Lova Endriani Zen
Universitas Putra Indonesia YPTK Padang

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Metode Forward Chaining dalam Menganalisis Penyakit Kucing Akibat Infeksi Virus Lova Endriani Zen; Gunadi Widi Nurcahyo; Y Yuhandri
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 4
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (348.175 KB) | DOI: 10.37034/jsisfotek.v3i4.74

Abstract

Cats are pets that are very popular today, their cute behavior and cute body shapes make people from all walks of life love them. We, especially those who like and keep cats, must pay attention to the cat's health condition, because it is possible that viral infections suffered by the cat can be contagious. At this time, the public does not have enough knowledge about education about cat diseases due to viral infections, resulting in cats being often late in getting treatment. This study aims to analyze cat diseases due to viral infections using the Forward Chaining method based on symptoms and to design an Expert System to measure the accuracy of analyzing cat diseases due to viral infections. The data needed in this study are cat data, symptom data and solution or treatment data needed to make decisions that are sourced from veterinarians from Paw's Vet Padang. Sourced form data analysis provided by the expert, the expert has a decision-making mode, which is to collect facts first to reach a conclusion or decision, so the Forward Chaining method can be used to conduct this research. The stages of data processing include preparing input data, expert decision tables, determining rules, conducting tracking processes, making decision trees and tracking results. The results obtained are successful in analyzing the symptoms and can determine diseases related to viral infectious diseases in cats so that solutions and initial steps can be determined for handling them. The results of trials conducted by comparing the data with the system that has been designed have a very good level of accuracy.