scholarly journals Penerapan Algoritma Convolutional Neural Network dalam Klasifikasi Telur Ayam Fertil dan Infertil Berdasarkan Hasil Candling

2021 ◽  
Vol 5 (4) ◽  
pp. 563
Author(s):  
Muhammad Rizky Firdaus
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Blood Vessels ◽  
Prediction Errors ◽  
Network Method ◽  
Hatching Process ◽  
Egg Shell ◽  
Chicken Eggs ◽  
Selection Of

Fertile chicken eggs are eggs that can hatch because these eggs have a development in the form of dots of blood and blood vessels or can be called an embryo, while infertile chicken eggs are a type of egg that cannot be hatched because there is no embryo development in the hatching process. Inspection of infertile chicken eggs must be carried out especially for breeders who will carry out the selection and transfer of fertile chicken eggs and infertile chicken eggs. However, currently, the selection of fertile and infertile chicken eggs is still using a less effective way, namely only by looking at the egg shell or called candling, this process is certainly less accurate to classify which eggs are fertile and infertile eggs because not all breeders are able to see the results of the eggs properly. candling so that the possibility of prediction errors. Therefore, in this study, a classification of fertile chicken eggs and infertile chicken eggs will be carried out based on candling results using the Convolutional Neural Network method. From the results of the classification carried out, the percentage of accuracy obtained for the classification of fertile and infertile chicken eggs is 98% and an error of 5%.

scholarly journals Classification of Bidikmisi Scholarship Acceptance using Neural Network Based on Hybrid Method of Genetic Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 396-404
Author(s):  
N Cahyani ◽  
Sinta Septi Pangastuti ◽  
K Fithriasari ◽  
Irhamah Irhamah ◽  
N Iriawan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimal Number ◽  
Connection Weight ◽  
The Neural Network ◽  
Network Method ◽  
Input Variables ◽  
Human Brains ◽  
Selection Of

A Neural network is a series of algorithms that endeavours to recognize underlying relationships in a set of data through processes that mimic the way human brains operate. In the case of classification, this method can provide a fit model through various factors, such as the variety of the optimal number of hidden nodes, the variety of relevant input variables, and the selection of optimal connection weights. One popular method to achieve the optimal selection of connection weights is using a Genetic Algorithm (GA), the basic concept is to iterate over Darwin's evolution. This research presents the Neural Network method with the Backpropagation Neural Network (BPNN) and the combined method of BPNN with GA, where GA is used to initialize and optimize the connection weight of BPNN. Based on accuracy value, the BPNN method combined with GA provides better classification, which is 90.51%, in the case of Bidikmisi Scholarship classification in East Java.

scholarly journals Image Classification of Beef and Pork Using Convolutional Neural Network in Keras Framework

2021 ◽  
Vol 5 (02) ◽  
pp. 245-248
Author(s):  
Salsa Bila ◽  
Anwar Fitrianto ◽  
Bagus Sartono
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activation Function ◽  
Food Ingredient ◽  
Model Classification ◽  
Regularization Techniques ◽  
The Difference ◽  
Fully Connected ◽  
Selection Of

Beef is a food ingredient that has a high selling value. Such high prices make some people manipulate sales in markets or other shopping venues, such as mixing beef and pork. The difference between pork and beef is actually from the color and texture of the meat. However, many people do not understand these differences yet. In addition to socialization related to understanding the differences between the two types of meat, another solution is to create a technology that can recognize and differentiate pork and beef. That is what underlies this research to build a system that can classify the two types of meat. Convolutional Neural Network (CNN) is one of the Deep Learning methods and the development of Artificial Intelligence science that can be applied to classify images. Several regularization techniques include Dropout, L2, and Max-Norm were applied to the model and compared to obtain the best classification results and may predict new data accurately. It has known that the highest accuracy of 97.56% obtained from the CNN model by applying the Dropout technique using 0.7 supported by hyperparameters such as Adam's optimizer, 128 neurons in the fully connected layer, ReLu activation function, and 3 fully connected layers. The reason that also underlies the selection of the model is the low error rate of the model, which is only 0.111.Keywords: Beef and Pork, Model, Classification, CNN

scholarly journals Classification of surface water objects in visible spectrum images

2020 ◽  
pp. 87-95
Author(s):  
A. A. Artemyev ◽  
E. A. Kazachkov ◽  
S. N. Matyugin ◽  
V. V. Sharonov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Surface Water ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Visible Spectrum ◽  
Training Dataset ◽  
And Training ◽  
Selection Of

This paper considers the problem of classifying surface water objects, e.g. ships of different classes, in visible spectrum images using convolutional neural networks. A technique for forming a database of images of surface water objects and a special training dataset for creating a classification are presented. A method for forming and training of a convolutional neural network is described. The dependence of the probability of correct recognition on the number and variants of the selection of specific classes of surface water objects is analysed. The results of recognizing different sets of classes are presented.

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

2019 ◽  
Vol 13 (2) ◽  
pp. 189 ◽  
Author(s):  
Muhammad Faqih Dzulqarnain ◽  
Suprapto Suprapto ◽  
Faizal Makhrus
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Digital Image ◽  
Selection Process ◽  
Noise Removal ◽  
Classification Model ◽  
Accuracy Improvement ◽  
Zero Padding ◽  
Selection Of

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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