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All Journal International Journal of Electrical and Computer Engineering International Journal of Evaluation and Research in Education (IJERE) Indonesian Journal of Electrical Engineering and Computer Science
Wah, Yap Bee
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Computer Science & IT Education Electrical & Electronics Engineering

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Predicting automobile insurance fraud using classical and machine learning models Shareh Nordin, Shareh-Zulhelmi; Wah, Yap Bee; Haur, Ng Kok; Hashim, Asmawi; Rambeli, Norimah; Jalil, Norasibah Abdul
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp911-921

Abstract

Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases.
Examining the trends of research on educators’ readiness in online teaching & learning (2013-2022): a bibliometric analysis Mohd Nasir, Farah Damia; Ghazali, Norliza; Mustakim, Siti Salina; Azmar, Khairunisa; Wah, Yap Bee
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i6.27113

Abstract

Despite recent emphasis, the usage of online and blended learning as instructional methodologies in higher education is still unequal, leading to variations in students’ learning experiences across structures, areas, and programs. Research on the aspects concerning educators’ adoption and adaptation of online teaching is crucial to overcome this limitation. By using the terms ‘Educators AND Online Learning AND Readiness’, 391 journal articles were listed for further analysis. Microsoft Excel was used for frequency analysis, VOSviewer to visualize data, Harzing’s Publish or Perish to compute and evaluate citation metrics, and Words Cloud to create a cluster of words that were shown in various sizes. The bibliometric criteria used in this study to summarize results includes language, topic area, research trends by year of publication, top contributors by nation, most significant institution, and name of the active source and others. This research includes a citation analysis, an authorship analysis, and keyword analysis. The results show that from 2013 to 2022, the rate of publication increased, and a spike was seen from 2019 to 2020 due to the COVID-19 epidemic. Identification of the important blended learning outcome predictors will help with the initial planning of this creative pedagogical strategy.
Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data Malek, Nur Hanisah Abdul; Yaacob, Wan Fairos Wan; Wah, Yap Bee; Md Nasir, Syerina Azlin; Shaadan, Norshahida; Indratno, Sapto Wahyu
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp598-608

Abstract

Training an imbalanced dataset can cause classifiers to overfit the majority class and increase the possibility of information loss for the minority class. Moreover, accuracy may not give a clear picture of the classifier’s performance. This paper utilized decision tree (DT), support vector machine (SVM), artificial neural networks (ANN), K-nearest neighbors (KNN) and Naïve Bayes (NB) besides ensemble models like random forest (RF) and gradient boosting (GB), which use bagging and boosting methods, three sampling approaches and seven performance metrics to investigate the effect of class imbalance on water quality data. Based on the results, the best model was gradient boosting without resampling for almost all metrics except balanced accuracy, sensitivity and area under the curve (AUC), followed by random forest model without resampling in term of specificity, precision and AUC. However, in term of balanced accuracy and sensitivity, the highest performance was achieved by random forest with a random under-sampling dataset. Focusing on each performance metric separately, the results showed that for specificity and precision, it is better not to preprocess all the ensemble classifiers. Nevertheless, the results for balanced accuracy and sensitivity showed improvement for both ensemble classifiers when using all the resampled dataset.
Co-Authors Azmar, Khairunisa Ghazali, Norliza Hashim, Asmawi Haur, Ng Kok Indratno, Sapto Wahyu Jalil, Norasibah Abdul Malek, Nur Hanisah Abdul Md Nasir, Syerina Azlin Mohd Nasir, Farah Damia Rambeli, Norimah Shaadan, Norshahida Shareh Nordin, Shareh-Zulhelmi Siti Salina Mustakim Yaacob, Wan Fairos Wan