MULTI-LAYER INFERENCE FUZZY TSUKAMOTO DETERMINING LAND SUITABILITY CLASS OF COCOA PLANTS - Iin Intan Uljanah; Shofwatul Uyun (JURNAL TEKNIK INFORMATIKA, 2021)
Determining the land suitability class of plants specifically cocoa (Theobroma cacao) is significant to do because each plant has a different characteristic of growth. This research aims at implementing the algorithm to determine the land suitability class of cocoa plants using the Multi-Layer Inference Fuzzy Tsukamoto (MLIFT). This research uses 18 input variables including 15 non-linguistic variables or crisp and the rest are linguistic ones or fuzzy as the data of growth requirements of cocoa plants. Generally, the algorithm used consists of three main steps those are fuzzification, Tsukamoto inference machine, and defuzzification consisting of three layers. The first layer covers seven inference engines, while each of the second and the third ones only consists of one inference engine. The concept of inference process in Fuzzy Tsukamoto is calculating the weighted average of each result of the  nference process. Based on the testing result, it can be concluded that the multi-layer inference Fuzzy Tsukamoto for determining the land suitability class of cocoa plants has an accuracy level amounted 97%.