Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Al-Sawaff, Zaid Husham
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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

Found 2 Documents
Search

 1 
Performance Comparison of Random Forest, Support Vector Machine and Neural Network in Health Classification of Stroke Patients Sari, Windy Junita; Melyani, Nasya Amirah; Arrazak, Fadlan; Anahar, Muhammad Asyraf Bin; Addini, Ezza; Al-Sawaff, Zaid Husham; Manickam, Selvakumar
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1119

Abstract

Stroke is the second most common cause of death globally, making up about 11% of all deaths from health-related deaths each year, the condition varies from mild to severe, with the potential for permanent or temporary damage, caused by non-traumatic cerebral circulatory disorders. This research began with data understanding through the acquisition of a stroke patient health dataset from Kaggle, consisting of 5110 records. The pre-processing stage involved transforming the data to optimize processing, converting numeric attributes to nominal, and preparing training and test data. The focus then shifted to stroke disease classification using Random Forest, Support Vector Machines, and Neural Networks algorithms. Data processing results from the Kaggle dataset showed high performance, with Random Forest achieving 98.58% accuracy, SVM 94.11%, and Neural Network 95.72%. Although SVM has the highest recall (99.41%), while Random Forest and ANN have high but slightly lower recall rates, 98.58% and 95.72% respectively. Model selection depends on the needs of the application, either focusing on precision, recall, or a balance of both. This research contributes to further understanding of stroke diagnosis and introduces new potential for classifying the disease.
Comparative Analysis of Weather Image Classification Using CNN Algorithm with InceptionV3, DenseNet169 and NASNetMobile Architecture Models Wulandari, Vina; Sari, Windy Junita; Al-Sawaff, Zaid Husham; Manickam, Selvakumar
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1608

Abstract

Rapid weather changes have a significant impact on various aspects of human life, including social and economic development. Weather analysis traditionally relies on data from Doppler radar, weather satellites, and weather balloons. However, advancements in computer vision technology provide new opportunities to enhance weather prediction systems through image recognition and classification. Studies evaluating and comparing deep learning architectures for weather image classification remain limited.This research utilizes Convolutional Neural Networks (CNN) to classify weather images using three architectures: InceptionV3, DenseNet169, and NASNetMobile. The results show that InceptionV3 achieved 97.94% accuracy on training data, 92.34% on validation data, and 93.81% on test data. DenseNet169 achieved 98.09% accuracy on training data, 88.46% on validation data, and 92.33% on test data. NASNetMobile achieved 96.51% accuracy on training data, 87.82% on validation data, and 89.97% on test data. Based on these results, InceptionV3 is the optimal choice for weather classification due to its consistent performance.This research addresses the gap in evaluating CNN architectures for weather data and contributes to improving weather monitoring systems, early disaster warnings, and applications reliant on accurate predictions. These findings also provide a foundation for the development of advanced technologies in image analysis and weather forecasting in the future.
Co-Authors Addini, Ezza Anahar, Muhammad Asyraf Bin Arrazak, Fadlan Manickam, Selvakumar Melyani, Nasya Amirah Sari, Windy Junita Wulandari, Vina