I Gusti Putu Asto Buditjahjanto
The Fuzzy Multi-attribute Group Decision Making (FMAGDM) has been used to assist people in making decisions for sophisticated problems. In Traditional Chinese Medicine (TCM), decision making is used to select the syndrome differentiation of a patient. If the TCM physicians or practitioners are mistaken to determine the syndrome differentiation of a patient can cause wrong in the medication. Conventionally, the problem in TCM is the difficulty in determining a syndrome differentiation of patients. The difficulty is because the numbers of symptoms are numerous and these symptoms are correlated directly to syndrome differentiation. Even some symptoms in one syndrome can emerge from other syndromes. Therefore, the TCM physicians or practitioners must capable to identify the type of symptoms that correlated to the syndrome. The objective of this research is to use FMAGDM to determine the syndrome differentiation in the lung organ from the TCM perspective. Lung syndrome differentiation has five syndromes and 18 symptoms. This research is composed of three methods such as a Fuzzy Linguistic, FMAGDM, and Simple Additive Weighting. The fuzzy set is utilized to capture the experts’ linguistic preference toward the severity level of symptoms, FMAGDM is utilized to build the weight matrix of the experts and Simple Additive Weighting is utilized to select the syndrome differentiation of the patient through selecting the highest preference value of the expert of lung syndrome differentiation. By using data from 9 lung syndrome patients who had different symptoms, the FMAGDM simulation results showed that FMAGDM can determine the type of lung syndrome suffered by each patient. The FMAGDM simulation results for the 9 patients were the same as the diagnosis result of a physician exactly. This shows that FMAGDM can determine the type of lung syndrome as well. © 2021
Universitas Negeri Surabaya, Indonesia
