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1
A Data Mining Approach to Construct Graduates Employability Model in Malaysia
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E4ML: Educational Tool for Machine Learning
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A data mining approach to construct graduates employability model in Malaysia
Published 2011“…The performance of Bayes algorithms are also compared against a number of tree-based algorithms. …”
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Article -
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Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study
Published 2024“…Conclusion: The findings demonstrated the application of ML algorithms and PCC to predict dental students’ academic performance. …”
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5
A conceptual framework for multi-objective optimization of building performance: Integrating intelligent algorithms, simulation tools, and climate adaptation
Published 2025“…Its practical value is demonstrated through applications in residential, educational, and commercial buildings across various climate zones. …”
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Artificial Intelligence (AI) to predict dental student academic performance based on pre-university results
Published 2022“…Methods: Various Machine Learning (ML) algorithms were applied using academic result samples of graduates of the Kulliyyah of Dentistry, IIUM from 2012-2017. …”
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Proceeding Paper -
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Students’ attitude towards video-based learning: machine learning analysis with rapid software / Abdullah Sani Abd Rahman ... [et al.]
Published 2022“…The results show that all the three machine learning algorithms produced high accuracy (above 95%) prediction results based on the hold-out testing dataset. …”
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Classification models for higher learning scholarship award decisions
Published 2018“…A dataset of successful and unsuccessful applicants was taken and processed as training data and testing data used in the modelling process. …”
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Feature selection methods application towards a new dataset based on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]
Published 2023“…This study will perform Analysis of Variance Test (ANOVA), Chisquared Test, Recursive Feature Elimination (RFE) and Extra Tree algorithm (ET) as feature selection methods to pre-process the proposed dataset that is considered raw data. …”
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Exploring students' performance in mathematics in Portugal using data analytics techniques: a data science use-case
Published 2024“…The study employs descriptive and predictive analytics techniques to understand student performance patterns and forecast future outcomes based on family background factors. The practical application of this research lies in developing predictive models that inform data- driven decisions by educators and policymakers. …”
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Book Chapter -
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Enhancing understanding of programming concepts through physical games
Published 2017“…There are many approaches in teaching programming such as through application software on line and offline, through software application games) and physical activities such as board games, dancing and computational thinking activities. …”
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Enhancing fairness and efficiency in teacher placement based on staff placement model: an intelligent teacher placement selection model for Ministry of Education Malaysia
Published 2025“…The effectiveness of ITPS was evaluated using five machine learning algorithms: J48, Decision Tree, Naïve Bayes, Random Forest, and K-Nearest Neighbors. …”
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First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms
Published 2014“…From the experiment, the models develop using Rule Based and Decision Tree algorithm shows the best result compared to the model develop from the Naïve Bayes algorithm. …”
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14
Visualisasi pohon sintaksis berasaskan model dan algoritma sintaks ayat bahasa Melayu
Published 2018“…These results proved that the algorithm and model, for syntactic tree output enhancement, are generalisable enough to be tested on other languages. …”
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15
Prediction of life expectancy for Asian population using machine learning ALGORITHMS / Nurul Shahira Pisal, Shuzlina Abdul-Rahman, Mastura Hanafiah and Saidatul Izyanie Kamarudin
Published 2022“…This study presents machine learning algorithms for life expectancy based on the Asian population dataset. …”
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Ensemble learning for multidimensional poverty classification
Published 2020“…Analysis of this study showed that Per Capita Income, State, Ethnic, Strata, Religion, Occupation and Education were found to be the most important variables in the classification of poverty at a rate of 99% accuracy confidence using Random Forest algorithm.…”
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An application of predicting student performance using kernel k-means and smooth support vector machine
Published 2012“…This thesis presents the model of predicting student academic performances inHigher Learning Institution (HLI).The prediction ofstudentssuccessfulis one of the most vital issues inHLI.In the previous work, thereare many methodsproposed topredictthe performanceof students such as Scholastic Aptitude Test (SAT) or American College Test (ACT), Intelligent Test, Fuzzy Set Theory, Neural Network, Decision Tree and Naïve Bayes.However, thefactremainsfound ina variety of debateamongeducators inhigher learning institution, especially those relatedto predictorvariablesthatused and the resulting level of prediction accuracy.This shown that the rule model in predicting student performanceisstilla gapand it is urgent for educators to obtain a more accurate prediction results.The objective of thisstudyis to create a rule model in predicting of students performance based on their psychometric factors. …”
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Beyond Grades - Predicting Programme Learning Outcomes with Multi-Output Regression in Malaysian Higher Education
Published 2025journal-article -
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A 'snowflake' geometrical representation for optimised degree six 3-modified chordal ring networks
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Factors with retirement behaviour among retirees and pre-retirees identified with a machine learning method / Muhammad Aizat Zainal Alam
Published 2023“…This study uses 3,067 responses which are then be coupled with a machine learning methodology (ranging from Naïve Bayesian, Generalised Linear Model, Logistic Regression, Artificial Neural Network, Decision Tree, Random Forest, and Gradient Boosted Trees) via RapidMiner Studio to expand the understanding of how categories of wealth and expenditures can affect retirement behaviour, given the increasingly important role of machine learning algorithms within the context of behavioural economics where it has been demonstrated to describe patterns and relationships in behavioural data better than standard statistical analysis. …”
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