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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Samsiah Ahmad
Universiti Teknologi MARA
Electrical & Electronics Engineering

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Mobile development: learn du’a for early childhood learners Mohamed Imran Mohamed Ariff; Natasha Irina Khairul Annuar; Ahmad Farid Najmuddin; Ireen Munira Ibrahim; Noreen Izza Arshad; Samsiah Ahmad; Khairulliza Ahmad Salleh
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3860

Abstract

This project stands by acknowledging the use of mobile technology is progressively popular among early childhood learners in recent years. But, an extensive systematic literature search on educational mobile technology and touchscreen indicated that most of the applications are not suitable for early childhood learners as it lacks several multimedia features. Therefore, this project designates the development procedure of an educational mobile application for early childhood learners, who are very receptive to new knowledge and information. As a proof of concept, the development of this application utilizes the learning of the Islamic du’a recitation. This application was designed using the android studio and the flutter software combined with the java programming language. Upon the development of this application, several testings were done and analyzed. The result shows that users were satisfied with the applications as most of the testing scores were highly rated. Based on these results, the usage of this newly mobile application can be suggested to be adopted by early childhood learners.
Exploring public sentiments towards courier services in Malaysia through Twitter: a sentiment analysis approach Mohamed Imran Mohamed Ariff; Rasyidin Muhammad Zaharin Raja Salim; Azilawati Azizan; Samsiah Ahmad; Noreen Izza Arshad; Mohammad Nasir Abdullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1966-1973

Abstract

This study delves into the sentiment analysis of courier services in Malaysia through the lens of tweets. With the escalating reliance on courier services for daily parcel deliveries, understanding public sentiment becomes imperative. Through a systematic methodology encompassing data gathering, preparation, alteration, analysis, modeling, and deployment, this study aims to furnish valuable insights and recommendations. A dynamic graphical representation of sentiment scores is presented for intuitive interpretation and informed decision-making. The study's significance lies in empowering Malaysians to make judicious choices regarding courier services, thereby fostering transparency in the industry. The study proposes several avenues for future enhancements, including the integration of autocorrect features, diversifying data sources and languages, incorporating deep learning techniques, and developing a user-friendly interface for enhanced visualization. This study serves as a guiding resource, illuminating sentiments surrounding courier services in Malaysia and paving the way for future advancements in sentiment analysis within this domain.
Malaysian views on COVID-19 vaccination program: a sentiment analysis study using Twitter Mohamed Imran Mohamed Ariff; Nurul Erina Shuhada Zubir; Azilawati Azizan; Samsiah Ahmad; Noreen Izza Arshad
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6097

Abstract

This study aimed to analyze the opinions and emotions of Malaysians towards the COVID-19 vaccination program, as expressed on Twitter. By collecting data from the Twitter network and utilizing the machine learning life cycle technique. The results show that Malaysians have a mostly neutral viewpoint of the COVID-19 vaccination, with an accuracy score of 93%, an F1-score of 94%, a recall measurement of 94%, and a precision measure of 93%. These findings emphasize the significance of understanding public sentiment and perception towards crucial issues such as the COVID-19 vaccine and can be utilized to support healthcare professionals, policymakers, and the public in making informed decisions regarding the COVID-19 vaccination program.
Co-Authors Ahmad Farid Najmuddin Azilawati Azizan Ireen Munira Ibrahim Khairulliza Ahmad Salleh Mohamed Imran Mohamed Ariff Mohammad Nasir Abdullah Natasha Irina Khairul Annuar Noreen Izza Arshad Nurul Erina Shuhada Zubir Rasyidin Muhammad Zaharin Raja Salim