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All Journal Arcitech: Journal of Computer Science and Artificial Intelligence
Darmawan, Algifari
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Computer Science & IT

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Sistem Informasi Pendeteksi Penyakit Pada Kucing Dengan Metode Backward Chaining Karnila, Sri; Darmawan, Algifari
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 2 (2024): December 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i2.12033

Abstract

Cats are a popular pet in Indonesia, with a significant increase in the number of owners. However, difficulties in recognizing early symptoms of diseases often lead to delayed treatment and worsen the health condition of the cats. The purpose of this study is to design a web-based information system that can help detect diseases in cats earlier. The research methodology employs Backward Chaining to detect cat diseases at an early stage. The process begins with collecting symptoms from users, matching these symptoms against a database, and backtracking to determine the likely diseases. Black box testing shows that the system functions well, while validation with entered case data indicates that the Backward Chaining method is successful in providing relevant initial action recommendations. Unlike previous studies, which generally only developed systems based on symptom lists without deep inferential capabilities, this research fills a gap by integrating a more systematic backtracking mechanism through the Backward Chaining method. This approach allows the system to deliver more accurate and specific diagnoses based on a combination of symptoms.
Co-Authors Sri Karnila