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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Journal of Applied Data Sciences
Normi Sham Awang Abu Bakar
International Islamic University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Polarity classification tool for sentiment analysis in Malay language Normi Sham Awang Abu Bakar; Ros Aziehan Rahmat; Umar Faruq Othman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (315.723 KB) | DOI: 10.11591/ijai.v8.i3.pp259-263

Abstract

The popularity of the social media channels has increased the interest among researchers in the sentiment analysis (SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool (MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data.
Test First Model for Application in the Academic Setting Normi Sham Awang Abu Bakar; Norzariyah Yahya
Bulletin of Electrical Engineering and Informatics Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (324.397 KB) | DOI: 10.11591/eei.v7i1.897

Abstract

This research elaborates the selection of the Test First and Test Last model for a pilot experiment that was executed as a feasibility study to validate the suitability of the existing Test First model for its implementation in the series of actual experiment. The series of actual experiment is designed to investigate the quality of the project developed by the students in higher educational institution with the Test First over Test Last model. The findings gathered from the pilot experiment demonstrate that there were misunderstandings on the user stories among the participants that have led to the development of an enhanced Test First model.
Test First Model for Application in the Academic Setting Normi Sham Awang Abu Bakar; Norzariyah Yahya
Bulletin of Electrical Engineering and Informatics Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (324.397 KB) | DOI: 10.11591/eei.v7i1.897

Abstract

This research elaborates the selection of the Test First and Test Last model for a pilot experiment that was executed as a feasibility study to validate the suitability of the existing Test First model for its implementation in the series of actual experiment. The series of actual experiment is designed to investigate the quality of the project developed by the students in higher educational institution with the Test First over Test Last model. The findings gathered from the pilot experiment demonstrate that there were misunderstandings on the user stories among the participants that have led to the development of an enhanced Test First model.
Test First Model for Application in the Academic Setting Normi Sham Awang Abu Bakar; Norzariyah Yahya
Bulletin of Electrical Engineering and Informatics Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (324.397 KB) | DOI: 10.11591/eei.v7i1.897

Abstract

This research elaborates the selection of the Test First and Test Last model for a pilot experiment that was executed as a feasibility study to validate the suitability of the existing Test First model for its implementation in the series of actual experiment. The series of actual experiment is designed to investigate the quality of the project developed by the students in higher educational institution with the Test First over Test Last model. The findings gathered from the pilot experiment demonstrate that there were misunderstandings on the user stories among the participants that have led to the development of an enhanced Test First model.
The Determinant Factors For The Issuance Of Central Bank Digital Currency (CBDC) In Malaysia Using Machine Learning Framework Normi Sham Awang Abu Bakar; Norzariyah Yahya; Norbik Bashah Idris; Engku Rabiah Adawiah Engku Ali; Jasni Mohamad Zain; Erni Eliana Khairuddin; Ahmad Firdaus Zainal Abidin; Sheikh Mohammad Tahsin Murtaj; Siti Sarah Maidin
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.176

Abstract

In order to identify the factors influencing the establishment of the Centre Bank Digital Currency (CBDC) in Malaysia, this study leverages the machine-learning technique to determine the most critical factors leading to CBDC issuance in Malaysia. The overall Central Bank Digital Currency Project Index (CBDCPI) was selected as a target variable,while two machine learning algorithms, Random Forest and XGBoost were used to identify the determining variables. The accuracy obtained through the Random Forest is 83% and subsequently, 80% in XGBoost. This study explored a new research frontier by creating two machine-learning models that treated retail and wholesale CBDCPI as target variables. The data used in the process are gathered from various official sources such as the Bank for International Settlements (BIS), the International Monetary Fund (IMF), and the World Bank. The Circulation of Cash, Prevalence of Cryptocurrencies, Effect of CBDC on International Trade, the Search Interest, Financial Development Index, Innovation Value, and Trade Openness are some of the most critical factors determining whether CBDC will be issued in Malaysia. Generally, are identified as important factors determining whether CBDC will be issued in Malaysia. Eventually, the factors identified will be used to develop a framework for the implementation of CBDC in Malaysia.
Co-Authors Ahmad Firdaus Zainal Abidin Engku Rabiah Adawiah Engku Ali Erni Eliana Khairuddin Jasni Mohamad Zain Norbik Bashah Idris Norzariyah Yahya Norzariyah Yahya Ros Aziehan Rahmat Sheikh Mohammad Tahsin Murtaj Siti Sarah Maidin Umar Faruq Othman