Abstract
Vegetables are essential food for Indonesian people in their daily lives. One of them is the cabbage plant. Cabbage is one type of vegetable that is widely consumed because it contains sources of vitamins A, B, C, minerals, carbohydrates, and proteins beneficial for health. Cabbage has perishable properties, seasonal production, and can not be stored for long. Cabbage leaves are soft, and the water content is quite a lot, thus easily penetrated by agricultural tools as well as pests and pathogens. Cited from the data of the Central Statistics Agency, cabbage production in 2017 in Indonesia reached 1.44 million tons from the previous year. One factor that causes this is cabbage plant disease. One factor that causes this is cabbage plant disease. The lack of disease control information and the limited number of experts causes these problems to be resolved to the maximum. Therefore in this study, an expert system that could diagnose cabbage plant diseases based on knowledge obtained from the expert directly was built. The expert system is built based on the web using the PHP programming language and MySQL database. The inference method used is the Forward Chaining, which can diagnose the disease by adjusting the facts experienced with the rules that have been declared. This study processed 7 diseases and 24 data symptoms. Test results show that: (1) Functional testing using the Black Box Equivalence Partitioning (EP) method gets the results as expected in the test scenarios in each test class. (2) Expertise testing by comparing the results of expert diagnoses, and the system is appropriate and running well. (3) External testing using a questionnaire involving 20 respondents from the faculty of agriculture shows that the system built has a total percentage value of an average of 83% with the category "Very Good".
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