Perbandingan Arsitektur Convolutional Neural Network Untuk Klasifikasi Fundus

Setiawan, Wahyudi

doi:10.21107/simantec.v7i2.6551

Cited by 22 publications

(17 citation statements)

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“…Penelitian terkait lainnya yang pernah dilakukan oleh Wahyudi Setiawan seperti klasifikasi citra fundus [12]. Citra uji coba menggunakan fundus retina.…”

Section: Pendahuluanunclassified

Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra

Lasniari

Jasril

Sanjaya

et al. 2022

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Beef is an example of an animal protein-rich food. The consumption of meat in Indonesia is increasing year after year, in tandem with the country's growing population. Many traders purposefully combine beef and pork in order to maximize profits. With the naked eye, it's difficult to tell the difference between pork and beef. In Muslim-majority countries, the assurance of halal meat is crucial. This study uses Deep Learning with the Convolutional Neural Network (CNN) method and ResNet-50 with data augmentation to classify images of beef and pork. The original meat picture databases contain 457 images, however following the data augmentation process, there are 2742 images in total, divided into three classes. The distribution of training and test data is 90 percent:10 percent in the comparison test scenario between the two original data schemes and supplemented data. With an average of 87.64 % accuracy, 87.59 % recall, and 90.90 % precision, the Confusion Matrix is the best classification performance model. There was no evidence of overfitting based on observations from the visualization of the training and testing process.

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“…Penelitian terkait lainnya yang pernah dilakukan oleh Wahyudi Setiawan seperti klasifikasi citra fundus [12]. Citra uji coba menggunakan fundus retina.…”

Section: Pendahuluanunclassified

Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra

Lasniari

Jasril

Sanjaya

et al. 2022

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“…Then, training is carried out using feed forward and backpropagation methods. The architecture of CNN is divided into two major parts, namely the Feature Extraction Layer and the Fully-Connected Layer (MLP) (Setiawan, 2019). The following is an illustration of the CNN architecture which can be seen in Figure 1 below.…”

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confidence: 99%

Performance of Deep Learning Inception Model and MobileNet Model on Gender Prediction Through Eye Image

Listio¹

2022

SinkrOn

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Convolutional neural network (CNN) is one of the neural networks used in image data. CNN has a good ability to detect objects in an image. This study discusses the comparison of two deep learning models based on convolutional neural network, namely the Inception-V3 method and the MobileNet method. Both algorithms are analyzed fairly on gender classification using eye images. There have been many research completions that have conducted studies on gender classification based on faces, but gender classification based on eyes has many challenges. This gender classification is grouped into two classes, namely male and female. This study aims to build a gender classification model from eye image. The processes in this research include selecting the dataset, preprocessing the data, dividing the data which is divided into training data and test data, modeling, and evaluating the performance of the model. This study uses a public dataset, where the data contains a total of 2,681 images consisting of 1251 male eyes and 1430 female eyes. This study concludes that gender classification using eye image using the Inception-V3 method is better than the MobileNet method. This is obtained based on the accuracy value generated by the Inception-V3 method which is higher than the MobileNet-V2 method which obtains an accuracy of 91.82%.

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“…Where the last number of this architecture represents the number of layers in the network. RESNET stands for Residual Network which is an artificial neural network innovation that won the 2015 ILSVRC classification competition with an error rate of only 3.15% [15]. While VGG-16 stands for Visual Geometry Group and 16 is the number of layers.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Music Genre Recommendations Based on Spectrogram Analysis Using Convolutional Neural Network Algorithm with RESNET-50 and VGG-16 Architecture

Purnama

2022

JISA

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Recommendations are a very useful tool in many industries. Recommendations provide the best selection of what the user wants and provide satisfaction compared to ordinary searches. In the music industry, recommendations are used to provide songs that have similarities in terms of genre or theme. There are various kinds of genres in the world of music, including pop, classic, reggae and others. With genre, the difference between one song and another can be heard clearly. This genre can be analyzed by spectrogram analysis. In this study, a spectrogram analysis was developed which will be the input feature for the Convolutional Neural Network. CNN will classify and provide song recommendations according to what the user wants. In addition, testing was carried out with two different architectures from CCN, namely VGG-16 and RESNET-50. From the results of the study obtained, the best accuracy results were obtained by the VGG-16 model with 20 epochs with accuracy 60%, compared to the RESNET-50 model with more than 20 epochs. The results of the recommendations generated on the test data obtained a good similarity value for VGG-16 compared to RESNET-50.

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Perbandingan Arsitektur Convolutional Neural Network Untuk Klasifikasi Fundus

Cited by 22 publications

References 0 publications

Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra

Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra

Performance of Deep Learning Inception Model and MobileNet Model on Gender Prediction Through Eye Image

Music Genre Recommendations Based on Spectrogram Analysis Using Convolutional Neural Network Algorithm with RESNET-50 and VGG-16 Architecture

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