Expert System for Diagnosing Disease Symptoms of Rice Pests Using the Dempster Shafer Algorithm and Fuzzy Tsukamoto Algorithm

Abstract

Agriculture is the largest sector in almost every developing country economy. This sector produces food for most of the population in the country. Some Indonesian people work as farmers who have an important role to ensure the availability of basic ingredients, namely rice from rice. However, the limited number of experts, namely Field Agricultural Extension Officers (PPL) results in limited counseling that will be obtained by farmers, because to overcome all the problems faced by farmers, it is constrained by time and the number of farmers who have problems with their crops. In this case, farmers find it difficult to deal with problems of pests and diseases that attack rice, therefore a tool or an expert application is needed that can help farmers to diagnose pests and diseases of rice in order to provide solutions to overcome them. In connection with that, this study aims to develop an application design of an expert system for diagnosing rice pests using the Fuzzy Tsukamoto algorithm which is a method for classifying objects based on the most similar data, and adding the Dempster Shafer algorithm as a comparison of the methods used to obtain data. maximum result validation. By using the Fuzzy Tsukamoto Algorithm, the author classifies similar objects, in this case the symptoms that often occur during the rice harvest season, then compares them with the Dempster Shafter Algorithm to obtain validation of diseases that occur in rice plants based on the classification of symptoms that have been mapped. . Furthermore, the system will provide the best decision to provide advice related to diseases experienced by rice plants so that farmers can immediately resolve them.