Expert System for Diagnosing Gourami Fish Diseases Using the Certainty Factor Approach
Gourami is an economically significant fish in the aquaculture sector due to its high market demand and relatively stable price. However, it is also challenging to cultivate, with disease outbreaks being one of the primary difficulties. Early diagnosis of gourami fish diseases requires expertise fro...
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| Format: | Article |
| Language: | Indonesian |
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Universitas Muhammadiyah Purwokerto
2025-03-01
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| Series: | Jurnal Informatika |
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| Online Access: | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/26031 |
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| author | Hindayati Mustafidah Ilham Gunadi Cahyono Purbomartono Suwarsito Suwarsito Eri Zuliarso |
| author_facet | Hindayati Mustafidah Ilham Gunadi Cahyono Purbomartono Suwarsito Suwarsito Eri Zuliarso |
| author_sort | Hindayati Mustafidah |
| collection | DOAJ |
| description | Gourami is an economically significant fish in the aquaculture sector due to its high market demand and relatively stable price. However, it is also challenging to cultivate, with disease outbreaks being one of the primary difficulties. Early diagnosis of gourami fish diseases requires expertise from fish health specialists, who are often difficult to find due to their limited availability. With advancements in artificial intelligence-based technology, this study developed an expert system to diagnose gourami fish diseases based on observed symptoms. The system employs the Certainty Factor (CF) approach to estimate the likelihood of a particular disease affecting the fish. The Certainty Factor approach utilizes a knowledge base derived from expert knowledge to address uncertainty in diagnosis. The certainty factor weights are determined based on confidence levels from both experts and users to generate an accurate diagnosis. This expert system was developed using data from 20 types of gourami fish diseases and 38 associated symptoms. The system successfully identified diseases with a certain level of confidence and provided appropriate treatment recommendations based on the confidence level obtained. By implementing this expert system, the risk of disease outbreaks can be minimized, thereby improving efficiency and productivity in gourami fish farming while helping maintain fish health and reducing economic losses caused by disease. |
| format | Article |
| id | doaj-art-d1bd3045c50a4b71b3dd2712bd860a86 |
| institution | DOAJ |
| issn | 2086-9398 2579-8901 |
| language | Indonesian |
| publishDate | 2025-03-01 |
| publisher | Universitas Muhammadiyah Purwokerto |
| record_format | Article |
| series | Jurnal Informatika |
| spelling | doaj-art-d1bd3045c50a4b71b3dd2712bd860a862025-08-20T02:57:08ZindUniversitas Muhammadiyah PurwokertoJurnal Informatika2086-93982579-89012025-03-01677510.30595/juita.v13i1.2603121043Expert System for Diagnosing Gourami Fish Diseases Using the Certainty Factor ApproachHindayati Mustafidah0Ilham Gunadi1Cahyono Purbomartono2Suwarsito Suwarsito3Eri Zuliarso4Universitas Muhammadiyah PurwokertoUniversitas Muhammadiyah PurwokertoUniversitas Muhammadiyah PurwokertoUniversitas Muhammadiyah PurwokertoUniversitas STIKUBANKGourami is an economically significant fish in the aquaculture sector due to its high market demand and relatively stable price. However, it is also challenging to cultivate, with disease outbreaks being one of the primary difficulties. Early diagnosis of gourami fish diseases requires expertise from fish health specialists, who are often difficult to find due to their limited availability. With advancements in artificial intelligence-based technology, this study developed an expert system to diagnose gourami fish diseases based on observed symptoms. The system employs the Certainty Factor (CF) approach to estimate the likelihood of a particular disease affecting the fish. The Certainty Factor approach utilizes a knowledge base derived from expert knowledge to address uncertainty in diagnosis. The certainty factor weights are determined based on confidence levels from both experts and users to generate an accurate diagnosis. This expert system was developed using data from 20 types of gourami fish diseases and 38 associated symptoms. The system successfully identified diseases with a certain level of confidence and provided appropriate treatment recommendations based on the confidence level obtained. By implementing this expert system, the risk of disease outbreaks can be minimized, thereby improving efficiency and productivity in gourami fish farming while helping maintain fish health and reducing economic losses caused by disease.http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/26031certainty factordiseaseexpert systemgouramisymptoms. |
| spellingShingle | Hindayati Mustafidah Ilham Gunadi Cahyono Purbomartono Suwarsito Suwarsito Eri Zuliarso Expert System for Diagnosing Gourami Fish Diseases Using the Certainty Factor Approach Jurnal Informatika certainty factor disease expert system gourami symptoms. |
| title | Expert System for Diagnosing Gourami Fish Diseases Using the Certainty Factor Approach |
| title_full | Expert System for Diagnosing Gourami Fish Diseases Using the Certainty Factor Approach |
| title_fullStr | Expert System for Diagnosing Gourami Fish Diseases Using the Certainty Factor Approach |
| title_full_unstemmed | Expert System for Diagnosing Gourami Fish Diseases Using the Certainty Factor Approach |
| title_short | Expert System for Diagnosing Gourami Fish Diseases Using the Certainty Factor Approach |
| title_sort | expert system for diagnosing gourami fish diseases using the certainty factor approach |
| topic | certainty factor disease expert system gourami symptoms. |
| url | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/26031 |
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