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Negara, Candra Putra
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Reconstruction Old Students Image Using The Autoencoder Method Negara, Candra Putra; Zaman, Azmi Badhi’uz; Setiawan, Dimas Aji; Azhari, Ahmad
Letters in Information Technology Education (LITE) Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um010v5i22022p51-54

Abstract

Image Processing is image processing with a digital computer to produce new images according to the user's wishes. One implementation is to reconstruct the image. Through the extraction stages are able to get the characteristics of an image. The algorithm used is Adam Optimization, which is an extension of the stochastic gradient reduction that has just seen wider adoption for deep learning applications in computer vision and natural language processing. In this study using the autoencoder technique, which is one variant of artificial neural networks that are generally used to "encode" data. Autoencoder is trained to be able to produce the same output as the input. This image reconstruction aims to process an image whose quality is not very clear to be clear. This if possible can be used to detect someone's face from a distance of photos. In reconstructing this image through the encode and decode process by defining Conv2D and Maxpool, it is processed into training with epoch 100 times while for the prediction process using Keras library. Then the last one gets an accuracy of 0,022. The final result is the output of the reconstructed image and calculation graph.
Reconstruction Old Students Image Using The Autoencoder Method Azhari, Ahmad; Negara, Candra Putra; Zaman, Azmi Badhi'uz; Setiawan, Dimas Aji
Letters in Information Technology Education (LITE) Vol 6, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um010v6i22023p1-5

Abstract

Image Processing is image processing with a digital computer to produce new images according to the user's wishes. One implementation is to reconstruct the image. Through the extraction stages can get the characteristics of an image. The algorithm used is Adam Optimization, an extension of the stochastic gradient reduction that has seen wider adoption for deep learning applications in computer vision and natural language processing. In this study, we use the autoencoder technique, one variant of artificial neural networks generally used to "encode" data. The autoencoder is trained to produce the same output as the input. This image reconstruction aims to process an image whose quality is not very clear, to be precise. This, if possible, can be used to detect someone's face from photos. In reconstructing this image through the encode and decode process by defining Conv2D and Maxpool, it is processed into training with epoch 100 times while for the prediction process using Keras library. Then, the last one gets an accuracy of 0,022. The result is the output of the reconstructed image and calculation graph
Co-Authors Ahmad Azhari Setiawan, Dimas Aji Zaman, Azmi Badhi'uz Zaman, Azmi Badhi’uz