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All Journal International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics
Baharun, Norhayati
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Education Electrical & Electronics Engineering

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Genetic programming in machine learning based on the evaluation of house affordability classification Masrom, Suraya; Baharun, Norhayati; Mohamad Razi, Nor Faezah; Abd Rahman, Abdullah Sani; Mohammad, Nor Hazlina; Sarkam, Nor Aslily
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7594

Abstract

One of the big challenges in machine learning is difficulty of achieving high accuracy in a short completion time. A more difficulties appeared when the algorithm needs to be used for solving real dataset from the survey-based data collection. Imbalance dataset, insufficient strength of correlations, and outliers are common problems in real dataset. To accelerate the modelling processes, automated machine learning based on meta-heuristics optimization such as genetic programming (GP) has started to emerge and is gaining popularity. However, identifying the best hyper-parameters of the meta-heuristics’ algorithm is the critical issue. This paper demonstrates the evaluation of GP hyper-parameters in modeling machine learning on house affordability dataset. The important hyper-parameters of GP are population size (PS), that has been observed with different setting in this research. The machine learning with GP was used to predict house affordability among employers with transport expenditure and job mobility as some of the attributes. The results from testing that run on hold-out samples show that GP machine learning can reach to 70% accuracy with split ratio 0.2 and GP PS 30. This research contributes to the advancement of automated machine learning techniques, offering potential for faster and more accurate real survey-based datasets.
Efficiency in academic evaluation: data envelopment analysis systematic review Razi, Nor Faezah Mohamad; Baharun, Norhayati; Masrom, Suraya
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i4.26956

Abstract

Education 5.0@UiTM is transforming Universiti Teknologi MARA (UiTM). This new approach emphasizes flexible learning paths that teach life and cross-disciplinary skills. To make higher education future-ready, universities must adapt fast to the significant global and technical changes caused by the 4th Industrial Revolution and transition from content-based to individualized learning. To ensure students meet Education 5.0@UiTM standards and corporate needs, the evaluation process must be reviewed. Competency-based academic achievement evaluation outperforms grades. This assessment is rarely systematically analyzed. This study was conducted to thoroughly explore the selection of input and output factors in determining student achievement efficiency using the data envelopment analysis (DEA) approach. The study reviewed publications from Scopus, Google Scholar, and Science Direct and found six themes for input variables: human resources, facilities condition and equipment, finance, curriculum, students characteristics, and community resources. output variables included student achievement, satisfaction, graduation rate, employment, research, and community resources. In DEA analysis, input and output selection affects scope, aim, production frontier, efficiency scores, accuracy and completeness. So, characterizing student achievement requires choosing relevant input and output variables. This study can enhance student evaluation processes to prepare college graduates for the next industrial revolution.
Evaluating digital competency among statistical educators: a comparative analysis of input-oriented DEA models Faezah Mohamad Razi, Nor; A. Wahab, Jufiza; Zafirah Azmi, Anis; Baharun, Norhayati; Masrom, Suraya; Aslily Sarkam, Nor
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.8106

Abstract

As the educational landscape shifts towards online learning, assessing educators' digital competencies has become crucial. This study aims to evaluate the digital competencies of university educators using data envelopment analysis (DEA), specifically comparing the banker, charnes, and cooper (BCC) input-oriented models (super efficiency and Bi-O multi-criteria data envelopment analysis (MCDEA) super efficiency BCC models). The research was conducted in three phases. Initially, the BCC model assessed educators' digital competencies. Subsequently, the Bi-O MCDEA model evaluated these competencies within an online learning context. Finally, the effectiveness of the two models was compared. Data was collected through a survey administered to 30 educators from Universiti Teknologi MARA, with a response rate of 75%. Results showed that while the BCC model identified 23 out of 30 educators as efficient, the Bi-O MCDEA model recognized only two as efficient. This discrepancy highlights the different stringencies of the models and their impact on assessing digital competencies. The super efficiency (SE) model was then used to rank the efficient educators to determine the most proficient. The study underscores the need for precise assessment tools in online education to enhance digital competencies effectively. It suggests that integrating advanced DEA models can significantly improve the identification and training of educators, thereby enriching the educational outcomes in digital environments.
Enhancing teachers’ digital literacy for security: a systematic review of frameworks and analytical methods in education Othman, Nadirah; Sarkam, Nor Aslily; Baharun, Norhayati; Hoon, Teoh Sian; Masrom, Suraya; Mohamad Razi, Nor Faezah; Abd Rahman, Abdullah Sani
International Journal of Evaluation and Research in Education (IJERE) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v14i6.32613

Abstract

The rising use of digital tools in education emphasizes the crucial need of teachers’ digital security, which relies on strong digital literacy. This study assesses teachers’ digital literacy on digital security literature to meet the urgent need for safe practices in schools due to increased security breaches. A total of 30 studies were reviewed using preferred reporting items for systematic reviews and meta-analyses (PRISMA) criteria to build frameworks and data analysis methodologies in this field. Five research areas were identified: teacher perspectives, security-related issues, educational impacts, pedagogical approaches, and instrument validation. The predominant framework used was the digital competence framework for citizens (DigComp), however hybrid frameworks that integrate other theoretical perspectives were highly commended for their comprehensive approach. The 30% of the studies focused on security issues, including cyberbullying and data protection, while 70% incorporated security dimensions into digital literacy frameworks. Quantitative approaches comprising 60%, including t-tests, analysis of variance (ANOVA), and regression analysis. Structural equation modeling (SEM) was used in several studies to examine complex relationships. Although current research predominantly emphasizes quantitative methods, future investigations could enhance knowledge of teachers’ digital literacy and security by integrating SEM with artificial neural networks (ANN). This review emphasizes the necessity for hybrid frameworks and sophisticated approaches to enhance research.
Co-Authors A. Wahab, Jufiza Abd Rahman, Abdullah Sani Aslily Sarkam, Nor Faezah Mohamad Razi, Nor Masrom, Suraya Mohamad Razi, Nor Faezah Mohammad, Nor Hazlina Othman, Nadirah Razi, Nor Faezah Mohamad Sarkam, Nor Aslily Teoh Sian Hoon Zafirah Azmi, Anis