OPTIMASI K-MEANS CLUSTERING UNTUK IDENTIFIKASI DAERAH ENDEMIK PENYAKIT MENULAR DENGAN ALGORITMA PARTICLE SWARM OPTIMIZATION DI KOTA SEMARANG


Suhardi Rustam(1*); Heru Agus Santoso(2); Catur Supriyanto(3);

(1) Universitas Ichsan Gorontalo
(2) Universitas Dian Nuswantoro
(3) Universitas Dian Nuswantoro
(*) Corresponding Author

  

Abstract


Tropical regions is a region endemic to various infectious diseases. At the same time an area of high potential for the presence of infectious diseases. Infectious diseases still a major public health problem in Indonesia. Identification of endemic areas of infectious diseases is an important issue in the field of health, the average level of patients with physical disabilities and death are sourced from infectious diseases. Data Mining in its development into one of the main trends in the processing of the data. Data Mining could effectively identify the endemic regions of hubunngan between variables. K-means algorithm klustering used to classify the endemic areas so that the identification of endemic infectious diseases can be achieved with the level of validation that the maximum in the clustering. The use of optimization to identify the endemic areas of infectious diseases combines k-means clustering algorithm with optimization particle swarm optimization ( PSO ). the results of the experiment are endemic to the k-means algorithm with iteration =10, the K-Fold =2 has Index davies bauldin = 0.169 and k-means algorithm with PSO, iteration = 10, the K-Fold = 5, index davies bouldin = 0.113. k-fold = 5 has better performance.


Keywords


Endemic infectious Disease; Data Mining; Clustering; K-Means; Particle Swarm Optimization

  
  

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Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v10i3.342.251-259
  

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References


Achmadi UF,2009. Manajemen Penyakit berbasis wilayah. Jurnal Kesehatan Masyarakat Nasional Vol. 3,No.4

Van der Merwe DW, Engelbrecht AP. 2003. Data Clustering using Practicle Swarm Optimization, University of Pretoria, IEEE 0-7803-7804-0/03, hlm 215-220.

Fan Y. Do “smart” places have less urban health penalty?.2009. 47th International Making Cities Livable Conference; Portland, Oregon

Han Jiawei, 2006. Kamber M.Data mining: Concepts and techniques.2nd ed.Beijing: China Machine Press

Hasibuan, A Zaenal .2007. Metodelogi Penelitian Pada Bidang Ilmu Komputer dan Teknologi Informasi. 20 Juli 2018

Agusta, Y.2007, K-means-Penerapan, Permasalahan dan Metode Terkait. Jurnal Sistem dan Informatika Vol.3 (Februari):47-60

Utami Nanik. Dkk,. Aplikasi Metode Particle Swarm Optimization(PSO) dalam Clustering. ITS Surabaya


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