Spatial Mapping of Toddler Pneumonia Vulnerability in Bojonegoro, Indonesia, Using Hybrid Genetic Algorithm – K-means (GA-Kmeans)

 
 
 
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  • Abstract


    Pneumonia is an acute infection that affects the lung tissue (alveoli) which can be caused by various microorganisms such as viruses, fungi, and bacteria. Pneumonia was the second leading cause of death (13.2%) after diarrhea (17.2%) among under-fives. It shows that pneumonia is a disease that becomes a public health problem that contributes to the high mortality rate of children under five in Indonesia.  In recent years, Bojonegoro city gives a large contribution for toddler pneumonia under five years of age in East Java district, Indonesia.  In 2012, pneumonia sufferers reached 90.17% of the total number of the toddler and still many the next years.  There are five risk factors for pneumonia include the number of children under five, the estimated number of patients, the number of sufferers, environmental factors and nutritional status.  A spatial approach is needed to see the spreading of pneumonia vulnerability level in each sub-district in Bojonegoro.  This approach can be used by the government as a supporting effort in controlling and preventing pneumonia which is more focused, efficient, and effective.  This paper proposes a new approach to generate a vulnerability mapping of toddler pneumonia using hybrid genetic algorithm - K-means (GA-Kmeans) clustering algorithm according to five risk factors.  K-means is a clustering algorithm that can produce data groupings based on several attributes well and quickly.  However, there is a problem in the initialization stage of the initial random seeds from K-means, which is very difficult to reach an optimum global.  The genetic algorithm is used to optimize initial seeds in the K-means algorithm. The vulnerability level of toddler pneumonia is classified into low, medium and high, then it is visualized into spatial mapping.  The result of GA-Kmeans test iteration experiments produced best variance cluster 0.99 (almost 1) and determined high levels of vulnerability in 2016 are Kedungadem, Kepohbaru, Baureno, Kanor, Sumberrejo, Balen, Kapas, Bojonegoro, Dander and Ngasem sub-districts.

     

     


  • Keywords


    Pneumonia vulnerability, K-means, genetic algorithms, spatial mapping.

  • References


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Article ID: 24425
 
DOI: 10.14419/ijet.v7i4.40.24425




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