Neural Network Application For The Analysis Of The Nutrition And Environment Effect To Microbial Growth Rate On Fermented Soybean Patty (Tempe) Fermentation

Nur Yanti, Fathur Zaini Rachman

Abstract


The problems that still often faced by the industry in the processing of soybeans into tempe today is the lengthy process of fermentation is about 72 hours. The Long of fermentation will certainly slow down the productivity tempe. The long fermentation process is influenced by several factors such as the growth of microbial as microorganisms that do fermentation activity. The growth rate of microbes in the fermentation process is influenced by the content of the nutrient or nutrients that support the microbes can multiply rapidly perform cell division. In addition to nutrients, environmental factors such as acidity and temperature also affects to the growth rate of microbes. By applying Neural Network (Artificial Neural Networks), can produce a proper analysis to take into account the adequacy of nutrients, temperature stability and pH which accelerates the growth of tempe mold, so the fermentation process will go faster and the quality of tempe like the flavor and the aroma produced is better. The method that used is the combination method of qualitative and quantitative. The results of this study are the output data analysis results using a neural network to identify the composition of the nutrient content, temperature and pH is good and right for the fermentation process. The learning process of the neural network generates output data with high accuracy, namely with 2-4-1 architecture, learning rate 0:02, MSE 0.000999 <target error at epoch 315 0001 for nutrient content analysis. As for the temperature and pH stability analysis, learning outcomes network architecture yields 2-3-1, with learning rate 0:02, MSE 0.000986 <target error at epoch 295. 0001 performance results for the performance of the neural network is better than other architectures.


Keywords


Tempe (Fermented Soybean Patty) Fermentation, Environment, Nutrients, Neural Network

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DOI: https://doi.org/10.32487/jtt.v4i2.171

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