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All Journal International Journal of Advances in Applied Sciences JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Ibrahim, Zaidah
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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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.
An Assessment Algorithm for Indoor Evacuation Model Samah, Khyrina Airin Fariza Abu; Halim, Amir Haikal Abdul; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.933

Abstract

The public buildings increased significantly with the economy's growth and the population's advancement. The complexity of the indoor layout and the involvement of many people cause the indoor evacuation wayfinding to the nearest exit to be more challenging during emergencies such as fire. In order to overcome the problem, each building is compulsory to follow the standard evacuation preparedness required by Uniform Building By-Law (UBBL). Researchers have also developed evacuation models to help evacuees evacuate safely during the evacuation from a building. However, building owners do not know which evacuation model is suitable for implementing the chosen high-rise building. Two problems were identified in choosing a suitable evacuation model during the decision-making process. First, many developed evacuation models focus on studying different features of evacuation behavior and evacuation time. Second, the validation and comparison of the evacuation model is the missing process before applying the suitable evacuation model. Both validation and comparison procedures were made independently without any standard assessment that encapsulates the critical incident features during the indoor evacuation and virtual spatial elements. Therefore, this research proposed an indoor evacuation assessment algorithm to solve the problem. The assessment algorithm refers to the elements developed in our previous study. We determined attributes, executed simulations, and evaluated the cluster performance using the developed framework. The outcome can help the building owners assess which suitable existing evacuation model is the best to implement at the chosen building.
Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine Eko Minarno, Agus; Setiyo Kantomo, Ilham; Setiawan Sumadi, Fauzi Dwi; Adi Nugroho, Hanung; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.991

Abstract

The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions. Among the most prevalent diseases of the brain is the development of aberrant tissue in brain cells, which results in the formation of brain tumors. According to data from the International Agency for Research on Cancer (IARC), more than 124,000 people worldwide were diagnosed with brain tumors in 2014, and more than 97,000 people died due to the condition. Current research indicates that magnetic resonance imaging (MRI) is the most effective means of detecting brain cancers. Because brain tumors are associated with significant mortality risk, a large number of brain tumor MRI imaging datasets were used in this research to detect brain cancers using deep learning techniques. To classify three forms of brain tumors, including glioma, meningioma, and pituitary, a deep learning model called DenseNet 201 paired with Support Vector Machines (SVM) was employed in this work included three types of brain tumors. Based on the results of the tests that were conducted, the best accuracy results obtained in this study were 99.65 percent, with a comparison ratio of 80 percent for training data and 20 percent for testing data, oversampled with the SMOTE method, with the best accuracy results obtained in this study being 99.65 percent.
Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network Minarno, Agus Eko; Cokro Mandiri, Mochammad Hazmi; Azhar, Yufis; Bimantoro, Fitri; Nugroho, Hanung Adi; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.857

Abstract

Diabetic Retinopathy (DR) is a disease that causes visual impairment and blindness in patients with it. Diabetic Retinopathy disease appears characterized by a condition of swelling and leakage in the blood vessels located at the back of the retina of the eye. Early detection through the retinal fundus image of the eye could take time and requires an experienced ophthalmologist. This study proposed a deep learning method, the Efficientnet-b7 model to identify diabetic retinopathy disease automatically. This study applies three preprocessing techniques that could be implemented in the dataset "APTOS 2019 Blindness Detection". In preprocessing technique trial scenarios, Usuyama preprocessing technique obtained the best results with accuracy of 89% of train data and 84% in test data compared to Harikrishnan preprocessing technique which has 82% accuracy in test data, and Ben Graham preprocessing has 81% accuracy in test data. In this study, Hyperparameter tuning was conducted to find the best parameters for use on the EfficientNet-B7 Model. In this study, we tested the Efficientnet-B7 model with an augmentation process that can reduce the occurrence of overfitting compared to models without augmentation. Preprocessing techniques and augmentation techniques can influence the proposed EfficientNet-B7 model in terms of performance results and reduce the overfitting of models.
Co-Authors Agus Eko Minarno Cokro Mandiri, Mochammad Hazmi Fitri Bimantoro Halim, Amir Haikal Abdul Hanung Adi Nugroho Kasiran, Zolidah Mat Diah, Norizan Muhaimin Lim, Danishah Hana Muhammad Samah, Khyrina Airin Fariza Abu Setiyo Kantomo, Ilham Sumadi, Fauzi Dwi Setiawan Yuda Munarko Yufis Azhar