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Open access Jurnal SINTA 2

Optimasi Cluster Pada Fuzzy C-Means Menggunakan Algoritma Genetika Untuk Menentukan Nilai Akhir

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 6 No. 1 (2013)

DOI
10.22146/ijccs.2145
ISSN
2460-7258, 1978-1520
Published
2013-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

AbstrakNilai akhir mahasiswa dapat ditentukan dengan berbagai cara, beberapa diantaranya menggunakan range nilai, standart deviasi, dll. Dalam penelitian ini akan ditawarkan sebuah metode baru untuk menentukan nilai akhir mahasiswa menggunakan clustering dalam hal ini adalah Fuzzy C-Means.Fuzzy C-Means digunakan untuk mengelompokkan sejumlah data dalam beberapa cluster. Tiap data memiliki derajat keanggotaan pada masing-masing cluster antara 0-1 yang diukur melalui fungsi objektif. Pada Fuzzy C-Means ini fungsi objektif diminimumkan menggunakan iterasi yang biasanya terjebak dalam optimum lokal. Algoritma genetika diharapkan dapat menangani masalah tersebut karena algoritma genetika berbasis evolusi yaitu dapat mencari individu terbaik melalui operasi genetika (seleksi, crossover, mutasi) dan dievaluasi berdasarkan nilai fitness. Penelitian ini bertujuan untuk mengoptimasi titik pusat cluster pada Fuzzy C-Means menggunakan algoritma genetika. Hasilnya, bahwa dengan menggunakan GFS didapatkan fungsi objektif yang lebih kecil daripada menggunakan FCM, walaupun membutuhkan waktu yang relative besar. Meskipun selisih antara FCM dan GFS tidak terlalu besar namun hal tersebut berpengaruh pada anggota cluster Kata kunci— clustering, Fuzzy C-Means, algoritma genetika AbstractThe final grade of students could be determined in various ways, some of which use a range of values, deviation standard, etc. In this study will be offered a new method for determining final grades of students by using the clustering method. In this research the clustering method that will be used is the Fuzzy C-Means (FCM).Fuzzy C-Means is used to group a number of data in multiple clusters. Each data has a degree of membership (the range value of membership degree is 0-1). Membership degree is measured through the objective function. In Fuzzy C-Means, objective function is minimized by using iteration and is usually trapped in a local optimum. Genetic algorithm is expected to handle these problems. The operation of genetic algorithm based on evolution that is able to find the best individuals through genetic operations (selection, crossover and mutation) and evaluated based on fitness values.This research aims to optimize the cluster center point of FCM by using genetic algorithms. The result of this research shows that by combining the Genetic Algorithm with FCM could obtained a smaller objective function than using FCM, although it takes longer in execution time. Although the difference of objective function that produced by FCM and FCM-Genetic Algorithm combination is not too big each other, but it takes effect on the cluster members. Keywords— clustering, fuzzy c-means, genetic algorithm

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
Rina PramitasariUniversitas Gadjah Mada1
Albert Dian SanoAffiliation unknown1
Abdul WahidInstitut Teknologi dan Sains Mandala1
Nur HasanahAffiliation unknown1
Muhamad Munawar YusroJenderal Soedirman University1
Muhammad FakhrurrifqiAffiliation unknown1
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">Optimasi  Cluster Pada Fuzzy C-Means Menggunakan Algoritma Genetika Untuk Menentukan Nilai Akhir</dc:title>
	<dc:creator>Mas`udia, Putri Elfa</dc:creator>
	<dc:creator>Wardoyo, Retantyo</dc:creator>
	<dc:description xml:lang="en-US">AbstrakNilai akhir mahasiswa dapat ditentukan dengan berbagai cara, beberapa diantaranya menggunakan range nilai, standart deviasi, dll. Dalam penelitian ini akan ditawarkan sebuah metode baru untuk menentukan nilai akhir mahasiswa menggunakan clustering dalam hal ini adalah Fuzzy C-Means.Fuzzy C-Means digunakan untuk mengelompokkan sejumlah data dalam beberapa cluster. Tiap data memiliki derajat keanggotaan pada masing-masing cluster antara 0-1 yang diukur melalui fungsi objektif. Pada Fuzzy C-Means ini fungsi objektif diminimumkan menggunakan iterasi yang biasanya terjebak dalam optimum lokal. Algoritma genetika diharapkan dapat menangani masalah tersebut karena algoritma genetika berbasis evolusi yaitu dapat mencari individu terbaik melalui operasi genetika (seleksi, crossover, mutasi) dan dievaluasi berdasarkan nilai fitness. Penelitian ini bertujuan untuk mengoptimasi titik pusat cluster pada Fuzzy C-Means menggunakan algoritma genetika. Hasilnya, bahwa dengan menggunakan GFS didapatkan fungsi objektif yang lebih kecil daripada menggunakan FCM, walaupun membutuhkan waktu yang relative besar. Meskipun selisih antara FCM dan GFS tidak terlalu besar namun hal tersebut berpengaruh pada anggota cluster  Kata kunci— clustering, Fuzzy C-Means, algoritma genetika AbstractThe final grade of students could be determined in various ways, some of which use a range of values, deviation standard, etc. In this study will be offered a new method for determining final grades of students by using the clustering method. In this research the clustering method that will be used is the Fuzzy C-Means (FCM).Fuzzy C-Means is used to group a number of data in multiple clusters. Each data has a degree of membership (the range value of membership degree is 0-1). Membership degree is measured through the objective function. In Fuzzy C-Means,  objective function is minimized by using iteration and is usually trapped in a local optimum. Genetic algorithm is expected to handle these problems. The operation of genetic algorithm based on evolution that is able to find the best individuals through genetic operations (selection, crossover and mutation) and evaluated based on fitness values.This research aims to optimize the cluster center point of FCM by using genetic algorithms. The result of this research shows that by combining the Genetic Algorithm with FCM could obtained a smaller objective function than using FCM, although it takes longer in execution time. Although the difference of objective function that produced by FCM and FCM-Genetic Algorithm combination is not too big each other, but it takes effect on the cluster members. Keywords— clustering, fuzzy c-means, genetic algorithm</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>2013-01-31</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/2145</dc:identifier>
	<dc:identifier>10.22146/ijccs.2145</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 6, No 1 (2012): January</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/2145/1926</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2013 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
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