RUBBER PLANT DISEASE DIAGNOSIS SYSTEM USING DEPTH FIRST SEARCH AND CERTAINTY FACTOR METHOD

Abstract

Rubber plants have a very important role in the economy in Indonesia, because many people depend on this commodity. The area of ​​rubber plantations in Indonesia has reached more than 3 million hectares, while Malaysia and Thailand, which are Indonesia's main competitors, have a rubber plantation area below that number. Only 15% of the rubber area is large plantations, while 85% is smallholder plantations which are managed simply as is, some even rely on natural growth. The problems faced by rubber farmers are disease and treatment problems. With these conditions, the researcher aims to build an expert system application for the diagnosis of rubber plant diseases by applying the depth first search method and Certainty Factor is used so that the expert system can reason like an expert, and to get the highest confidence value. The problems faced by rubber farmers are disease and treatment problems. Given these conditions, the researcher aims to build an expert system application for the diagnosis of rubber plant diseases by applying the depth first search certainty factor method. Depth first search and Certainty Factor methods are used so that the expert system can reason like an expert, and to get the highest confidence value. The application design by applying the depth first search method and certainty factor was successfully built into a web-based application. Black box testing on this application system has been successful in accordance with the design that has been made. The test results by experts on the identification system are in accordance with direct identification. And the results of beta testing produce a percentage of 83%, which means that users have a high level of satisfaction with the application. With the results of this test, the selected diseases were fungus disease with an accuracy of 87.54%, spot cancer with an accuracy of 97.64% and root rot disease with an accuracy of 97.41%.