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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Wan Fairos Wan Yaacob
Universiti Teknologi MARA Cawangan Kelantan

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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.
Co-Authors Nik Siti Madihah Nik Mangsor Norafefah Mohd Sobri Shuzlina Abdul Rahman Syerina Azlin Md Nasir Wan Faizah Wan Yaacob Zurina Ismail