Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan
Vol 11 No 1 (2021): Jurnal Ilmiah Rekam Medis dan Informatika Kesehatan

Diagnosa Resiko Penyakit Jantung Menggunakan Logika Fuzzy Metode Tsukamoto

Ummi Athiyah (Prodi Sains Data, Fakultas Informatika, Institut Teknologi Telkom Purwokerto)
Felia Citra Dwiyani Putri Rosyadi (Prodi Teknik Informatika, Fakultas Informatika, Institut Teknologi Telkom Purwokerto)
Reno Agil Saputra (Prodi Teknik Informatika, Fakultas Informatika, Institut Teknologi Telkom Purwokerto)
Hafidz Daffa Hekmatyar (Prodi Teknik Informatika, Fakultas Informatika, Institut Teknologi Telkom Purwokerto)
Tufail Akhmad Satrio (Prodi Teknik Informatika, Fakultas Informatika, Institut Teknologi Telkom Purwokerto)
Adam Ikbal Perdana (Prodi Teknik Informatika, Fakultas Informatika, Institut Teknologi Telkom Purwokerto)



Article Info

Publish Date
18 Feb 2021

Abstract

The heart is one of the most vital organs in the body and its a very important role for humans. Therefore, it is very important to pay attention to the risk of heart disease from an early age. This disease can be detected early with routine examinations. Based on WHO data (2011), heart disease is the number one cause of death in the world and at least 17.5 million or the equivalent of 30% of deaths worldwide are caused by heart disease. From these problems, the researchers created an expert system using the Fuzzy Tsukamoto method to diagnose the risk of heart disease. The benefit of this research is that it can help make it easier for the general public to check the level of risk for heart disease. The input from the system is blood sugar, cholesterol, blood pressure, and body mass index (BMI), while the output is a risk rating for heart disease with 3 categories, namely small, medium, and large. The stages of the fuzzy method Tsukamoto include fuzzification, formation of IF-THEN rules, inference engine, and finally defuzzification. From the application of the fuzzy Tsukamoto produces an expert system that can diagnose heart disease with three risk categories and based on 30 test data, an accuracy value of 83 percent is generated based on a comparison of the system results with expert results.

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Journal Info

Abbrev

infokes

Publisher

Subject

Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

Description

Medical Record Medical Informatics Health Policy and Management Midwifery Public Health Critical Care and Intensive Care Medicine ...