Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal International Journal of Advances in Applied Sciences Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Ibrahim, Zaidah
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Image Retrieval Based on Texton Frequency-Inverse Image Frequency Azhar, Yufis; Minarno, Agus Eko; Munarko, Yuda; Ibrahim, Zaidah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 2, May 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (550.257 KB) | DOI: 10.22219/kinetik.v5i2.1026

Abstract

In image retrieval, the user hopes to find the desired image by entering another image as a query. In this paper, the approach used to find similarities between images is feature weighting, where between one feature with another feature has a different weight. Likewise, the same features in different images may have different weights. This approach is similar to the term weighting model that usually implemented in document retrieval, where the system will search for keywords from each document and then give different weights to each keyword. In this research, the method of weighting the TF-IIF (Texton Frequency-Inverse Image Frequency) method proposed, this method will extract critical features in an image based on the frequency of the appearance of texton in an image, and the appearance of the texton in another image. That is, the more often a texton appears in an image, and the less texton appears in another image, the higher the weight. The results obtained indicate that the proposed method can increase the value of precision by 7% compared to the previous method.
Betta fish species classification using light weight deep learning algorithm Muhaimin Lim, Danishah Hana Muhammad; Mat Diah, Norizan; Ibrahim, Zaidah; Kasiran, Zolidah
International Journal of Advances in Applied Sciences Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i1.pp28-38

Abstract

Betta fish sellers and breeders often face challenges in accurately identifying Betta fish species due to variations in colors, patterns, and shapes, leading to potential financial losses and deceptive transactions. To address this issue, we developed a mobile application that employs MobileNet, a deep learning (DL) technique, to classify Betta fish species. The dataset, acquired from online stores, comprises 400 images, with 100 images representing each of the four studied Betta fish species: comb tail, delta tail, spade tail, and veil tail. Prior to model implementation, the dataset undergoes pre-processing with data augmentation techniques, including rotation, shear, zoom-in, horizontal flip, and brightness adjustments, enhancing the model performance. Training utilizes 80% of the data, with the remaining 20% allocated for testing. Three distinct MobileNet models are developed for males, females, and both genders combined, achieving accuracies of 70, 83.75, and 65%, respectively. These trained models are the foundation for a mobile application developed for the Android platform that enables users, particularly Betta fish sellers, and breeders, to efficiently classify Betta fish species, empowering them to set accurate prices based on the identified species.
Co-Authors Agus Eko Minarno Kasiran, Zolidah Mat Diah, Norizan Muhaimin Lim, Danishah Hana Muhammad Yuda Munarko Yufis Azhar