Abstract
Rubella or better known as German measles is a viral infection characterized by a red rash on the skin. Lack of general public understanding of this disease makes the number of patients with Rubella increasing. So Fuzzy Tsukamoto's method is used to detect Rubella disease. The purpose of this study is to facilitate the public in understanding about Rubella disease so as to reduce the number of sufferers of this disease. The first step to detecting Rubella disease is to determine the fuzzy set and the domain which includes 3 variables: red rash, swollen lymph nodes, and fever. The output of fuzzy calculations is someone experiencing Rubella or normal symptoms. The value obtained from the calculation process usig the Tsukamoto method is 6.00.
Highlights
a viral infection characterized by a red rash on the skin
Lack of general public understanding of this disease makes the number of patients with Rubella increasing
The purpose of this study is to facilitate the public in understanding about Rubella disease
Summary
Kurnianingtyas (2017) optimasi fuzzy Tsukamoto pada diagnosis penyakit sapi potong menghasilkan nilai fuzzyfikasi batasan gejala penyakit yang optimal dengan akurasi sebesar 98,04%. Akurasi tersebut mengalami peningkatan sebesar 3,54% sesudah dilakukannya. Pembentukan Rule optimasi [12]. Pada penelitian terdahulu yang dilakukan oleh Kurniati (2019) Diagnosa Autisme ADHD (Attention Deficit Hyperaktivity Disorder) dapat. Mesin Inferensi dilakukan dengan menggunakan logika fuzzy tsukamoto dengan persentasi keakuratan dengan hasil perhitungan IARS sebesar 100% [13].
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