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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
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Articles 121 Documents
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Search results for , issue "Vol 13, No 3: September 2024" : 121 Documents clear
A mobile-optimized convolutional neural network approach for real-time batik pattern recognition Rosalina, Rosalina; Sahuri, Genta; Desriva, Hana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3018-3027

Abstract

This research focuses on preserving and sharing knowledge about Indonesian batik, a blend of art and technology symbolizing the nation's creativity. To address declining awareness of batik types, a mobile application is introduced for real-time recognition and classification of batik motifs. The goal is to maintain appreciation and understanding of this cultural heritage. Using the EfficientNet convolutional neural network (CNN) architecture, the study enhances model accuracy with effective scaling. A dataset of 1350 images representing 15 batik types supports robust model training and evaluation. Results demonstrate successful implementation, yielding an Android app capable of deep learning-based real-time recognition with an 83% accuracy rate. This innovation aims to empower users to identify and appreciate distinct batik types, ensuring cultural preservation for current and future generations.

Page 13 of 13 | Total Record : 121

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