Expert System for Diagnosing Potential Diabetes Attacks Using the Fuzzy Tsukamoto

Christy Atika Sari, Wellia Shinta Sari, Andi Danang Krismawan

Abstract


Diabetes is one of the top three killers in Indonesia. According to the 2014 sample enrollment survey, the number of people with diabetes is increasing year by year. This is because the diagnosis of the disease is delayed. Also unhealthy lifestyle. In an era of fast and efficient technological advancement, this is a very good thing for advancement in various fields. More and more fields of knowledge are developing, one of which is expert systems. An expert system is a software or computer program that matches the ability of an expert, meaning that it can match humans with special abilities that ordinary people cannot solve. Expert systems aim to solve specific problems, such as in fields such as medicine, education, etc. This expert system takes as inputs several variables consisting of transient blood sugar (GDS), fasting blood sugar (GDP), frequent hunger, thirst, weight loss, and urine (BAK), the method used by the author is Fuzzy Tsukamoto. This Tsukamoto method states that every result of IF-Then must be described as a fuzzy set with an immutable or monotonic membership function, and uses PHP for programming. The results obtained in the study conducted by the authors were in the form of an expert system that detects diabetes and obtains results with 94% accuracy.

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DOI: https://doi.org/10.33633/jais.v7i2.6796

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