Abstract
The main weakness in shrimp marketing is the perishable food nature of shrimp. Generally, people identify the freshness of shrimp by direct observation. However, it will be difficult to detect the freshness of shrimp if it is marketed in a closed container. In this study, a label indicator of purple sweet potato will be made to detect the freshness of shrimp. The increase in the efficiency of indicator readings is carried out using a neural network algorithm. The results of the sensitivity test showed that the label indicator of purple sweet potato extract was sensitive to the presence of ammonia.Through a comparison between the storage time of shrimp and the organoleptic quality of shrimp, it is known that the quality of shrimp is divided into four classes, namely: (i) "Very fresh" marked with a solid red color (ii) "Fresh marked with a deep blue color (iii) "not fresh marked with a dark red color. gray and (iv) “very unrefreshing marked with a faded brown color. Through label indicator image classification using a neural network algorithm, from 73 training data obtained an accuracy rate of 95.89% and a precision of 92%.
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