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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Nik Siti Madihah Nik Mangsor
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.
Predicting likelihood of fraud among financial distressed firms in Malaysia using textual analysis Marziana Madah Marzuki; Syerina Azlin Md Nasir; Siti Fadilah Mat Zain; Nik Siti Madihah Nik Mangsor
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1620-1631

Abstract

This research paper aims to analyze and predict fraud patterns among failed companies in Malaysia. The approach involves utilizing textual analysis on the management discussion and analysis (MD&A) section within the annual reports. The dataset is subjected to text clustering to group companies based on similar financial characteristics. This clustering process entails several steps, including data conversion, collation, and summarization into a structured format, followed by text pre-processing to cleanse the dataset. Notably, RapidMiner Studio software was utilized to extract data for the study. Subsequently, the documents are clustered using both the K-means and latent dirichlet allocation (LDA) methods. Upon examining a sample of 22 failed companies in the year 2020, the study reveals that financially distressed companies exhibit prominent financial negativity and utilize litigious financial terms within their MD&A sections. These linguistic traits are found to be closely associated with seven distinct characteristics of fraudulent firms. This preliminary findings provide compelling evidence that financial pressure may serve as a triggering factor for fraudulent activities within companies.
Co-Authors Marziana Madah Marzuki Shuzlina Abdul Rahman Siti Fadilah Mat Zain Syerina Azlin Md Nasir Wan Fairos Wan Yaacob Zurina Ismail