Proceedings of the 1st Asian Conference on Humanities, Industry, and Technology for Society, ACHITS 2019, 30-31 July 2019, Surabaya, Indonesia

Research Article

Weighted sum model for Spatial Analysis in Classification of Areas Prone to Diphtheria Tetanus

Download491 downloads
  • @INPROCEEDINGS{10.4108/eai.30-7-2019.2287611,
        author={Anik Vega Vitianingsih and Achmad  Choiron and Dwi  Cahyono and Suyanto  Suyanto},
        title={ Weighted sum model for Spatial Analysis in Classification of Areas Prone to Diphtheria Tetanus },
        proceedings={Proceedings of the 1st Asian Conference on Humanities, Industry, and Technology for Society, ACHITS 2019, 30-31 July 2019, Surabaya, Indonesia},
        publisher={EAI},
        proceedings_a={ACHITS},
        year={2019},
        month={9},
        keywords={spatial analysis spatial data modeling gis saw wpm mcdm},
        doi={10.4108/eai.30-7-2019.2287611}
    }
    
  • Anik Vega Vitianingsih
    Achmad Choiron
    Dwi Cahyono
    Suyanto Suyanto
    Year: 2019
    Weighted sum model for Spatial Analysis in Classification of Areas Prone to Diphtheria Tetanus
    ACHITS
    EAI
    DOI: 10.4108/eai.30-7-2019.2287611
Anik Vega Vitianingsih1,*, Achmad Choiron1, Dwi Cahyono1, Suyanto Suyanto1
  • 1: Informatics Departments, Universitas Dr. Soetomo Surabaya, Indonesia
*Contact email: vega@unitomo.ac.id

Abstract

The health sector in developing countries such as the State of Indonesia is still experiencing problems to find out the distribution of areas prone to tropical infectious diseases, including diphtheria and tetanus. The purpose of the discussion in this paper is to perform spatial analysis of geographical information system (GIS) fields through spatial data modeling for classification of diphtheria and tetanus-prone areas based on spatial parameters of datasets such as immunization status coverage, PD3I, epidemics, nutrition status, and population density. The Weighted sum model (WSM) method is used in the process of spatial analysis which is a multiple criteria decision making (MCDM). The classification results of diphtheria and tetanus-prone areas consist of good, fair, and poor categories. Preference values are generated using the Guttman method as a basis for spatial analysis in the next series of data. The test results can be used as recommendations in policymaking to anticipate the distribution of areas in the poor category.