Optimasi Cluster Pada Fuzzy C-Means Menggunakan Algoritma Genetika Untuk Menentukan Nilai Akhir
| Main Authors: | Mas`udia, Putri Elfa, Wardoyo, Retantyo |
|---|---|
| Other Authors: | indoceiss |
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Indonesian Computer, Electronics, and Instrumentation Support Society
, 2013
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| Online Access: |
https://journal.ugm.ac.id/ijccs/article/view/2145 https://journal.ugm.ac.id/ijccs/article/view/2145/1926 |
| ctrlnum |
article-2145 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Optimasi Cluster Pada Fuzzy C-Means Menggunakan Algoritma Genetika Untuk Menentukan Nilai Akhir</title><creator>Mas`udia, Putri Elfa</creator><creator>Wardoyo, Retantyo</creator><description 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</description><publisher lang="en-US">Indonesian Computer, Electronics, and Instrumentation Support Society</publisher><contributor lang="en-US">indoceiss</contributor><date>2013-01-31</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Other:</type><type>File:application/pdf</type><identifier>https://journal.ugm.ac.id/ijccs/article/view/2145</identifier><identifier>10.22146/ijccs.2145</identifier><source lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 6, No 1 (2012): IJCCS</source><source>2460-7258</source><source>1978-1520</source><language>eng</language><relation>https://journal.ugm.ac.id/ijccs/article/view/2145/1926</relation><rights lang="en-US">Copyright (c) 2013 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</rights><rights lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</rights><recordID>article-2145</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other Other: File:application/pdf File Journal:eJournal |
| author |
Mas`udia, Putri Elfa Wardoyo, Retantyo |
| author2 |
indoceiss |
| title |
Optimasi Cluster Pada Fuzzy C-Means Menggunakan Algoritma Genetika Untuk Menentukan Nilai Akhir |
| publisher |
Indonesian Computer, Electronics, and Instrumentation Support Society |
| publishDate |
2013 |
| url |
https://journal.ugm.ac.id/ijccs/article/view/2145 https://journal.ugm.ac.id/ijccs/article/view/2145/1926 |
| contents |
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 |
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IOS1094.article-2145 |
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Universitas Gadjah Mada |
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19 |
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Perpustakaan Pusat Universitas Gadjah Mada |
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488 |
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IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
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1094 |
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Program Komputer dan Teknologi Informasi |
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SLEMAN |
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DAERAH ISTIMEWA YOGYAKARTA |
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IOS1094 |
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2017-02-27T01:56:58Z |
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