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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Manshor, Noridayu
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Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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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.
Cross-project software defect prediction through multiple learning Zakariyau Bala, Yahaya; Abdul Samat, Pathiah; Yatim Sharif, Khaironi; Manshor, Noridayu
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5258

Abstract

Cross-project defect prediction is a method that predicts defects in one software project by using the historical record of another software project. Due to distribution differences and the weak classifier used to build the prediction model, this method has poor prediction performance. Cross-project defect prediction may perform better if distribution differences are reduced, and an appropriate individual classifier is chosen. However, the prediction performance of individual classifiers may be affected in some way by their weaknesses. As a result, in order to boost the accuracy of cross-project defect prediction predictions, this study proposed a strategy that makes use of multiple classifiers and selects attributes that are similar to one another. The proposed method's efficacy was tested using the Relink and AEEEM datasets in an experiment. The findings of the experiments demonstrated that the proposed method produces superior outcomes. To further validate the method, we employed the Wilcoxon sum rank test at 95% significance level. The approach was found to perform significantly better than the baseline methods.
Co-Authors Abdul Samat, Pathiah Hamdan, Hazlina Khalaf, Ban Safir Mohamed, Raihani Norwati Mustapha Peizhi, Wang Yatim Sharif, Khaironi Zakariyau Bala, Yahaya