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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics
Masrom, Suraya
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Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
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.
Machine learning prediction for academic misconduct prediction: an analysis of binary classification metrics Masrom, Suraya; Abdul Samad, Nor Hafiza; Septiyanti, Ratna; Roslan, Nurshafinas; Rahman, Rahayu Abdul
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.v13i1.5629

Abstract

Academic misconduct is unethical behavior in academic work. To sustain integrity culture and mitigating unethical conducts among higher education institutions community, the academic misconduct detection must be done at an earlier stage. Thus, this study attempted to provide a new empirical contribution with the analysis of binary classification performances metrics to describe the ability of machine learning in predicting academic misconduct. Four machine learning algorithms have been used namely generalized linear model (GLM), logistic regression (LR), decision tree (DT), and random forest (RF). Beside performances comparison, this paper presents the analysis of academic misconduct factors that were constructed based on demography and fraud triangle theory (FTT). The findings showed that all the four machine learning algorithms have obtained good ability in the prediction models with the accuracy at above 80% and below 20% of the classification errors. Rationalization from the FTT attributes has shown as the most important factor in GLM, LR, and DT. In RF, opportunity of FTT attributes have become the most important. Compared to FTT attributes, demography attributes were not providing much benefits to all the machine learning models but remain applicable at very low weight correlations.
Predicting the intention to adopt e-zakat payment services: a machine learning approach Abdul Samad, Nor Hafiza; Abdul Rahman, Rahayu; Masrom, Suraya; Omar, Norliana; Che Hasan, Haslinawati
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8512

Abstract

The technology evolution in the zakat collection and payment services has brought about a profound transformation in the global processes of gathering and distributing charitable contributions. Despite witnessing a positive trend in annual zakat collection in Malaysia, it has yet to reach its optimal level. Therefore, predictions regarding performance and comparisons across multiple models for online zakat collection hold crucial significance in improving the overall collection rate. This paper, utilizing data from 230 zakat payers, presents an empirical assessment of various machine learning algorithms aimed at predicting zakat payer intentions when utilizing online platforms for zakat payments. Additionally, this paper presents the analysis of machine learning features importance to justify the effect of technology acceptance model (TAM) and theory of technology readiness (TR) attributes in the machine learning algorithms for predicting e-zakat payment service adoption intention. The findings show that many of the machine learning models are able to perform for highly accurate results, with most achieving over 80% accuracy. The most crucial attribute influencing these predictions was found to be the TAM. This study's methodology is designed to be easily replicable, allowing for further detailed exploration of both the influencing factors and the machine learning algorithms used.
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.
Co-Authors A. Wahab, Jufiza Abd Rahman, Abdullah Sani Abdul Rahman, Rahayu Abdul Samad, Nor Hafiza Aslily Sarkam, Nor Baharun, Norhayati Che Hasan, Haslinawati Faezah Mohamad Razi, Nor Md Sakip, Siti Rasidah Mohamad Razi, Nor Faezah Mohammad, Nor Hazlina Mohd Zukri, Anis Zulaikha Omar, Norliana Othman, Nadirah Rahman, Rahayu Abdul Ratna Septiyanti Razi, Nor Faezah Mohamad Roslan, Nurshafinas Sarkam, Nor Aslily Teoh Sian Hoon Zafirah Azmi, Anis