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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Khyrina Airin Fariza Abu Samah
Universiti Teknologi MARA Melaka Branch
Computer Science & IT Electrical & Electronics Engineering

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Malaysian fibre internet service provider: a naïve Bayes classification Twitter sentiment analysis Khyrina Airin Fariza Abu Samah; Muhamad Nabil Fahruddin; Raseeda Hamzah; Lala Septem Riza; Khairul Nurmazianna Ismail; Rosniza Roslan; Raihah Aminuddin; Nor Intan Shafini Nasaruddin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.25990

Abstract

In the highly competitive landscape of Malaysian internet service providers (ISPs), users seek efficient ways to assess service quality. While various websites allow visual comparisons of fiber ISPs, a direct side-by-side evaluation remains elusive. A survey of 101 respondents revealed that 92.1% found researching a company’s reputation time-consuming. Additionally, relying on English-centric online ratings may lead to skewed outcomes, disregarding reviews in diverse languages. In response, we developed a web-based dashboard utilizing Twitter sentiment analysis (SA) and the naïve Bayes (NB) algorithm to classify Malaysia’s best fiber ISPs. The SA focused on four key factors: package price, internet speed, coverage area, and customer service, simplifying the comparison process. The system’s usability and functionality tests showed that both the English and Malay models could classify scraped Twitter data with an accuracy of 80%. The system’s remarkable usability score of 94.58% on the system usability scale (SUS) confirms its acceptability and excellent performance in achieving research goals.
Comparative analysis of ensemble learning algorithms in enhanced confidence-based assessments Nur Maisarah Nor Azharludin; Khyrina Airin Fariza Abu Samah; Mohamad Faiz Dzulkalnine; Ahmad Firdaus Ahmad Fadzil; Mohd Nor Hajar Hasrol Jono; Lala Septem Riza
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9935

Abstract

This paper provides a comparative analysis of ensemble learning (EL)algorithms to enhance the confidence-based assessment (CBA) in evaluating student performance. Traditional CBA often suffers from misclassification caused by overconfidence and underconfidence, limiting its accuracy and fairness. To address these challenges, an enhanced CBA-EL model integrating bagging and boosting ensemble algorithms is proposed. Five bagging algorithms, which are random forest (RF), decision tree (DT)support vector machine (SVM), K-nearest neighbors (KNN), Naïve Bayes(NB), and four boosting algorithms, which are adaptive boosting(AdaBoost), eXtreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were evaluated using a dataset of 276 responses collected from Pre- and Post-Quiz CBA in a discrete structures course. Algorithm performance was evaluated using accuracy, correlation, weighted mean precision (WMP), and weighted mean recall (WMR). RF achieved 73.19% accuracy, 0.725 correlation, 0.751 WMP, and 0.766 WMR, while CatBoost outperformed all with 86.23% accuracy and the highest correlation, WMP, and WMR values, with 0.842, 0.843, and 0.862, respectively. The findings indicate that integrating EL into CBA improves prediction accuracy and supports bias-aware student evaluation. This research advances reliable assessment practices and informs the development of adaptive learning systems.
Co-Authors Ahmad Firdaus Ahmad Fadzil Khairul Nurmazianna Ismail Lala Septem Riza Mohamad Faiz Dzulkalnine Mohd Nor Hajar Hasrol Jono Muhamad Nabil Fahruddin Nor Intan Shafini Nasaruddin Nur Maisarah Nor Azharludin Raihah Aminuddin Raseeda Hamzah Rosniza Roslan