Klasifikasi Tekstur Kematangan Buah Jeruk Manis Berdasarkan Tingkat Kecerahan Warna dengan Metode Deep Learning Convolutional Neural Network

Abstract

Sweet orange is very much consumed by humans because oranges are rich in vitamin C, sweet oranges can be consumed directly to drink. The classification carried out to determine proper (good) and unfit (rotten) oranges still uses manual methods, This classification has several weaknesses, namely the existence of human visual limitations, is influenced by the psychological condition of the observations and takes a long time. One of the classification methods for sweet orange fruit with a computerized system the Convolutional Neural Network (CNN) is algorithm deep learning to the development of the Multilayer Perceptron (MLP) with 100 datasets of sweet orange images, the classification accuracy rate was 97.5184%. the classification was carried out, the result was 67.8221%. Testing of 10 citrus fruit images divided into 5 good citrus images and 5 rotten citrus images at 96% for training 92% for testing which were considered to have been able to classify the appropriateness of sweet orange fruit very well. The graph of the results of the accuracy testing is 0.92 or 92%. This result is quite good, for the RGB histogram display the orange image is good