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 stud...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | Indonesian |
| Published: |
Indonesian Society of Applied Science (ISAS)
2025-06-01
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| Series: | Journal of Applied Computer Science and Technology |
| Subjects: | |
| Online Access: | https://journal.isas.or.id/index.php/JACOST/article/view/1113 |
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| Summary: | 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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| ISSN: | 2723-1453 |