Search Results - (( _ education model algorithm ) OR ( data classification using algorithm ))*
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First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms
Published 2014“…The comparative analysis is also conducted to discover the best classification model for prediction. 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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Minimizing Classification Errors in Imbalanced Dataset Using Means of Sampling
Published 2023Conference Paper -
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A Data Mining Approach to Construct Graduates Employability Model in Malaysia
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Jogging activity recognition using k-NN algorithm
Published 2022“…Jogging activity recognition using the k-NN algorithm is a system that can help users collect information data of user speed movement using speed sensor and give the classification of jogging activity to the user. …”
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Academic Exercise -
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Talent classification using support vector machine technique / Hamidah Jantan, Norazmah Mat Yusof and Mohd Hanapi Abdul Latif
Published 2014“…The objective of this study is to suggest the potential classification model for talent forecasting throughout some experiments using SVM learning algorithm. …”
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Research Reports -
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A data mining approach to construct graduates employability model in Malaysia
Published 2011“…This study is to construct the Graduates Employability Model using classification task in data mining. To achieve it, we use data sourced from the Tracer Study, a web-based survey system from the Ministry of Higher Education, Malaysia (MOHE) for the year 2009. …”
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Article -
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Classification models for higher learning scholarship award decisions
Published 2018“…In this study, a data mining approach was used to propose a classification model of scholarship award result determination. …”
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Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk
Published 2024“…Despite advances in machine learning models for credit assessment, unbalanced datasets and some algorithms’ failure to explain forecasts remain major issues. …”
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Article -
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A study of feature selection algorithms for predicting students academic performance
Published 2018“…In EDM, Feature Selection (FS) plays a vital role in improving the quality of prediction models for educational datasets. FS algorithms eliminate unrelated data from the educational repositories and hence increase the performance of classifier accuracy used in different EDM practices to support decision making for educational settings. …”
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Article -
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A study of feature selection algorithms for predicting students academic performance
Published 2018“…In EDM, Feature Selection (FS) plays a vital role in improving the quality of prediction models for educational datasets. FS algorithms eliminate unrelated data from the educational repositories and hence increase the performance of classifier accuracy used in different EDM practices to support decision making for educational settings. …”
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Article -
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Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review
Published 2024“…The study also presents the most common balancing methods published from 2015 to 2021 and highlights their impact on resolving imbalanced classification in three approaches: data-level, algorithm-level, and hybrid-level. …”
Review -
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A hybrid spiking neural network model for multivariate data classification and visualization.
Published 2011“…Therefore, this hybrid learning model is proposed to harness the advantages of both SOM-AC and SNN to produce intuitive multivariate data classification and visualization. Empirical studies of the hybrid model using synthetic and benchmarking datasets yielded promising classification accuracy and intuitive rich visualization.…”
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Proceeding -
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Imbalanced Classification Methods for Student Grade Prediction : A Systematic Literature Review
Published 2023“…The study also presents the most common balancing methods published from 2015 to 2021 and highlights their impact on resolving imbalanced classification in three approaches: data-level, algorithm-level, and hybrid-level. …”
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Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad
Published 2023“…This project implements the Naive Bayes algorithm as the classification algorithm. The collected data from LinkedIn profiles then undergoes data preprocessing. …”
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Thesis -
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Predicting students’ STEM academic performance in Malaysian secondary schools using educational data mining
Published 2023“…It proceeds through three phases of Need Analysis, Development of the Model and Evaluation of the Model. Four different data mining classification algorithms which are Random Forest, PART, J48 and Naive Bayes will be used on the dataset. …”
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Poverty risk prediction based on socioeconomic factors using machine learning approach
Published 2025“…These findings imply that Logistic Regression is the suitable and interpretable model that can be used with structured data in the classification of poverty. …”
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Student Project -
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A comparative analysis of four classification algorithms for university students performance detection
Published 2019“…This paper proposes a model which able to identify the students who need special attention. …”
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Conference or Workshop Item -
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Penilaian esei berbantukan komputer menggunakan teknik Bayesian dan pengunduran linear berganda
Published 2006“…MMB Technique only required a small size of training data. (3) Prediction process of writing style using Multiple Linear Regression (MLR) Algorithm. …”
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VGG16-based deep learning architectures for classification of lung sounds into normal, crackles, and wheezes using Gammatonegrams
Published 2023“…The classification results were obtained using the Google Collaboratory platform.…”
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A Multi-Criteria Decision-Making Approach for Targeted Distribution of Smart Indonesia Card (KIP) Scholarships
Published 2025“…In the clustering stage, the combination of PCA+KMedoids with two initial medoids produced stable clusters in all iterations, suggesting that K-Medoids provided a better representation of data variation. Meanwhile, in the classification stage, the C5.0 algorithm achieved the highest accuracy of 97.27% from a total of 551 data points, with 80% used as training data and 20% as testing data. …”
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