SISTEM PAKAR DIAGNOSIS PENYAKIT HIPERTENSI DENGAN MENGUNAKAN METODE FORWARD CHAINING DAN TOERI PROBABILITAS

Authors

  • Emma Rosinta Simarmata Universitas Methodist Indonesia

DOI:

https://doi.org/10.46880/mtk.v7i1.398

Keywords:

Sistem Pakar, Forward Chaining, Hipertensi, Diagnosa

Abstract

At this time hypertension is a disease that endangers the community a lot because people often ignore it because this disease does not quickly affect the body but will slowly damage health. Hypertension is not contagious but can trigger other diseases, such as stroke and heart disease. The habit of people who will check their health after experiencing problems causes delays in their help. Symptoms of hypertension can be detected early so that people can prevent the seriousness of the disease. The expert system is one of the fields of science that can help the public to check whether a person has hypertension and the type of hypertension and the percentage of possible diseases he suffers because the expert system can be designed how to work as an expert. The expert system designed uses several stages, namely (1) system requirements analysis, (2) input and output design, (3) coding, and (4) testing. This expert system will be represented in the form of rules, the method of reasoning using the forward chaining method, and probability theory in calculating the percentage of rules that are fulfilled. The form of output (results) from the expert system is in the form of diagnostic results based on the symptoms given and suggestions that are in accordance with the diagnosis used are forward chaining, namely drawing conclusions from several types of hypertension obtained from symptoms. The output of the system is the type of hypertension diagnosed and the solution for the diagnosis. Keyword : Sistem Pakar, Forward Chaining, Hipertensi, Diagnosa

References

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Published

10-03-2021

How to Cite

[1]
E. R. Simarmata, “SISTEM PAKAR DIAGNOSIS PENYAKIT HIPERTENSI DENGAN MENGUNAKAN METODE FORWARD CHAINING DAN TOERI PROBABILITAS”, METHODIKA, vol. 7, no. 1, pp. 56–64, Mar. 2021.

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