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In an ordinary artificial neural network, for example, yl is the output value of a layer | and zl is the input value at layer | where WI and Dl are the weight and bias of layer 1, with units then the calculation of the feedforward process using the activation function f can be done on Equations 4 and 5 [25]. Meanwhile, in a network that implements the dropout technique, the variable rl represents the vector along j which stores the value obtained from the Bernoulli distribution. The feedforward process is carried out in Equations 6, 7, and 8.

Figure 6 In an ordinary artificial neural network, for example, yl is the output value of a layer | and zl is the input value at layer | where WI and Dl are the weight and bias of layer 1, with units then the calculation of the feedforward process using the activation function f can be done on Equations 4 and 5 [25]. Meanwhile, in a network that implements the dropout technique, the variable rl represents the vector along j which stores the value obtained from the Bernoulli distribution. The feedforward process is carried out in Equations 6, 7, and 8.

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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. Figure 1 shows the steps in digital image processing which consists of: Image acquisition, the first step in image processing is image acquisition, which is the process of capturing or taking the required image using imaging sensors such as cameras, scanners, and others.
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. Figure 1 shows the steps in digital image processing which consists of: Image acquisition, the first step in image processing is image acquisition, which is the process of capturing or taking the required image using imaging sensors such as cameras, scanners, and others.
ISSN (print): 1978-1520, ISSN (online): 2460-7258 Figure 2 Research Design with CNN Methods
ISSN (print): 1978-1520, ISSN (online): 2460-7258 Figure 2 Research Design with CNN Methods
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]. the bac The CNN method is the same as other neural network theories which are kpropagation algorithm. CNN is designed to recognize visual patterns d rained using irectly from image pixels by minimizing preprocessing. CNN can recognize patterns with a wide variety, resistan to distortion and simple geometric transformations. The architecture of CNN is divided into 2 major parts, namely the Feature Extraction Layer and the Fully-Connected Layer [20]. Each stage consists of three layers, namely the convolutional layer, the layer activation function, and the pooling layer, which can be seen in Figure 3 which is the CNN network architecture.
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]. the bac The CNN method is the same as other neural network theories which are kpropagation algorithm. CNN is designed to recognize visual patterns d rained using irectly from image pixels by minimizing preprocessing. CNN can recognize patterns with a wide variety, resistan to distortion and simple geometric transformations. The architecture of CNN is divided into 2 major parts, namely the Feature Extraction Layer and the Fully-Connected Layer [20]. Each stage consists of three layers, namely the convolutional layer, the layer activation function, and the pooling layer, which can be seen in Figure 3 which is the CNN network architecture.
After calculating the activation function, pooling operations are carried out by reducing size of the matrix using max-pooling or average-pooling. The output from the pooling operation is a matrix with smaller dimensions compared to the initial image[22]. The convolution and pooling process is carried out to obtain the desired feature map to be input into a fully connected layer [24]. The pooling illustration is shown in Figure 4, namely pooling by max-pooling.
After calculating the activation function, pooling operations are carried out by reducing size of the matrix using max-pooling or average-pooling. The output from the pooling operation is a matrix with smaller dimensions compared to the initial image[22]. The convolution and pooling process is carried out to obtain the desired feature map to be input into a fully connected layer [24]. The pooling illustration is shown in Figure 4, namely pooling by max-pooling.
Dropout is a neural network regulation technique to select several neuron randomly and not to be used during the training process, in other words, these neuron are randomly discarded [25]. This means that the contribution of the discarded neuron: will be temporarily stopped by the network and the new weight is also not applied to thi neurons at the time of backpropagation. Following the dropout process can be seen 11 Figure 5.
Dropout is a neural network regulation technique to select several neuron randomly and not to be used during the training process, in other words, these neuron are randomly discarded [25]. This means that the contribution of the discarded neuron: will be temporarily stopped by the network and the new weight is also not applied to thi neurons at the time of backpropagation. Following the dropout process can be seen 11 Figure 5.
images as validation data, and 99 images as testing data. Each of these sections has been divided into 2 classes, namely normal or cataract classes.
images as validation data, and 99 images as testing data. Each of these sections has been divided into 2 classes, namely normal or cataract classes.
Learning rate is one of the hyperparameters that greatly affects the performance of a CNN model. In searching for the right level of learning, a method called cyclic learning rates, in this method, training will be carried out several times, with a learning speed starting from a small value, and each iteration the learning level will be enlarged, each iteration will be seen from the losses obtained, and if the loss obtained increases drastically, the search process will be stopped. Based on data losses are obtained, then the level of learning chosen at any time before the loss has the lowest value. This research using a learning rate with a value of 5, 10, 20, and 50. It is shown in Figure 8,9,10,11 and 12 that doing experience by using variations in the number of epochs in learning, it appears that increasing the number of epochs greatly affects getting good accuracy.
Learning rate is one of the hyperparameters that greatly affects the performance of a CNN model. In searching for the right level of learning, a method called cyclic learning rates, in this method, training will be carried out several times, with a learning speed starting from a small value, and each iteration the learning level will be enlarged, each iteration will be seen from the losses obtained, and if the loss obtained increases drastically, the search process will be stopped. Based on data losses are obtained, then the level of learning chosen at any time before the loss has the lowest value. This research using a learning rate with a value of 5, 10, 20, and 50. It is shown in Figure 8,9,10,11 and 12 that doing experience by using variations in the number of epochs in learning, it appears that increasing the number of epochs greatly affects getting good accuracy.
3.4 Result of Cataract identification
3.4 Result of Cataract identification
Based on the results of the training with a variety of epoch, it can be seen in the yllowing Table 1:
Based on the results of the training with a variety of epoch, it can be seen in the yllowing Table 1:
Based on the table above, it can be seen that the more epoch values used, the better the accuracy results from image testing. Of course not only is this a determinant of the level of accuracy of the image test results but also many things such as the number of datasets, image dimension sizes, and others but in this study, the focus is on the learning rate value and the number of epochs. The prediction results from the model obtained on the tested dataset showed very good results, namely an average accuracy of 88% which can be seen in Table 2 as follows:
Based on the table above, it can be seen that the more epoch values used, the better the accuracy results from image testing. Of course not only is this a determinant of the level of accuracy of the image test results but also many things such as the number of datasets, image dimension sizes, and others but in this study, the focus is on the learning rate value and the number of epochs. The prediction results from the model obtained on the tested dataset showed very good results, namely an average accuracy of 88% which can be seen in Table 2 as follows:
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