Analisis Perbandingan Fuzzy Inference System Metode Mamdani dan Sugeno dalam Optimisasi Produksi Barang

Abstract

The demand and supply in the production sector do not exhibit a clear data trend, making them uncertain. Optimizing the production quantity is one solution to prevent losses. The Mamdani and Sugeno methods are approaches that can be used to optimize the production quantity of goods. This study analyzes the comparison between the Mamdani and Sugeno methods in production cases. Two different cases were used to compare their accuracy levels, measured by calculating the Mean Absolute Percentage Error (MAPE). In case 1, which involved bread production, the Mamdani method was found to be more accurate, while in case 2, which involved eggroll production, the Sugeno method proved to be more accurate. This research demonstrates that the difference in the accuracy of these methods depends on the data characteristics, types of distribution, and rules formation of each case. Based on the results of this study, it provides time efficiency in selecting the most accurate method for optimizing production.