Implementasi Dempster-Shafer Theory Sebagai Mesin Inferensi Pada Sistem Pakar Diagnosa Penyakit Cerebral Palsy

 (*)Moh. Erkamim Mail (Universitas Tunas Pembangunan, Surakarta, Indonesia)
 Mursalim Tonggiroh (Universitas Yapis Papua, Jayapura, Indonesia)
 Novi Yona Sidratul Munti (Universitas Pahlawan Tuanku Tambusai, Kampar, Indonesia)
 Yuri Rahmanto (Universitas Teknokrat Indonesia, Bandarlampung, Indonesia)

(*) Corresponding Author

Submitted: October 30, 2023; Published: December 26, 2023

Abstract

Early diagnosis and appropriate intervention are very important to minimize the long-term impact of Cerebral Palsy in children. Currently, the diagnosis of Cerebral Palsy in children is often based on clinical observations, developmental tests, and brain imaging. It requires medical knowledge and careful observation by an experienced health professional, which is often difficult to access in many areas. For this reason, early diagnosis by parents is very important for taking action against children suffering from Cerebral Palsy. This research aims to develop an expert system that can diagnose Cerebral Palsy in children using the Dempster-Shafer Theory algorithm as an inference engine to make it easier to diagnose and produce the right diagnosis. The Dempster-Shafer Theory approach works by calculating the level of confidence or belief in a hypothesis or certain event based on existing evidence. An expert system built on a website has the ability to make diagnoses based on symptoms and display diagnosis results, definitions of the type of Cerebral Palsy disease in children, as well as actions or methods of treating it. Based on the test results, the accuracy level obtained was a value of 90% and was classified as "Good" criteria.

Keywords


Cerebral Palsy; Dempster-Shafer Theory; Disease Diagnosis; Inference Engine; Expert System

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Copyright (c) 2023 Moh. Erkamim, Tungga Bhimadi Karyasa, Novi Yona Sidratul Munti, Yuri Rahmanto

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