Background of the study: Measles is a major cause of child death caused by a lack of immunization when a child is a baby.Purpose: The discussion in this paper aims to describe the results of the analysis of the spatial data modeling of measles, knowing the percentage distribution of measles-prone areas, each district based on coverage on immunization status with good, average, fair and poor classification categories. The classification results include areas with good, average, fair, and poor immunization coverage status categories.Method: The method used i.e. with to requirement gathering information data from the East Java health profile book in 2011-2016 for the measles attribute, a literature study to describe the parameter requirements based on the coverage of immunization status (infant immunization status, PD3I, epidemic, and nutritional status), and selection of artificial intelligent (AI) system methods that are in accordance with data behavior for the spatial data modeling process in the formulation of alternative preference values with a decision-making system that involves multi-criteria parameters (multiple attributes decision-making/MADM) with Simple Additive Weighting (SAW) method.Findings: The alternative preference value Vi in the spatial data modeling process with the SAW method can be used as a mathematical model for the same data series behavior.Conclusion: The results of the representation in the modeling of spatial data and this attribute data can be used as a reference for planning in the development of health care centers in areas with poor immunization status categories.
The object-oriented approach to geographic data modeling for spatial data integration
