Meilinda Sari
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Sistem Pakar Deteksi Penyakit pada Anak Menggunakan Metode Forward Chaining Sari, Meilinda; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurnal Sistim Informasi dan Teknologi 2020, Vol. 2, No. 4
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jsisfotek.v2i4.114

Abstract

Health is the most valuable thing for humans, because anyone is vulnerable to health problems. Especially in children, children are very susceptible to germs and sensitivity to symptoms of a disease is a fear for parents themselves. However, with the convenience of having an expert doctor, sometimes there are also weaknesses such as limited working hours or doctor's practice hours and the number of patients who have to wait for queues and also the many parents who do not know about the symptoms and types of children's diseases, a system is built to facilitate the medical team and system users. The purpose of this study was to detect children's diseases using the Forward Chaining method precisely and accurately. The data that were processed were 25 symptoms and 5 types of childhood diseases which were sourced from patient medical records and interviews with experts at the Ibnu Sina Simpang Empat Islamic Hospital. The symptoms and types of disease are entered into the Expert System using the rules of rules and the Forward Chaining method. To diagnose a child's disease, a Forward Chaining method is needed with the following stages: Preparing input data, determining decision tables, determining rules, tracking processes, making decision trees. The results of the study with 25 symptom data obtained as many as 5 decision rules, namely which type of childhood disease the patient has and the initial treatment that must be done. Based on the analysis carried out, it can be seen the types of diseases suffered by children so that it can be used as a reference for making decisions to diagnose diseases in children. This expert system calculation shows the percentage of success from the expert.