Sistem Rekomendasi untuk Diagnosa Penyakit Pencernaan Menggunakan Teorema Bayes Berbasis BOT Telegram

Hastalina, Sherina Eria (2022) Sistem Rekomendasi untuk Diagnosa Penyakit Pencernaan Menggunakan Teorema Bayes Berbasis BOT Telegram. Tugas Akhir thesis, University of Technology Yogyakarta.

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Abstract

Expert System For Digestive Disease Diagnosis Using the Telegram Bot-Based Bayes Theorem Sherina Eria Hastalina, Suyud Widiono M.Kom Information Systems Study Program, Faculty of Science & Technology University of Technology Yogyakarta Jl. Ringroad Utara Jombor Sleman Yogyakarta E-mail : hastalinas@gmail.com, suyud.w@uty.ac.id, ABSTRACT Digestive disease is a disease that is often suffered by the community. Digestive disease is a disease that attacks the digestive system which consists of the mouth, esophagus, stomach, small intestine, and large intestine. Lack of knowledge of the symptoms that attack can lead to delays in treatment and make the situation worse. Based on these problems, we need a system that can provide the right and fast solution in dealing with these problems. The system developed uses the Telegram Bot to display questions about the symptoms experienced so that it can provide recommendations for the first treatment. In this study, the Bayes theorem method is used to diagnose a disease. Bayes theorem is a method used to determine the probability value so that it can produce a decision based on the causes that occur. The results of this study can diagnose digestive diseases as many as 4 types of diseases, namely ulcers, GERD, diarrhea and constipation. In addition, it can also provide knowledge about the type of disease and how to treat it quickly so that it can help patients as system users to get help early. Keywords: Expert System, Bayes Theorem, Digestive Disease, Telegram Bot

Item Type: Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Fakultas Sains Dan Teknologi > S1 Sistem Informasi
Depositing User: Kaprodi S1 Sistem Informasi UTY
Date Deposited: 28 Sep 2022 01:21
Last Modified: 28 Sep 2022 01:21
URI: http://eprints.uty.ac.id/id/eprint/10363

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