scholarly journals ANALISA PENGGUNAAN NILAI MATA KULIAH UNTUK CLUSTER MAHASISWA PERGURUAN TINGGI DENGAN MENGGUNAKAN EM CLUSTERING

2013 ◽  
Vol 1 (1) ◽  
pp. 9
Author(s):  
Andy Prasetyo Utomo
Keyword(s):  
Em Clustering

ABSTRAK Nilai mahasiswa dapat digunakan sebagai acuan dalam menentukan atau menyarankan mata kuliah yang diambil sesuai dengan kemampuan atau potensi mahasiswa dengan melakukan clustering. Mahasiswa dengan nilai baik dapat diarahkan untuk mengambil tipe pendidikan intensif dan sebaliknya. Selain nilai mahasiswa, mata kuliah juga mengalami clustering kedalam kelompok kelompok jenis keilmuan, sehingga diharapkan mahasiswa mendapatkan kelompok keilmuan yang tepat sesuai dengan kemampuannya masing masing. Dengan itu perguruan tinggi dapat menciptakan mahasiswa yang berkualitas baik. Dalam makalah ini, teknik clustering yang digunakan adalah EM algoritihm dan menghasilkan 6 langkah pembuatan cluster. Kata kunci : WEKA, clustering, EM algoritihm, nilai mahasiswa, kelompok keahlian, prediksi mata kuliah..

scholarly journals Comparison of Clustering Algorithms on Air Quality Substances in Peninsular Malaysia

2018 ◽  
Vol 2 (1) ◽  
pp. 36-44
Author(s):  
Sitti Sufiah Atirah Rosly ◽  
Balkiah Moktar ◽  
Muhamad Hasbullah Mohd Razali
Keyword(s):  
Cluster Analysis ◽  
Air Quality ◽  
Expectation Maximization ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Peninsular Malaysia ◽  
Monthly Data ◽  
Knowledge Analysis ◽  
Em Clustering ◽  
Monitoring Stations

Air quality is one of the most popular environmental problems in this globalization era. Air pollution is the poisonous air that comes from car emissions, smog, open burning, chemicals from factories and other particles and gases. This harmful air can give adverse effects to human health and the environment. In order to provide information which areas are better for the residents in Malaysia, cluster analysis is used to determine the areas that can be clustering together based on their a ir quality through several air quality substances. Monthly data from 37 monitoring stations in Peninsular Malaysia from the year 2013 to 2015 were used in this study. K - Means (KM) clustering algorithm, Expectation Maximization (EM) clustering algorithm and Density Based (DB) clustering algorithm have been chosen as the techniques to analyze the cluster analysis by utilizing the Waikato Environment for Knowledge Analysis (WEKA) tools. Results show that K - means clustering algorithm is the best method among ot her algorithms due to its simplicity and time taken to build the model. The output of K - means clustering algorithm shows that it can cluster the area into two clusters, namely as cluster 0 and cluster 1. Clusters 0 consist of 16 monitoring stations and clu ster 1 consists of 36 monitoring stations in Peninsular Malaysia.

scholarly journals SSH Traffic Identification Using EM Clustering

2012 ◽  
Vol 37B (12) ◽  
pp. 1160-1167 ◽  
Author(s):  
Kyoung-Lyoon Kim ◽  
Myung-Sup Kim ◽  
Hyoung-Joong Kim
Keyword(s):  
Traffic Identification ◽  
Em Clustering

EM Clustering

2011 ◽  
pp. 311-311
Author(s):  
John Langford ◽  
Xinhua Zhang ◽  
Gavin Brown ◽  
Indrajit Bhattacharya ◽  
Lise Getoor ◽  
...  
Keyword(s):  
Em Clustering
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