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All Journal International Journal of Advances in Intelligent Informatics
Salama A Mostafa
Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia
Computer Science & IT

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Prediction of player position for talent identification in association netball: a regression-based approach Nur Hazwani Jasni; Aida Mustapha; Siti Solehah Tenah; Salama A Mostafa; Nazim Razali
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.707

Abstract

Among the challenges in industrial revolutions, 4.0 is managing organizations’ talents, especially to ensure the right person for the position can be selected. This study is set to introduce a predictive approach for talent identification in the sport of netball using individual player qualities in terms of physical fitness, mental capacity, and technical skills. A data mining approach is proposed using three data mining algorithms, which are Decision Tree (DT), Neural Network (NN), and Linear Regressions (LR). All the models are then compared based on the Relative Absolute Error (RAE), Mean Absolute Error (MAE), Relative Square Error (RSE), Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Relative Square Error (RSE). The findings are presented and discussed in light of early talent spotting and selection. Generally, LR has the best performance in terms of MAE and RMSE as it has the lowest values among the three models.
Comparative analysis of classification techniques for leaves and land cover texture Azri Azrul Azmer; Norlida Hassan; Shihab Hamad Khaleefah; Salama A Mostafa; Azizul Azhar Ramli
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.706

Abstract

The texture is the object’s appearance with different surfaces and sizes. It is mainly helpful for different applications, including object recognition, fingerprinting, and surface analysis. The goal of this research is to investigate the best classification models among the Naive Bayes (NB), Random Forest (DF), and k-Nearest Neighbor (k-NN) algorithms in performing texture classification. The algorithms classify the leaves and urban land cover of texture using several evaluation criteria. This research project aims to prove that the accuracy can be used on data of texture that have turned in a group of different types of data target based on the texture’s characteristic and find out which classification algorithm has better performance when analyzing texture patterns. The test results show that the NB algorithm has the best overall accuracy of 78.67% for the leaves dataset and 93.60% overall accuracy for the urban land cover dataset.
A data mining approach for classification of traffic violations types Nor Aqilah Othman; Cik Feresa Mohd Foozy; Aida Mustapha; Salama A Mostafa; Shamala Palaniappan; Shafiza Ariffin Kashinath
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.708

Abstract

Traffic summons, also known as traffic tickets, is a notice issued by a law enforcement official to a motorist, who is a person who drives a car, lorry, or bus, and a person who rides a motorcycle. This study is set to perform a comparative experiment to compare the performance of three classification algorithms (Naive Bayes, Gradient Boosted Trees, and Deep Learning algorithm) in classifying the traffic violation types. The performance of all the three classification models developed in this work is measured and compared. The results show that the Gradient Boosted Trees and Deep Learning algorithm have the best value in accuracy and recall but low precision. Naïve Bayes, on the other hand, has high recall since it is a picky classifier that only performs well in a dataset that is high in precision. This paper’s results could serve as baseline results for investigations related to the classification of traffic violation types. It is also helpful for authorities to strategize and plan ways to reduce traffic violations among road users by studying the most common traffic violation types in an area, whether a citation, a warning, or an ESERO (Electronic Safety Equipment Repair Order).
Co-Authors Aida Mustapha Azizul Azhar Ramli Azri Azrul Azmer Cik Feresa Mohd Foozy Nazim Razali Nor Aqilah Othman Norlida Hassan Nur Hazwani Jasni Shafiza Ariffin Kashinath Shamala Palaniappan Shihab Hamad Khaleefah Siti Solehah Tenah