswa dan kemudian dibandingkan dengan mahasiswa-mahasiswa sebelumnya dalam menyelesaian suatu mata kuliah.Pada penelitian ini, akan dilakukan perbandingan tingkat akurasi antara tiga algoritma, yaitu : Nearest Neighbour, C45 dan LVQ, pada kasus klasifikasi kemampuan mahasiswa untuk keperluan penentuan pembagian kelas mahasiswa baru. Selain itu, juga akan diperbandingkan tingkat kecepatan setiap algoritma dalam mendapatkan kelas kasus lama yang paling mirip dengan kasus baru yang dimasukkan.Kesimpulan yang didapatkan setelah sistem dibangun dan kemudian membandingkan ketiga algoritma tersebut adalah algoritma nearest neighbour dapat menghasilkan akurasi tertinggi. Kata kunci— nearest neighbour, c45, jaringan syaraf tiruan, lvq, mahasiswa AbstractDuring the lecture or in teaching-learning process, a lecturer sometimes finds that heterogeneous classroom as the obstacles due to the differences in students’ performances.  Thus, the lecturer should be aware of this phenomenon and one way to overcome this is to find out the characteristics of each student in order to compare them with the previous students while completing their study. In this research, the three algorithms are compared, they are namely; Nearest Neighbor, C45, and LVQ, this is done to classify the students’ ability and to decide the class for the new students. In addition, the speed level of each algorithm is compared by means of getting the nearest previous case study to the new class.    Finally, the ideas coming up as the conclusion for this research is that after the system is developed and those three algorithms are being compared, the result shows that there is nearest neighbour algorithm can produce the highest accuracy. Keywords—nearest neighbour, c45, artificial neural network, LVQ, students’ performances.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">IndoCEISS</dc:contributor>
	<dc:date>2013-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/3353</dc:identifier>
	<dc:identifier>10.22146/ijccs.3353</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 7, No 2 (2013): July; 145-154</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/3353/2912</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2013 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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