Identification of Color and Texture of Ripe Passion Fruit with Perceptron Neural Network Method

Abstract

Research using artificial neural network methods has been developed as a tool that can help human tasks, one of which is for passion fruit UMKM entrepreneurs. The problem so far that has been faced by UMKM entrepreneurs of passion fruit is that it is difficult to identify ripe passion fruit with sweet and sour taste, because there are 6 colors of passion fruit and the color of passion fruit skin is visually slightly different, as well as the texture of maturity. The main purpose of this study was to identify the color structure and texture of the ripeness of passion fruit, in order to recognize the color and texture of the ripeness of passion fruit which is good for processing into syrup, jam, jelly, juice, passion fruit juice powder by entrepreneurs of UMKM of passion fruit. This study empirically tested the color and texture of the ripeness of 10 passion fruit using the perceptron artificial neural network learning method. The data is obtained from an image that will be entered into the program. The results of the identification process using the perceptron artificial neural network from the tests that have been carried out previously, the highest calculation results obtained with the best results using a learning rate of 0.8 and 500 epoch iterations and producing an accuracy of 80%.

Computational Modelling for Optimization of Thermosonicated Sohshang (Elaeagnus latifolia) Fruit Juice Using Artificial Neural Networks

“…Through ANN modelling, the optimal treatment combination yielded the highest and most desired values for the physical and chemical properties, functioning, and biological attributes. These results demonstrated a good match with the expected values, demonstrating the accuracy and growth potential of the ANN model [28,49,50].…”