E-Nose Application for Detecting Banana Fruit Ripe Levels Using Artificial Neural Network Backpropagation Method

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Hendrick
Efrizon
Yultrisna
Yul Antonisfia
Yumna Silvia
Miguel Botto-Tobar
Humaira

Abstract

Bananas are one of the largest horticultural commodities in Indonesia because in each region of Indonesia there are different types of bananas. Bananas are climacteric because they will feel the experience even though they have been harvested. Currently, the introduction of banana ripeness is still done in a conventional way by utilizing sight and smell. However, this method is not effective in determining fruit maturity because we cannot distinguish between ripe bananas and bananas that are in the early stages because they have almost the same color and aroma. So a system is designed that resembles the human sense of smell to accurately identify the level of ripeness of the fruit. The system is named Electronic Nose or abbreviated as e-Nose. The design of the e-Nose will be done using the Artificial Neural Network Backpropagation method. The results obtained from the application of E-Nose to detect the level of ripeness of bananas with the Artificial Neural Network Backpropagation method, which is a tool capable of predicting the ripeness condition of the bananas being tested so that accurate predictions are obtained and the prediction results are displayed on the website. The accuracy results obtained from the use of the Backpropagation Neural Network method for 3 categories (immature bananas, ripe bananas, and rotten bananas) are 100%, with an epoch of 2000.

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How to Cite
[1]
Hendrick, “E-Nose Application for Detecting Banana Fruit Ripe Levels Using Artificial Neural Network Backpropagation Method”, Int. J. Data. Science., vol. 3, no. 1, pp. 11-18, Jun. 2022.
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Articles

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