GDL

The purpose of this study is to compare the results of malaria risk zone mapping using scoring method and fuzzy method against the actual data (API 2014). Variables used in determining malaria risk area are temperature, densiy of vegetation and land cover. Satellite image manipulation is performed using remote sensing technology. As a result, the average of every pixel in the district area for temperature, density of vegetation, and land cover is obtained. The result is then processed using the scoring model and fuzzy model. Accuracy tests have been conducted for 91 districts where the fuzzy model has an accuracy rate of 61.54% while the scoring model produce an accuracy of 18.68%. The test result shows that the fuzzy model tends to produce a higher grade than the actual grade. Fuzzy model produces class “High” but actually “Low” (17.85%), fuzzy model produces class “Medium” but actually “Low” (2.20%), or fuzzy models produces class “High” but actually “Medium” (14.29%). This could be caused by a form of intervention for mosquito nest eradication conducted in some districts, that is quite effective. Besides, some districts are not participating actively in detecting the number of malaria cases, hence, the actual data provided by Puskesmas tends to be lower.

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