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All Journal Proceeding Information Technology JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Intelligent Decision Support System (IDSS)
Ito Wasito
Universitas Pradita, Indonesia
Computer Science & IT

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Journal : JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI

 1 
Transfer Gaya Gambar Batik Menggunakan Neural Style Transfer dan Convolutional Autoencoder Celvyn Yulian; Handri Santoso; Ito Wasito; Haryono .
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.5573

Abstract

The challenge of neural networks to process visual art judgments like humans inspired Gatys et al and in 2015 they succeeded in creating neural style transfer (NST) that can transfer European artistic image styles to other images. At present, research related to NST has been widely conducted, but its use with a convolutional autoencoder (CAE) as one of the NN architectures capable of compressing NST output is still rare. This research intends to design an NST system with CAE as an additional architecture in charge of the compression process while maintaining the force transferred. As a substitute for European-style artistic images, batik is used as an original Indonesian artistic work. NST and compression images will be measured using structural similarity index measure (SSIM) evaluation metrics. The evaluation results showed that the system designed managed to get an average SSIM score of 0.67 out of 1 and an average value of storage size reduction ratio of 37.43% from the original size. Then, the survey showed that the quality of the compressed image was quite good with a score of 64.09% and the compressed image was quite usable in the field of work of each respondent with a score of 49.09%.
Sistem Pengenalan Emosi Menggunakan Autoencoder + CNN + Attention Mikhail Aresa Latumahina; Handri Santoso; Ito Wasito; Haryono .
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.5576

Abstract

In the Digital Transformation era, many businesses use technology in the form of Deep Learning which is used to change the way business is run, one of the methods used is Emotion Recognition. Emotion Recognition itself is part of Computer Vision, and computer vision tasks are usually done using the CNN algorithm. Accuracy is important in Emotion Recognition where many studies use various methods, both Transfer and Hybrid learning to try to improve this aspect, so this research intends to design a Autoencoder + CNN + Attention that can be used for Emotion recognition, which is made by combining Encoder, CNN, and Attention Mechanisms. this model is circumspect by using FER2013 and compared to the CNN + Attention model which is shutting down in the same way. Even though the Autoencoder + CNN + Attention managed to get 64% Accuracy in Evaluate Test_Model compared to CNN + Attention which got 55%, it should be noted that adjustments still have to be treated because of the 43% sensitivity of testing on external data such as tuning, layer adjustments, and FER2013 data augmentation.
Co-Authors Celvyn Yulian Handri Santoso Handri Santoso Haryono . Haryono Haryono Ionia Veritawati Mikhail Aresa Latumahina Mujiono Sadikin Willy Willy