Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal International Journal of Advances in Intelligent Informatics
Aida Mustapha
Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
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 Cik Feresa Mohd Foozy Nazim Razali Nor Aqilah Othman Nur Hazwani Jasni Salama A Mostafa Shafiza Ariffin Kashinath Shamala Palaniappan Siti Solehah Tenah