Spatial data modeling on GIS for classification of measles-prone region using multiple attribute decision making

Abstract

Indonesia is a country that has a tropical climate, so that many typical tropical climate diseases emerge. This disease is caused by viruses and parasites that breed during the dry season or the rainy season. One typical tropical disease is measles. This paper discusses the geographical information system (GIS) technology by analyzing spatial data modeling to determine the classification of measles-prone areas based on immunization status coverage using the Simple Additive Weighting (SAW) and Weight Product Model (WPM) method. Some parameters are used consist of immunization status data for multiple attribute decision making (MADM), diseases preventable by immunization (PD3I), epidemic and nutritional status of infants. The SAW and WPM method in modeling spatial data analysis processes data according to the parameters to determine the scale in comparing all alternative data on the scope of classification of immunization status areas, namely: good, average, fair and poor. The test results with the Cohen's Kappa Method Consistency Test (MCT) is obtained an average coefficient of 0.41 for consistent measurements for the chosen method. It can be concluded that the two measurements using the SAW and WPM methods have a moderate for the strength of agreement category, for using in spatial data modeling on the GIS for classification of measles prone regions using MADM.