Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Wan Fairos Wan Yaacob
Universiti Teknologi MARA Cawangan Kelantan

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analysing corporate social responsibility reports using document clustering and topic modeling techniques Nik Siti Madihah Nik Mangsor; Syerina Azlin Md Nasir; Wan Fairos Wan Yaacob; Zurina Ismail; Shuzlina Abdul Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1546-1555

Abstract

Corporate social responsibility (CSR) has become an imperative tool to address challenges and achieve sustainable growth. Realizing its impact to the society, companies are demanded to participate in sustainable development of which poverty eradication is one of it. The CSR practice, to date, is not strategically planned and executed especially when it comes into philanthropic corporate social responsibility (PCSR). This could be due to failure to identify categories of PCSR activities, limiting its effectiveness to achieve the intended outcomes. Thus, document clustering is proposed to be used to automate the pattern identification process. This study has extended document clustering by integrating the traditional document clustering application with topic modeling approach. This integrated approach enables the identification of the PCSR pattern. The analysis involved a three-year data from the annual report of the 25 CSR-award winning companies in Malaysia which involved several steps. Findings from this study revealed seven clusters that represented seven types of PCSR activities performed by the CSR-award winning companies in Malaysia. The findings offer an insight to be considered by companies in strategizing the CSR activities, particularly philanthropic responsibility in ensuring optimum impact to innovatively support the society and contribute towards poverty mitigation.
Supervised data mining approach for predicting student performance Wan Fairos Wan Yaacob; Syerina Azlin Md Nasir; Wan Faizah Wan Yaacob; Norafefah Mohd Sobri
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1584-1592

Abstract

Data mining approach has been successfully implemented in higher education and emerge as an interesting area in educational data mining research. The approach is intended for identification and extraction of new and potentially valuable knowledge from the data. Predictive model developed using supervised data mining approach can derive conclusion on students' academic success. The ability to predict student’s performance can be beneficial for innovation in modern educational systems. The main objective of this paper is to develop predictive models using classification algorithm to predict student’s performance at selected university in Malaysia. The prediction model developed can be used to identify the most important attributes in the data. Several predictive modelling techniques of K-Nearest Neighbor, Naïve Bayes, Decision Tree and Logistic Regression Model models were used to predict student’s performance whether excellent or non-excellent.  Based on accuracy measure, precision, recall and ROC curve, results show that the Naïve Bayes outperform other classification algorithm.  The Naïve Bayes reveals that the most significant factors contributing to prediction of excellent students is when the student scores A+ and A in Multivariate Analysis; A+, A and A- in SAS Programming and A, A- and B+ in ITS 472.
Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data Nur Hanisah Abdul Malek; Wan Fairos Wan Yaacob; Yap Bee Wah; Syerina Azlin Md Nasir; Norshahida Shaadan; Sapto Wahyu Indratno
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 Nik Siti Madihah Nik Mangsor Norafefah Mohd Sobri Norshahida Shaadan Nur Hanisah Abdul Malek Sapto Wahyu Indratno Shuzlina Abdul Rahman Syerina Azlin Md Nasir Wan Faizah Wan Yaacob Yap Bee Wah Zurina Ismail