Deteksi Tingkat Kematangan Buah Pisang Berdasarkan Citra Gambar Menggunakan Metode Convolutional Neural Network
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Putra, Arwinsyah
Rusjdi, Darma
Yudho, Satrio
Rusjdi, Darma
Yudho, Satrio
Subject
Teknik Informatika
Datestamp
2022-09-27 02:21:05
Abstract :
The quality of fresh bananas is a major concern for consumers and the fruit industry. The effectiveness and fast classification of the ripeness level of bananas are aspects that will determine the quality of banana.today's era, it is very important to create and implement deep learning to help human performance. One of them is to check the level of maturity of the banana fruit. The ripeness of the banana fruit usually affects the quality of the banana and the market price of the fruit. In this research, we propose an automatic computer vision system to make the maturity level of the banana using a deep learning approach. The preparations carried out were compiling a convolutional neural network working data framework using RGB images, and color characteristics and brown spots, and taking into account the ripening rate of bananas. The results and performance of the proposed system compared to other systems are quite superior where the accuracy rate reaches 98.55%.
The quality of fresh bananas is a major concern for consumers and the fruit industry. The effectiveness and fast classification of the ripeness level of bananas are aspects that will determine the quality of banana.today's era, it is very important to create and implement deep learning to help human performance. One of them is to check the level of maturity of the banana fruit. The ripeness of the banana fruit usually affects the quality of the banana and the market price of the fruit. In this research, we propose an automatic computer vision system to make the maturity level of the banana using a deep learning approach. The preparations carried out were compiling a convolutional neural network working data framework using RGB images, and color characteristics and brown spots, and taking into account the ripening rate of bananas. The results and performance of the proposed system compared to other systems are quite superior where the accuracy rate reaches 98.55%.
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