Sistem Pakar Mendiagnosa Penyakit pada Mentimun Menggunakan Fuzzy Mamdani

Cucumber is a horticultural crop with numerous benefits, both in the food and industrial sectors. Cucumber production in Kupang City has seen a notCucumber is a horticultural crop with numerous benefits, both in the food and industrial sectors. Cucumber production in Kupang City has seen a noticeable decline in recent years. One of the main driving factors is the infestation of plant diseases such as yellowing leaves, powdery mildew, and root rot, which impact crop yields and farmer income. To address this issue, an expert system capable of detecting cucumber plant diseases using Mamdani Fuzzy Logistics (FUZZ). The Mamdani Fuzzy Logistics method was chosen because of its decision-making capabilities and its easy-to-understand linguistic-based rules. This system is designed to enable farmers to detect diseases early, take appropriate mitigation measures, and reduce the risk of crop failure. Implementing this technology is expected to increase efficiency in agricultural management and maintain cucumber production. Testing results on 20 cucumber plant data samples using the Mean Absolute Percentage Error (MAPE) yielded an error of 10%. The results showed that two cucumber plant samples showed diagnostic inconsistencies. With this system, farmers are expected to be able to more quickly identify the types of diseases that endanger cucumber plants and also obtain recommendations for appropriate solutions to handle the problem, thereby being able to maintain agricultural productivity.iceable decline in recent years. One of the main driving factors is the infestation of plant diseases such as yellowing leaves, powdery mildew, and root rot, which impact crop yields and farmer income. To address this issue, an expert system capable of detecting cucumber plant diseases using Mamdani Fuzzy Logistics (FUZZ). The Mamdani Fuzzy Logistics method was chosen because of its decision-making capabilities and its easy-to-understand linguistic-based rules. This system is designed to enable farmers to detect diseases early, take appropriate mitigation measures, and reduce the risk of crop failure. Implementing this technology is expected to increase efficiency in agricultural management and maintain cucumber production. Testing results on 20 cucumber plant data samples using the Mean Absolute Percentage Error (MAPE) yielded an error of 10%. The results showed that two cucumber plant samples showed diagnostic inconsistencies. With this system, farmers are expected to be able to more quickly identify the types of diseases that endanger cucumber plants and also obtain recommendations for appropriate solutions to handle the problem, thereby being able to maintain agricultural productivity.