Convolutional Neural Network implementation for image-based Salak sortation

Rismiyati,; Sn, Azhari

doi:10.1109/icstc.2016.7877351

Cited by 13 publications

(13 citation statements)

References 9 publications

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“…The digital image is a multimedia component that plays an important role in presenting visual information. Image processing aims to improve image quality according to user needs [18]. Image processing techniques can produce other images or producing features from the input image.…”

Section: Digital Image Processingmentioning

confidence: 99%

“…Convolutional Neural Network (CNN) is developed based on the multilayer layer perceptron (MLP) which is designed or intended for processing two-dimensional data in the form of images. CNN is a variant of the deep neural network because of its high network depth and is widely applied to complex image data [18]. The image classification process can basically only use MLP, but one of the weaknesses of this MLP method is that it is not suitable because it cannot store spatial information from the mind data and each pixel is considered an independent or large feature, thus allowing to get unfavorable results [19].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

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Detection of Cataract Based on Image Features Using Convolutional Neural Networks

Weni

Utomo

Hutabarat

et al. 2021

Indonesian J. Comput. Cybern. Syst.

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Cataract are the highest cause of blindness that there are 32.4 million people experiencing blindness and as many as 191 million people experiencing visual disabilities in 2010 in the world. On the other hand, the longer a patient suffers from cataracts or late treatment. The development of cataract identification using a traditional algorithm based on feature representation is highly dependent on the classification process carried out by an eye specialist so that the method is prone to misclassification of a person detected or not. However, at this time there is a deep learning, convolutional neural network (CNN) which is used for pattern recognition which can help automate image classification. This research was conducted to increase the accuracy value and minimize data loss in the process of cataract identification by performing an experience namely the manipulation process was carried out by changing epochs. The results of this study indicate that the addition of epochs affects accuracy and loss data from CNN. By comparing variety of epoch values it can be ignored that the higher the age values used, the higher the value of the model. In this study, using the epoch 50 value reached the highest value with a value of 95%. Based on the model that has been made it has also been successful to receive images according to the specified class. After testing accurately, 10 images achieved an average accuracy of 88%.

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Section: Digital Image Processingmentioning

confidence: 99%

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Detection of Cataract Based on Image Features Using Convolutional Neural Networks

Weni

Utomo

Hutabarat

et al. 2021

Indonesian J. Comput. Cybern. Syst.

View full text Add to dashboard Cite

show abstract

“…The convolution operation starts from calculating of weight value for matrix of Gaussian kernel. Calculation of matrix weight Gaussian kernel is obtained from the Gaussian distribution function, as in the following equation [1]: (2) Where:…”

Section: 1 Noise Removing With Gaussian Blurmentioning

confidence: 99%

“…This feature extraction process can be done with Convolutional Neural Network (CNN). Previous research was able to extract the classification features of salak fruit with accuracy value is 70.7% for four class classification model and 81.45% for two class classification model [1]. Previous researchers stated that the accuracy value can increased.…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Convolutional Neural Network Accuracy on Salak Classification Based Quality on Digital Image

Dzulqarnain¹,

Suprapto²,

Makhrus³

2019

Indonesian J. Comput. Cybern. Syst.

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Salak is a seasonal fruit that has high export value. The success of salak fruit exported is influence by selection process, but there is still a problem in it. The selection of salak still done manually and potentially misclassified. Research to automate the selection of salak fruit has been done before. The process of selection this salak fruits used convolutional neural network (CNN) based on image of salak fruits. The resulting of accuracy value from previous research is 70.7% for four class classification model and 81.45% for two class classification model. This research was conducted to increase accuracy value the classification of salak exported based on previous research. Accuracy improvement by changing the noise removal process to produce a better image. The changing also occur in the CNN architecture that layer convolution is more deep and with additional parameters such as Stride, Zero Padding, and Adam Optimizer. This change hopefully can increase the accuracy value of the salak classification. The results showed an accuracy value increased 22.72% from 70.70% to 93.42% for the category of four classes CNN models and increased 13,29% from 81.45% to 94.74% for category two classes.

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Orange Fruit Images Classification using Convolutional Neural Networks

Asriny

Rani

Hidayatullah

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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The quality of fruit is important to increase sales in the market. Right now, the quality selection of orange fruit is mostly still complete by humans. Several drawbacks such as inaccuracy and inconsistency in results happened due to the limitation of human perceptions. The development of computer vision, making it possible to train the computer to classify images based on specified characteristics. This paper proposes the classification model to classify orange images using Convolutional Neural Network (CNN). Five classes of orange namely good-orange-grade-1, good-orange-grade-2, immature-orange, rotten-orange, and damaged-orange are classified using deep learning CNN. Total of 1000 orange images is collected using the smartphone camera. Each class consists of 200 images which are divided into 60% as the training data, 20% as the validation data, and 20% as the testing data. K-Fold Cross-Validation method is used to validate the model. In this paper, the hidden layer of CNN consists of 256 nodes. Two activation functions, ReLU and Tanh, are employed for comparing the accuracy of classification with the Softmax classifier. The result shows that the accuracy of ReLU activation function is 96%, which is better than the Tanh activation function that gives only 93,8%.

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Convolutional Neural Network implementation for image-based Salak sortation

Cited by 13 publications

References 9 publications

Detection of Cataract Based on Image Features Using Convolutional Neural Networks

Detection of Cataract Based on Image Features Using Convolutional Neural Networks

Improvement of Convolutional Neural Network Accuracy on Salak Classification Based Quality on Digital Image

Orange Fruit Images Classification using Convolutional Neural Networks

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