Spatial data modeling for mapping of slum region using multi-attribute utility theory method

Slums are one of the social problems that are often faced by almost all areas in big cities. The need for handling efforts to overcome slum settlements through mapping the distribution and knowing the priorities for handling slum settlements. This paper presents, spatial data modeling to map slum region using a multi-attribute decision making (MADM) approach based on geographical information system (GIS) technology. Mapping of slum region using the multi-attribute utility theory method based on multi-attribute parameters of the condition of building density, drainage, roads, drinking water supply, waste treatment, trash treatment, and fire protection. Dataset private data types from the department of public office in Mojokerto districts, was the subject of our analysis. The results of the method test show the advantages of mapping slum regions which will produce a layer of information on slum region, the level of the slum region, and the handling of slum regions with a precision value of 75%, recall 80%, and accuracy of 76%. With a kappa coefficient value of 0.62. The results of the trial state that this method has good agreement strength for use in mapping spatial data of slum regions using the MADM approach.