The Defuzzification Methods Comparison of Mamdani Fuzzy Inference System in Predicting Tofu Production

G. Mada, Nugraha K. F. Dethan, Andika Ellena Saufika Hakim Maharani
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引用次数: 5

Abstract

One of the tofu-producing companies in Kupang City is Bintang Oesapa. With the Covid-19 pandemic,the factory needs to reconsider the amount of production by taking into account the unpredictability ofdemand and resources to minimize losses due to excessive accumulation or shortages of supplies. Indetermining the amount of production, Mamdani’s Fuzzy Inference System (FIS) can be used, whichis a method for the analysis of an uncertain system. This method has three stages in the process ofdecision making, namely fuzzification, inferencing and defuzzification. In the defuzzification stage,the FIS Mamdani has five methods, namely Centroid, Bisector, Mean of Maximum (MOM), Smallestof Maximum (SOM), and Largest of Maximum (LOM). This study discusses an application of FISMamdani with five defuzzification methods for determining daily tofu production. The purpose of thisstudy is to offer a solution by first comparing the five defuzzification methods in assessing the amount oftofu production at the Bintang Oesapa factory and then determining that which is most appropriate. Theinput variables used in this research are the amount of demand and the amount of available stock, whilethe amount of production is our variable of interest. The results showed that the best defuzzificationmethod was the MOM method with an accuracy level of 94.73% and a small error value, 5.27%. TheMOM defuzzification is expected to aid decision makers in determining the best amount of productionduring the pandemic.
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Mamdani模糊推理系统在豆腐产量预测中的去模糊化方法比较
古邦市的一家豆腐生产公司是Bintang Oesapa。在新冠疫情背景下,工厂需要考虑需求和资源的不可预测性,重新考虑产量,以尽量减少因过度积累或供应短缺造成的损失。在确定产量时,可以使用Mamdani的模糊推理系统(FIS),这是一种分析不确定系统的方法。该方法在决策过程中有三个阶段,即模糊化、推理和去模糊化。在去模糊化阶段,FIS Mamdani有五种方法,即质心、平分线、最大值均值(MOM)、最大值最小值(SOM)和最大值最大值(LOM)。本研究探讨了fismandani与5种去模糊化方法在豆腐日产量测定中的应用。本研究的目的是提供一个解决方案,首先比较五种去模糊化方法在评估Bintang Oesapa工厂的豆腐产量,然后确定哪一种最合适。本研究中使用的输入变量是需求量和可用库存量,而产量是我们感兴趣的变量。结果表明,最佳的去模糊方法是MOM法,准确率为94.73%,误差较小,为5.27%。mom去模糊化有望帮助决策者确定大流行期间的最佳产量。
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