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All Journal Jurnal Algoritma Jurnal Algoritma
Zaelani, Jaka Muhammad
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Computer Science & IT

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Rancang Bangun Aplikasi Sistem Pakar Diagnosis Penyakit Ikan Air Tawar Menggunakan Forward Chaining Mulyani, Asri; Nuraeni, Fitri; Zaelani, Jaka Muhammad
Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-1.1420

Abstract

Diseases in freshwater fish can be grouped into several types, namely diseases caused by bacteria and viruses. This greatly affects the survival of fish, deaths in large numbers result in huge losses for fish farmers because it can cause yields that are not optimal. This research aims to design a web-based expert system for freshwater fish disease diagnosis using the Forward Chaining inference method. The design method used is the Rational Unified Process (RUP). This expert system aims to diagnose freshwater fish diseases. This expert system was developed by involving knowledge from fish experts and a knowledge base that includes freshwater fish symptoms and diseases. The result of this research is a web-based application that uses the Forward Chaining inference method to determine freshwater fish diseases based on the input symptoms. This application involves designing use case diagrams, class diagrams, activity diagrams, sequence diagrams, menu structures and interfaces. Alpha testing has resulted in an accurate system for producing drug administration recommendations based on a knowledge base that has been defined by fish experts. This research concludes that the use of the Forward Chaining inference method in an expert system for diagnosing freshwater fish diseases provides efficient and accurate results in providing recommendations for drug administration. With the existence of an expert system for diagnosing freshwater fish diseases that involves knowledge base sources from fish experts, fish farmers can be more effective in dealing with the symptoms and diseases suffered by fish. This is expected to support a sustainable increase in crop yields.
Rancang Bangun Aplikasi Sistem Pakar Diagnosis Penyakit Ikan Air Tawar Menggunakan Forward Chaining Mulyani, Asri; Nuraeni, Fitri; Zaelani, Jaka Muhammad
Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-1.1420

Abstract

Diseases in freshwater fish can be grouped into several types, namely diseases caused by bacteria and viruses. This greatly affects the survival of fish, deaths in large numbers result in huge losses for fish farmers because it can cause yields that are not optimal. This research aims to design a web-based expert system for freshwater fish disease diagnosis using the Forward Chaining inference method. The design method used is the Rational Unified Process (RUP). This expert system aims to diagnose freshwater fish diseases. This expert system was developed by involving knowledge from fish experts and a knowledge base that includes freshwater fish symptoms and diseases. The result of this research is a web-based application that uses the Forward Chaining inference method to determine freshwater fish diseases based on the input symptoms. This application involves designing use case diagrams, class diagrams, activity diagrams, sequence diagrams, menu structures and interfaces. Alpha testing has resulted in an accurate system for producing drug administration recommendations based on a knowledge base that has been defined by fish experts. This research concludes that the use of the Forward Chaining inference method in an expert system for diagnosing freshwater fish diseases provides efficient and accurate results in providing recommendations for drug administration. With the existence of an expert system for diagnosing freshwater fish diseases that involves knowledge base sources from fish experts, fish farmers can be more effective in dealing with the symptoms and diseases suffered by fish. This is expected to support a sustainable increase in crop yields.
Co-Authors Asri Mulyani Fitri Nuraeni