Detecting the Ripeness of Harvest-Ready Dragon Fruit using Smaller VGGNet-Like Network - Wismadi, I Made; Khrisne, Duman Care; Suyadnya, I Made Arsa (Journal of Electrical, Electronics and Informatics; Vol 3 No 2 (2019): JEEI (August 2019)DO - 10.24843/JEEI.2019.v03.i02.p01, 2019)
This study has a purpose to develop an application to detect the ripeness of the dragon fruit with the deep learning approach using the Smaller VGGNet-like Network method. In this study, the dragon fruit are classified into two classes: ripe or ready for harvest and still raw, by using the Convolutional Neural Network (CNN). The training process utilize the hard packages in python with the backend tensorflow. The model in this research is tested using the confusion matrix and ROC method with the condition that 100 new data are tested. Based on the test conducted, the level of accuracy in classifying the ripeness of the dragon fruit is 91%, and the test using 20 epoch, 50 epoch, 100 epoch, and 500 epoch produced an AUROC value of 0,95.