Penentuan Faktor Pemicu Gejala Penyakit Mata Glaukoma, Astigmatis, Hipermetropi, dan Miopi

Expert systems support medical problem-solving, including the analysis of eye diseases. According to BPS RI (2022), over 8 million Indonesians suffer from visual impairments. In diagnosis, doctors often struggle to identify the primary causes of symptoms, impacting treatment effectiveness. This study proposes a system that combines Backward Chaining and Simple Additive Weighting (SAW) to systematically identify and prioritize causal factors of eye diseases. Backward Chaining is used to trace relationships between symptoms and the causes of glaucoma, astigmatism, hyperopia, and myopia. SAW is applied to assign weights to each causal factor and determine priority based on score ranking. Testing with 45 patient cases shows the system achieves 91% accuracy in identifying dominant causes. The 9% error rate stems from data limitations, subjective weighting in SAW, and inference rules in Backward Chaining. This system offers valuable support in early decision-making by helping doctors prioritize handling strategies based on the most significant underlying factors, thereby enhancing diagnostic efficiency and consistency.

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Publication Details

Journal
Journal of Applied Computer Science and Technology
Published
2025-06-22
DOI
https://doi.org/10.52158/jacost.v6i1.1113
Primary Topic
Computer Science and Engineering
Type
article
Field-Weighted Citation Impact
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article

Penentuan Faktor Pemicu Gejala Penyakit Mata Glaukoma, Astigmatis, Hipermetropi, dan Miopi

Gede Indrawan, I Kadek Arta Wiguna, Dewa Gede Hendra Divayana
Journal of Applied Computer Science and Technology
Computer Science and Engineering
article

Penentuan Faktor Pemicu Gejala Penyakit Mata Glaukoma, Astigmatis, Hipermetropi, dan Miopi

Gede Indrawan, I Kadek Arta Wiguna, Dewa Gede Hendra Divayana
article en

Abstract

Expert systems support medical problem-solving, including the analysis of eye diseases. According to BPS RI (2022), over 8 million Indonesians suffer from visual impairments. In diagnosis, doctors often struggle to identify the primary causes of symptoms, impacting treatment effectiveness. This study proposes a system that combines Backward Chaining and Simple Additive Weighting (SAW) to systematically identify and prioritize causal factors of eye diseases. Backward Chaining is used to trace relationships between symptoms and the causes of glaucoma, astigmatism, hyperopia, and myopia. SAW is applied to assign weights to each causal factor and determine priority based on score ranking. Testing with 45 patient cases shows the system achieves 91% accuracy in identifying dominant causes. The 9% error rate stems from data limitations, subjective weighting in SAW, and inference rules in Backward Chaining. This system offers valuable support in early decision-making by helping doctors prioritize handling strategies based on the most significant underlying factors, thereby enhancing diagnostic efficiency and consistency.

Journal of Applied Computer Science and TechnologyVol. 6(1)
Universitas Pendidikan Ganesha (ID)
Openalex Percentile: Top 93%
Computer Science and Engineering
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