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ITS » Undergraduate Theses » Teknik Informatika
Posted by tondoindra@gmail.com at 29/03/2016 10:59:35  •  742 Views


IMPLEMENTASI DYNAMIC CLUSTER MENGGUNAKAN K-MEANS CLUSTER DAN PARTICLE SWARM OPTIMIZATION UNTUK IDENTIFIKASI KARAKTERISTIK LALU LINTAS BERDASARKAN JUMLAH DAN JENIS KENDARAAN

IMPLEMENTATION OF DYNAMIC CLUSTER USING K-MEANS CLUSTER AND PARTICLE SWARM OPTIMIZATION FOR TRAFFIC CHARACTERISTICS IDENTIFICATION BASED ON NUMBER AND KIND OF VEHICLES

Author :
MUKHTAR, TSABBIT AQDAMI ( 5110100227 )




ABSTRAK

Jalan raya merupakan fasilitas umum yang paling sering digunakan oleh masyarakat dalam kehidupan sehari-hari. Hal tersebut disebabkan karena jalan raya berfungsi untuk menghubungkan antar satu tempat dengan tempat lainnya. Setiap jalan raya memiliki lebar dan pembagian jalur yang berbedabeda disesuaikan dengan jumlah pengguna yang melintas. Meski terdapat algoritma pengelompokkan K-Means Cluster namun K-means Cluster saja tidak cukup untuk melakukan pengelompokkan jalan raya karena K-means membutuhkan jumlah cluster yang pasti. maka dari itu dikembangkan suatu metode Dynamic Cluster dengan menggunakan K-Means dan Particle Swarm Optimization PSO untuk mencari jumlah Cluster optimal. Algoritma PSO digunakan untuk mencari Jumlah Cluster yang optimal sementara K-Means digunakan untuk menghitung hasil Cluster terbaik. Uji coba dilakukan dengan menggunakan uji parameter dan uji skenario berdasarkan jenis kendaraan. Uji parameter dimaksudkan untuk mencari nilai optimal ambang batas dan konstanta dari dari algoritma PSO. Uji skenario berdasarkan jenis kendaraan dibagi menjadi dua yakni motor dan mobil di dalam rentang waktu tertentu. Hasil dariuji coba yang dilakukan adalah setiap Cluster yang terbentuk di dalam subskenario memiliki anggota yang selalu berubah-ubah berdasarkan kedekatan selisi jumlah kendaraan.


ABSTRACT

The Roadways are the most of public facilities is used by people in their activities. It is because the roadways function that connect one place to another places. Every roadway has its own capacity and the differences of compartmentation based on its vehicles. Although K-Means Cluster can be used for grouping method but it doesnt enough for this problem because it needs a spesific number of Clusters. This final pfoject implements new method in Dynamic Cluster using K-Means an d Particle Swarm Optimization for optimal solution in Cluster problem. Particle Swarm Optimizations algorithm is used to find the number of Clusters with which the K-Means is used to find the best Cluster result. Experimental evaluation is done by testing the parameter values in Particle Swarm Optimization and some scenarios problem about the condition of roadways. Experimental is divided based on the two kind of vehicles motorcyle and car in some ranges of the time. The result of Experimental that have been done is in every Cluster in the subscenario has the different member of roadways that depends of the time.



KeywordsK-Means Cluster, Dynamic Cluster, Particle Swarm Optimization.
 
Subject:  Analisis kluster
Contributor
  1. Prof.Dr.Ir.Joko Lianto Buliali, M.Sc.
  2. Diana Purwitasari, S.Kom, M.Sc.
Date Create: 29/03/2016
Type: Text
Format: PDF
Language: Indonesian
Identifier: ITS-Undergraduate-51001150008466
Collection ID: 51001150008466
Call Number: RSIf 005.74 Muk i


Source
Undergraduate Theses Of Informatics Engineering RSIf 005.74 Muk i, 2016

Coverage
ITS Community

Rights
Copyright @2016 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




[ Download - Open Access ]

  1.  ITS-Undergraduate-40965-5110100227-abstract_id.pdf - 88 KB
  2.  ITS-Undergraduate-40965-5110100227-abstract_en.pdf - 88 KB
  3.  ITS-Undergraduate-40965-5110100227-conclusion.pdf - 83 KB




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