Conclusion CONCLUSION AND RECOMMENDATION

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V. CONCLUSION AND RECOMMENDATION

5.1. Conclusion

Predictor Altitude or elevation of ≥ 250 meters is non-significant variable to be entered further to the model. Since the study area is relatively flat, with undulating area and with the ratio less or greater 250 meters, it must not be influenced to deforestation or stable occurrence. The accuracy of prediction is 56.8 is actually too small for being used the model for predicting deforestation rate at the same location and different time 1997 – 2001 according to available data. This less overall accuracy was caused by there are many independent variables contributed deforestation driving force that being assumed are able to increase deforestation occurrence. Wald test as one of the important test accepts null hypothesis and means that all variables of road, river, distance from population center, distance form coast line, slope, and aspect can be used to predict deforestation. LogR_COM or independent variables of aspect and LogR_Slp Slope have higher Sig. value. Aspect and slope have contributed more cells that being assumed for deforestation occurrence. Odd ratio, in this study is showing that distance less than 1,000 km tends to deforested occurrence 3.097 times than distance greater or equal 1 km from existing road. . Rapid assessment of image classification result of deforestation Period 1990 – 1997 and Period 1997 – 2001 can be said that this classification has the accuracy non spatial of 91.63 Forest area in Period 1990 – 1997 was 6,077 ha, but had changed to be 5,262 ha in polygon vector cells. Comparison between raster and vector process showed the wide changing between both processes. This phenomenon showed, Assigning data by location spatial join was not able to 67 accommodate all of the spatial features converting from polygons to polygon vector cells. The attribute of each class, could not entered exactly to the polygon vector cells, the line between two polygon especially was identified had no attribute of the class. This underestimate of especially deforestation class, had influenced, the logistic regression analysis gave 56.8 overall accuracy to predict deforestation in Period 1997 – 2001. Deforestation was given 1 value as dependent variables, could not accommodated the all predictor independent variables of deforestation occurrence, since number of deforestation cells were underestimating. The distribution of deforestation cells were not distributed quite well where the predictors generated its effect to be deforested occurrence. Overall accuracy from comparing image classification and logistic regression analysis is 72.03 . This means that classification is closed to real condition of deforestation.

5.2. Recommendation