Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal Compiler Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Aspirasi : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat Prosiding Seminar Nasional CORISINDO
Muhammad Faqih Dzulqarnain, Muhammad Faqih
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Public Health

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Compiler

 1 
Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder Dzulqarnain, Muhammad Faqih; Fadlil, Abdul; Riadi, Imam
Compiler Vol 13, No 2 (2024): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i2.2649

Abstract

This research investigates the development of model deep convolutional autoencoders to enhance the classification of digital batik images. The dataset used was sourced from Kaggle. The autoencoder was employed to enrich the image data prior to convolutional processing. By forcing the autoencoder to learn a lower-dimensional latent representation that captures the most salient features of the batik patterns. The performance of this enhanced model was compared against a standard convolutional neural network (CNN) without the autoencoder. Experimental results demonstrate that the incorporation of the autoencoder significantly improved the classification accuracy, achieving 99% accuracy on the testing data and loss value of 3.4%. This study highlights the potential of deep convolutional autoencoders as a powerful tool for augmenting image data and improving the performance of deep learning models in the context of batik image classification.
Co-Authors Abdul Fadlil Abdul Fadlil Imam Riadi Khelfa Arzakky Syukri Muhammad Muhammad Tegar Adji Nugroho Tole Sutikno