SISTEM MULTIAGEN UNTUK PENGKLASTERAN PENDAKI MENGGUNAKAN K-MEANS

The beginner climbers should do mountain climbing as a group, but the current classification system is not capable of automatically clustering hikers. Therefore, an application program that is able to automatically clustering climbers is needed, especially for the solo climber who does not have the...

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Main Authors: , Maya Cendana, , Dr. Azhari S.N., M.T
Format: Thesis
Published: [Yogyakarta] : Universitas Gadjah Mada 2014
Subjects:
ETD
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author , Maya Cendana
, Dr. Azhari S.N., M.T
author_facet , Maya Cendana
, Dr. Azhari S.N., M.T
author_sort , Maya Cendana
collection UGM
description The beginner climbers should do mountain climbing as a group, but the current classification system is not capable of automatically clustering hikers. Therefore, an application program that is able to automatically clustering climbers is needed, especially for the solo climber who does not have the climbing community. Clustering will be done with K-means clustering algorithm based on intelligent agents. The agents involved are the user agent, the database agent, clustering agent, and validation agent. The agents will collaborate to determine the best cluster for the climbers to the K-means clustering can be done once / multithread. Clustering process will go through two phases: the auction and validation phase. The agents will be built on top of the JADE platform with communication language FIPA ACL. Evaluation of the 10, 100 and 200 climbers with the data to a particular cluster number to calculate the value of cohesion / density in a cluster and inter-cluster separation distances. Metric measurement used is WGAD and BGAD.
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spelling oai:generic.eprints.org:1307152016-03-04T08:02:03Z https://repository.ugm.ac.id/130715/ SISTEM MULTIAGEN UNTUK PENGKLASTERAN PENDAKI MENGGUNAKAN K-MEANS , Maya Cendana , Dr. Azhari S.N., M.T ETD The beginner climbers should do mountain climbing as a group, but the current classification system is not capable of automatically clustering hikers. Therefore, an application program that is able to automatically clustering climbers is needed, especially for the solo climber who does not have the climbing community. Clustering will be done with K-means clustering algorithm based on intelligent agents. The agents involved are the user agent, the database agent, clustering agent, and validation agent. The agents will collaborate to determine the best cluster for the climbers to the K-means clustering can be done once / multithread. Clustering process will go through two phases: the auction and validation phase. The agents will be built on top of the JADE platform with communication language FIPA ACL. Evaluation of the 10, 100 and 200 climbers with the data to a particular cluster number to calculate the value of cohesion / density in a cluster and inter-cluster separation distances. Metric measurement used is WGAD and BGAD. [Yogyakarta] : Universitas Gadjah Mada 2014 Thesis NonPeerReviewed , Maya Cendana and , Dr. Azhari S.N., M.T (2014) SISTEM MULTIAGEN UNTUK PENGKLASTERAN PENDAKI MENGGUNAKAN K-MEANS. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=71142
spellingShingle ETD
, Maya Cendana
, Dr. Azhari S.N., M.T
SISTEM MULTIAGEN UNTUK PENGKLASTERAN PENDAKI MENGGUNAKAN K-MEANS
title SISTEM MULTIAGEN UNTUK PENGKLASTERAN PENDAKI MENGGUNAKAN K-MEANS
title_full SISTEM MULTIAGEN UNTUK PENGKLASTERAN PENDAKI MENGGUNAKAN K-MEANS
title_fullStr SISTEM MULTIAGEN UNTUK PENGKLASTERAN PENDAKI MENGGUNAKAN K-MEANS
title_full_unstemmed SISTEM MULTIAGEN UNTUK PENGKLASTERAN PENDAKI MENGGUNAKAN K-MEANS
title_short SISTEM MULTIAGEN UNTUK PENGKLASTERAN PENDAKI MENGGUNAKAN K-MEANS
title_sort sistem multiagen untuk pengklasteran pendaki menggunakan k means
topic ETD
work_keys_str_mv AT mayacendana sistemmultiagenuntukpengklasteranpendakimenggunakankmeans
AT drazharisnmt sistemmultiagenuntukpengklasteranpendakimenggunakankmeans