Spatial Analysis on Potato Cyst Nematode (Globodera rostochiensis) Attacks Identification using the Fuzzy Mamdani Method

Abstract

The most important risk in potatoes farming is the Potato Cyst Nematode (PCN) attacks. The attacks were marked by a decrease in production of up to 70%. This means it has dropped to 11.89 tons/ha from Indonesia's average production of 16.99 tons/ha. The objective of this study was identifying PCN attacks using the Fuzzy Mamdani method. The contribution of this study was that we used spatial analysis to identify abiotic factors that affect the PCN attacks level, namely altitude, slope, temperature, and rainfall. To balance sensitivity we arranged in random grid-based sampling points. We took 5-10 stabs/ha in Kejajar, Indonesia. The sampling pattern used a combination of military standard 105B with a random grid. We used 4 stages to get the output, namely the fuzzy sets formation, the implications function with the minimum method, the rules composition with the maximum method and defuzzification. The fuzzy model was designed with 81 rules to obtain 3 types of PCN attack level intensity. The results showed that the accuracy rate of this method was 98.3%. This means that to support decision making in identifying PCN attacks, this spatial analysis method can be used. For further research, this method can be implemented for other potato disease types.