Aplikasi algoritma metaheuristik basis fuzzy K- modes untuk supplier clustering = Application of metaheuristic based fuzzy K-modes algorithm to supplier clustering / Yuliana Portti
Yuliana Portti;
Amalia Suzianti, supervisor; Arian Dhini, supervisor; Erlinda Muslim, examiner; Maya Arlini Puspasari, examiner
([Publisher not identified]
, 2015)
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[ABSTRAK Penelitian ini mengusulkan tiga algoritma meta-heuristik berbasis Fuzzy K-modesuntuk clustering binary data set. Ada tiga metode metaheuristik diterapkan, yaituParticle Swarm Optimization (PSO), Genetika Algoritma (GA), dan Artificial BeeColony (ABC). Ketiga algoritma digabungkan dengan algoritma K-modes.Tujuannya adalah untuk memberikan modes awal yang lebih baik untuk K-modes.Jarak antara data ke modes dihitung dengan menggunakan koefisien Jaccard.Koefisien Jaccard diterapkan karena dataset mengandung banyak nilai nol . Dalamrangka untuk melakukan pengelompokan set data real tentang supplier otomotif diTaiwan, algoritma yang diusulkan diverifikasi menggunakan benchmark set data.Hasil penelitian menunjukkan bahwa PSO K-modes dan GA K-modes lebih baikdari ABC K-modes. Selain itu, dari hasil studi kasus, GA K-modes memberikanSSE terkecil dan juga memiliki waktu komputasi lebih cepat dari PSO K-modesdan ABC K-modes. ABSTRACT This study proposed three meta-heuristic based fuzzy K-modes algorithms forclustering binary dataset. There are three meta-heuristic methods applied, namelyParticle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA) algorithm,and Artificial Bee Colony (ABC) algorithm. These three algorithms are combinedwith k-modes algorithm. Their aim is to give better initial modes for the k-modes.Herein, the similarity between two instances is calculated using jaccard coefficient.The Jaccard coefficient is applied since the dataset contains many zero values. Inorder to cluster a real data set about automobile suppliers in Taiwan, the proposedalgorithms are verified using benchmark data set. The experiments results showthat PSO K-modes and GA K-modes is better than ABC K-modes. Moreover,from case study results, GA fuzzy K-modes gives the smallest SSE and also hasfaster computational time than PSO fuzzy K-modes and ABC fuzzy K-modes., This study proposed three meta-heuristic based fuzzy K-modes algorithms forclustering binary dataset. There are three meta-heuristic methods applied, namelyParticle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA) algorithm,and Artificial Bee Colony (ABC) algorithm. These three algorithms are combinedwith k-modes algorithm. Their aim is to give better initial modes for the k-modes.Herein, the similarity between two instances is calculated using jaccard coefficient.The Jaccard coefficient is applied since the dataset contains many zero values. Inorder to cluster a real data set about automobile suppliers in Taiwan, the proposedalgorithms are verified using benchmark data set. The experiments results showthat PSO K-modes and GA K-modes is better than ABC K-modes. Moreover,from case study results, GA fuzzy K-modes gives the smallest SSE and also hasfaster computational time than PSO fuzzy K-modes and ABC fuzzy K-modes.] |
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No. Panggil : | T44406 |
Entri utama-Nama orang : | |
Entri tambahan-Nama orang : | |
Entri tambahan-Nama badan : | |
Subjek : | |
Penerbitan : | [Place of publication not identified]: [Publisher not identified], 2015 |
Program Studi : |
Bahasa : | ind |
Sumber Pengatalogan : | LibUI ind rda |
Tipe Konten : | text |
Tipe Media : | unmediated ; computer |
Tipe Carrier : | volume ; online resource |
Deskripsi Fisik : | xv, 93 pages : illustration ; 28 cm + appendix |
Naskah Ringkas : | |
Lembaga Pemilik : | Universitas Indonesia |
Lokasi : | Perpustakaan UI, Lantai 3 |
No. Panggil | No. Barkod | Ketersediaan |
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T44406 | TERSEDIA |
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Tidak ada ulasan pada koleksi ini: 20414356 |