• Dony Sihotang
  • Honey Ndoen
  • Defritus Punuf
Keywords: Malaria, fuzzy model, level of risk


The purpose of this study was to compare the result of mapping malaria risk zone by fuzzy method against actual data of Annual Parasite Insidence (API 2014). Variables used in determining malaria risk area are temperature, densiy of vegetation and land cover. From the result of satellite image processing by remote sensing technology obtained an average of every pixel in the district area for temperature, density of vegetation and land cover are then processed by the fuzzy model. Accuracy tests have been conducted for 100 districts where the fuzzy model has an accuracy rate of 61.54%. From the test results shows that the fuzzy model tends to produce a higher grade than the grade on actual data. As 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 is quite effective. Beside that, several districts are still passive in detecting the number of malaria cases so that the actual data is provided tends to lower.


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