<![CDATA[Gathering geospatial information in an organization is one of the most critical processes to support decision-making and business sustainability. However, many obstacles can hinder this process, like uncertain natural conditions and a large geographical area. This problem causes the organization only to obtain a few sample points of observation, resulting in incomplete information. The data incompleteness problem can be solved by applying spatial interpolation to estimate or determine the value of unavailable data. Spatial interpolation generally uses geostatistical methods. These geostatistical methods require a variogram as a model built based on the knowledge and input of geostatistic experts. The existence of this variogram becomes a necessity to implement these methods. However, it becomes less suitable to be applied to organizations that do not have geostatistics experts. This research will develop a Fast IGMN model in solving spatial interpolation. In this study, results of the modified Fast IGMN model in spatial interpolation increase the interpolation accuracy. Fast IGMN without modification produces MSE = 1.234429691, while using Modified Fast IGMN produces MSE = 0.687391. The MSE value of the Fast IGMN-Modification model is smaller, which means that the smaller the MSE value, the higher the accuracy of the interpolation results. This modified Fast IGMN model can solve problems in gathering information for an organization that does not have geostatistics experts in the spatial data modeling process. However, it needs to be developed again with more varied input data.]]>
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