THE APPLICATION OF THE DEMPSTER SHAFER METHOD FOR DIAGNOSTIC ON CONTENT HEALTH WEB BASED ON

  • amanda amanda Prodi Sistem Informasi, SekolahTinggi Manajemen Informatika Komputer Royal Kisaran
  • Moh. Sidik Program Studi Sistem Informasi, Sekolah Tinggi Manajemen Informatika Komputer Royal Kisaran
  • Muhammad Ihsan Program Studi Sistem Komputer, Sekolah Tinggi Manajemen Informatika Komputer Royal Kisaran
Keywords: Expert System, Gynecological disorders, Dempster Shafer, Symptoms, Solution

Abstract

Diseases or disorders in the womb can cause death, especially in pregnant women. This will also affect the infant mortality rate that is being conceived. Diseases that attack a woman's womb cannot be considered trivial and require the intervention of an expert, while an expert for this disease is still rare and certainly requires no small cost. Not only the age factor, but also the lack of knowledge about the health of the womb which is the cause. In the health sector, there is an artificial intelligence called an expert system, which is a computer system that uses knowledge, facts and reasoning techniques in solving problems that usually can only be solved by an expert in their field. This study uses the Dempster Shafer method which will perform an in-depth probability-based data analysis based on the facts that will be provided by the user. The purpose of this study is to help pregnant women to diagnose diseases during pregnancy without having to see an expert. The data used are in the form of data on symptoms, diseases, and solutions. The results of this study are an expert system for diagnosing pregnancy diseases.

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Author Biographies

Moh. Sidik, Program Studi Sistem Informasi, Sekolah Tinggi Manajemen Informatika Komputer Royal Kisaran

Sistem Informasi

Muhammad Ihsan, Program Studi Sistem Komputer, Sekolah Tinggi Manajemen Informatika Komputer Royal Kisaran

Komputer Sistem

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Published
2022-06-29
How to Cite
[1]
amanda amanda, M. S. Siddik, and M. I. Ihsan, “THE APPLICATION OF THE DEMPSTER SHAFER METHOD FOR DIAGNOSTIC ON CONTENT HEALTH WEB BASED ON”, J. Tek. Inform. (JUTIF), vol. 3, no. 3, pp. 601-610, Jun. 2022.