IMPLEMENTASI METODE FORWARD CHAINING UNTUK DIAGNOSA PENYAKIT PADA TUMBUHAN JERUK TANAMAN BUAH DALAM POT
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
A AHRIFAENI, A AHRIFAENI
Djamain, Yasni
Sikumbang, Hengki
Djamain, Yasni
Sikumbang, Hengki
Subject
Teknik Informatika
Datestamp
2023-05-31 03:01:00
Abstract :
Fruit plants in citrus fruit pots are one of the most popular fruit plant media in pots. The purpose of this study is to apply a system for citrus fruit plants in pots (tabulampot), and make it easier for users to find out diseases in citrus plants from the symptoms that have been determined. Applying the Forward Chaining method to identify diseases and symptoms in citrus fruit plants in pots (tabulampot). The author uses Forward Chaining, Forward Chaining is a search method or forward tracking technique that combines rules to reach conclusions or goals after starting with searching for existing information. Of the seven data rules consisting of CVPD, Tristeza, Blendok, Powdery mildew, Scabies, Root and Stem Rot, Upas Mushrooms that were tested for system diagnostic data, the results for the seven data were the same as expert diagnoses, and had a Root Mean Squared Error (RMSE). which is really accurate. Based on the system that has been tested using the Root Mean Quared Error (RMSE) method with an accuracy value of 0.03 where the smaller the value, the closer the prediction is.
Fruit plants in citrus fruit pots are one of the most popular fruit plant media in pots. The purpose of this study is to apply a system for citrus fruit plants in pots (tabulampot), and make it easier for users to find out diseases in citrus plants from the symptoms that have been determined. Applying the Forward Chaining method to identify diseases and symptoms in citrus fruit plants in pots (tabulampot). The author uses Forward Chaining, Forward Chaining is a search method or forward tracking technique that combines rules to reach conclusions or goals after starting with searching for existing information. Of the seven data rules consisting of CVPD, Tristeza, Blendok, Powdery mildew, Scabies, Root and Stem Rot, Upas Mushrooms that were tested for system diagnostic data, the results for the seven data were the same as expert diagnoses, and had a Root Mean Squared Error (RMSE). which is really accurate. Based on the system that has been tested using the Root Mean Quared Error (RMSE) method with an accuracy value of 0.03 where the smaller the value, the closer the prediction is.
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