Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Areen Arabiat
Al-Ahliyya Amman University
Education Electrical & Electronics Engineering

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Assessing the effectiveness of data mining tools in classifying and predicting road traffic congestion Areen Arabiat; Muneera Altayeb
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1295-1303

Abstract

Traffic congestion is a significant issue in cities, impacting the environment, commuters, and the economy. Predicting congestion is crucial for efficient network operation, but high-quality data and computational techniques are challenging for scientists and engineers. The revolution of data mining and machine learning has enabled the development of effective prediction methods. Machine learning (ML) approaches have shown potential in predicting traffic congestion, with classification being a key area of study. Open-source software tools WEKA and Orange are used to predict and classify traffic congestion. However, there is no single best strategy for every situation. This study compared the effectiveness of both data mining tools for predicting congestion in one of the areas of the capital of the Hashemite Kingdom of Jordan, Amman, by testing several classifiers including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF) classifications. The results showed that the Orange mining tool was superior in predicting traffic congestion, with a prediction accuracy of 100% for Random forest, logistic regression, and 99.8% for KNN. On the other hand, results were better in WEKA for the SVM classifier with an accuracy of 99.7%.
Classifier comparison benchmark for machine learning weather prediction enhancement Areen Arabiat; Mohammad Hassan
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.12060

Abstract

Artificial intelligence (AI) and data mining can improve next-generation weather forecasting for urban planning, agriculture, and disaster management. This study investigates how machine learning (ML) classifiers can reduce forecast errors and support decision-making in sectors that require accurate predictions, including agriculture and transportation. We evaluate four classifiers—K-nearest neighbor (KNN), random forest (RF), Naive Bayes (NB), and multilayer perceptron (MLP)—using Waikato environment for knowledge analysis (WEKA) and Orange3 to compare their performance in identifying rain. A 10-fold cross-validation approach is applied to reduce overfitting, and model effectiveness is measured using key performance indicators including accuracy, precision, sensitivity (recall), and F-measure. Results show that classifier performance varies across tools, indicating that the analytical framework can influence outcomes. Among all models, the RF classifier performs best, achieving 99.92% accuracy in WEKA and 99.9% in Orange3. The MLP also shows strong performance with 99.20% accuracy in WEKA and 98.7% in Orange3. KNN and NB exhibit comparable performance, but lower precision and F-measure in WEKA. Overall, the findings suggest that RF is the most effective approach for rain prediction using data mining tools, with practical relevance for agriculture, transportation, and power systems.
Bridging ethics and performance in engineering education through predictive learning analytics Hamza Abu Owida; Areen Arabiat
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i4.38767

Abstract

This literature review examines the opportunities, implementation challenges, ethical implications, and emerging directions of predictive learning analytics (PLA) in engineering education. Using a structured review of the literature, the study synthesizes evidence from several publications with emphasis on studies examining risk prediction, personalized support, curricular improvement, interpretability, fairness, and intervention design. The review shows that PLA can improve early identification of at-risk students, support adaptive learning pathways, and inform data-driven refinements in engineering curricula; however, its impact depends on data quality, model transparency, institutional capacity, and the availability of timely human support. The analysis further indicates that the most consequential barriers are fragmented data ecosystems, the difficulty of translating predictions into effective interventions, and unresolved ethical concerns related to privacy, bias, consent, and student agency. The article contributes to educational research by offering an integrated synthesis that connects technical development with pedagogical evaluation and ethical governance in engineering education. It concludes by proposing that future PLA adoption should align predictive modeling with explainable artificial intelligence, learning-theory-informed intervention design, and institution-level implementation strategies. Publications were selected for relevance to PLA in engineering education and then synthesized narratively across opportunities, challenges, ethics, and future directions.
Co-Authors Hamza Abu Owida Mohammad Hassan Muneera Altayeb