Barwardono, Wiyoga
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Pengembangan Sistem Pakar Diagnosa Penyakit Kubis Berbasis Forward Chaining Septiana, Yosep; Barwardono, Wiyoga; Nurhalim, Ahmad
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.21-1.1438

Abstract

Diseases that often attack cabbage plants such as soft rot, black rot, stem rot, crop worms, ground worms, leaf spot, leaf caterpillars and clubroot often occur, making it difficult for farmers to identify the disease. Diseases in cabbage plants are often a factor that cabbage farmers often complain about and many still have difficulty in diagnosing diseases that attack cabbage plants. In addition, cabbage farmers often do not have adequate knowledge about the various types of diseases that can attack cabbage plants, so they often have difficulty choosing the right treatment strategy. The aim of this research is to develop an expert system for diagnosing cabbage diseases using the forward chaining method. To provide benefits for farmers or experts to identify diseases that attack cabbage from the symptoms experienced by the plants. This system was built using the forward chaining inference method and the design method used the rational unified process method, the stages starting from inception, elaboration, construction and transition. In connection with this problem, to simplify or obtain information about the symptoms that occur in cabbage. This research includes developing an expert system and diagnosing cabbage diseases from symptoms collected using the forward chaining method. It is hoped that this system can help farmers or agricultural experts in preventing diseases that can damage cabbage plants and can improve quality and production. Good. As a result of the research, an expert system for diagnosing cabbage diseases was created. This application was built for farmers to diagnose diseases in cabbage from the symptoms they experienced. With this system, it can provide forgiveness and knowledge for farmers.
Pengembangan Sistem Pakar Diagnosa Penyakit Kubis Berbasis Forward Chaining Septiana, Yosep; Barwardono, Wiyoga; Nurhalim, Ahmad
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.21-1.1438

Abstract

Diseases that often attack cabbage plants such as soft rot, black rot, stem rot, crop worms, ground worms, leaf spot, leaf caterpillars and clubroot often occur, making it difficult for farmers to identify the disease. Diseases in cabbage plants are often a factor that cabbage farmers often complain about and many still have difficulty in diagnosing diseases that attack cabbage plants. In addition, cabbage farmers often do not have adequate knowledge about the various types of diseases that can attack cabbage plants, so they often have difficulty choosing the right treatment strategy. The aim of this research is to develop an expert system for diagnosing cabbage diseases using the forward chaining method. To provide benefits for farmers or experts to identify diseases that attack cabbage from the symptoms experienced by the plants. This system was built using the forward chaining inference method and the design method used the rational unified process method, the stages starting from inception, elaboration, construction and transition. In connection with this problem, to simplify or obtain information about the symptoms that occur in cabbage. This research includes developing an expert system and diagnosing cabbage diseases from symptoms collected using the forward chaining method. It is hoped that this system can help farmers or agricultural experts in preventing diseases that can damage cabbage plants and can improve quality and production. Good. As a result of the research, an expert system for diagnosing cabbage diseases was created. This application was built for farmers to diagnose diseases in cabbage from the symptoms they experienced. With this system, it can provide forgiveness and knowledge for farmers.