Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal SinarFe7
Hudaya, Citra
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
PENERAPAN ARSITEKTUR JST DALAM DEEP LEARNING UNTUK MENINGKATKAN AKURASI KLASIFIKASI GAMBAR DENGAN AUTOENCODER: Bahasa Indonesia Hudaya, Citra; Gunawan, Ardiansyah; Wijaya Tri, Bintar
SinarFe7 Vol. 7 No. 1 (2025): SinarFe7-7 2025
Publisher : FORTEI Regional VII Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract - The development of artificial intelligence technology, particularly deep learning, has made significant contributions to digital image processing across various fields such as medicine, security, and manufacturing industries. This study aims to implement the autoencoder method within an Artificial Neural Network (ANN) architecture to optimally enhance image classification accuracy. The autoencoder is employed as an unsupervised learning technique to extract essential and relevant features from input images before passing them to the classification layer. The training process was carried out using a carefully curated image dataset, and the model was evaluated to measure classification performance based on accuracy, precision, and recall. The experimental results show that integrating an autoencoder into the ANN architecture can improve feature extraction efficiency, reduce noise, and deliver more accurate and consistent classification results compared to conventional approaches. This research demonstrates that the autoencoder can serve as a vital component in modern deep learning-based classification systems.
Co-Authors Gunawan, Ardiansyah Wijaya Tri, Bintar