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All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization IJIES (International Journal of Innovation in Enterprise System)
Hamdan, Hazlina
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Exploring COVID-19 vaccine sentiment: a Twitter-based analysis of text processing and machine learning approaches Khalaf, Ban Safir; Hamdan, Hazlina; Manshor, Noridayu
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7855

Abstract

In the wake of the 2020 coronavirus disease (COVID-19) pandemic, the swift development and deployment of vaccines marked a critical juncture, necessitating an understanding of public sentiments for effective health communication and policymaking. Social media platforms, especially Twitter, have emerged as rich sources for gauging public opinion. This study harnesses the power of natural language processing (NLP) and machine learning (ML) to delve into the sentiments and trends surrounding COVID-19 vaccination, utilizing a comprehensive Twitter dataset. Traditional research primarily focuses on ML algorithms, but this study brings to the forefront the underutilized potential of NLP in data preprocessing. By employing text frequency-inverse document frequency (TF-IDF) for text processing and long short-term memory (LSTM) for classification, the research evaluates six ML techniques K-nearest neighbors (KNN), decision trees (DT), random forest (RF), artificial neural networks (ANN), support vector machines (SVM), and LSTM. Our findings reveal that LSTM, particularly when combined with tweet text tokenization, stands out as the most effective approach. Furthermore, the study highlights the pivotal role of feature selection, showcasing how TF-IDF features significantly bolster the performance of SVM and LSTM, achieving an impressive accuracy exceeding 98%. These results underscore the potential of advanced NLP applications in real-world settings, paving the way for nuanced and effective analysis of public health discourse on social media.
Recent Advances on Meta-heuristic Algorithms for Training Multilayer Perceptron Neural Network Al-Asaady, Maher Talal; Aris, Teh Noranis Mohd; Sharef, Nurfadhlina Mohd; Hamdan, Hazlina
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3109

Abstract

Artificial Neural Networks (ANNs) have demonstrated applicability and effectiveness in several domains, including classification tasks. Significant emphasis has been given to the training techniques of ANNs in identifying appropriate weights and biases. Conventional training techniques such as Gradient Descent (GD) and Backpropagation (BP), while thorough, have several disadvantages such as early convergence, being highly dependent on the initial parameters, and quickly getting stuck in local optima. Conversely, meta-heuristic algorithms show great potential as effective approaches for training ANNs with high computational efficiency, high quality, and global search capabilities. The literature has proposed several such techniques; hence, this paper offers a thorough examination of current advancements in training a Multilayer Perceptron (MLP) neural network using meta-heuristic algorithms, with a focus on classification benchmark datasets. The study was conducted over a period of ten years, from the year 2014 to 2024. The research papers were specifically chosen from four widely used databases: ScienceDirect, Scopus, Springer, and IEEE Xplore. Through the use of a research methodology that incorporates specific criteria for including and excluding articles, and by thorough examination of more than 53 publications, we present a comprehensive study of meta-heuristic methods for training MLPs. Our main focus is on discovering trends across these tools. The analysis has been conducted utilizing relevant factors such as evaluation metrics for classification models, fitness functions, comparing approaches, datasets, and observed outcomes. The present work serves as a significant asset for researchers, facilitating the identification of suitable optimization methodologies for various application areas. 
Designing an Instrument to Conduct a Survey on Requirement Reuse Practices in Malaysia Tungadi, Adri Riawan; Che Pa, Noraini; Ali, Norhayati Md; Aris, Noranis Mohd; Atan, Rodziah; Ban, Ainita; Hamdan, Hazlina; Ariffin, Mohamed Hazrat
International Journal of Innovation in Enterprise System Vol. 3 No. 2 (2019): International Journal of Innovation in Enterprise System
Publisher : School of Industrial and System Engineering, Telkom University

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Abstract

A set of questionnaire was designed to study the current state of requirement reuse practices in Malaysia. This paper describes the design of the survey on requirement reuse practice instrument, and the assessment of its reliability and validity. Cronbach’s Alpha test was used to check on its reliability and respondents’ feedback, to assess the level of understanding, the level of difficulty in responding and the level of relevancy to the subject area including the duration taken to complete the questionnaire. Results from the feedback suggested three main issues that need to be looked upon as a way to improve the instrument design. Future efforts will focus on improving the structure and contents of the questionnaire in order to achieve higher reliability and better number of responses to study on requirement reuse. Keywords—requirement reuse, requirement, engineering, software development, survey design
Co-Authors Al-Asaady, Maher Talal Ali, Norhayati Md Ariffin, Mohamed Hazrat Aris, Noranis Mohd Aris, Teh Noranis Mohd Ban, Ainita Khalaf, Ban Safir Manshor, Noridayu Noraini Che Pa, Noraini Rodziah Atan, Rodziah Sharef, Nurfadhlina Mohd Tungadi, Adri Riawan