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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science International Journal of Trends in Accounting Research
Rahayu Abdul Rahman
Universiti Teknologi Mara
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences

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The Influence of Risk Preference and Financial Condition on Tax Compliance of Boarding House Tax in Banjarmasin Saifhul Anuar Syahdan; Rahayu Abdul Rahman; Rizky Nastiti; Gemi Ruwanti; Norbaiti
INTERNATIONAL JOURNAL OF TRENDS IN ACCOUNTING RESEARCH Vol. 3 No. 1 (2022): International Journal of Trends in Accounting Research (IJTAR)
Publisher : Asosiasi Dosen Akuntansi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to examine the effect of risk preference and financial condition on tax compliance of boarding house owners. The variables of this study are tax compliance, risk preference and financial condition. This study used primary data obtained from the questionnaire. In addition, the respondents of this study were the taxpayers who owned a boarding house in Banjarmasin chosen by using purposive sampling. Furthermore, multiple regression analysis was employed to analyze the obtained data. The results of the study concluded that risk preference and financial condition had positive effect on tax compliance.    
Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms Suraya Masrom; Rahayu Abdul Rahman; Masurah Mohamad; Abdullah Sani Abd Rahman; Norhayati Baharun
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1153-1163

Abstract

This paper addresses the performances of machine learning classification models for the detection of tax avoidance problems. The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). Dealing with a real dataset on the tax avoidance cases among companies in Malaysia, has created a stumbling block for the conventional machine learning models to achieve higher accuracy in the detection process as the associations among all of the features in the datasets are extremely low. This paper presents a hybrid meta-heuristic between PSO and adaptive GA operators for the optimization of features selection in the machine learning models. The hybrid PSO-GA has been designed to employ three adaptive GA operators hence three groups of features selection will be generated. The three groups of features selection were used in random forest (RF), k-nearest neighbor (k-NN), and support vector machine (SVM). The results showed that most models that used PSO-GA hybrids have achieved better accuracy than the conventional approach (using all features from the dataset). The most accurate machine learning model was SVM, which used a PSO-GA hybrid with adaptive GA mutation.
Machine learning prediction of video-based learning with technology acceptance model Rahayu Abdul Rahman; Suraya Masrom; Nor Hafiza Abd Samad; Rulfah M. Daud; Evi Meutia
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1560-1566

Abstract

COVID-19 outbreak has significant impacts on education system as almost all countries shift to new way of teaching and learning; online learning. In this new environment, various innovative teaching methods have been created to deliver educational material in ensuring the learning outcomes such as video content. Thus, this research aims to implement machine learning prediction models for video-based learning in higher education institutions. Using survey data from 103 final year accounting students at Malaysian public university, this paper presents the fundamental frameworks of evaluating three machine learning models namely generalized linear model, random forest and decision tree. Besides demography attributes, the performance of each machine learning algorithm on the video-based learning usage has been observed based on the attributes of technology acceptance model namely perceived ease of use, perceived usefulness and attitude. The findings revealed that the perceived ease of use has given the highest weight of contributions to the generalized linear model and random forest while the major effects in decision tree has been given by the attitude variable. However, generalized linear model outperformed the two algorithms in term of the prediction accuracy.
Machine learning with task-technology fit theory factors for predicting students’ adoption in video-based learning Suraya Masrom; Rahayu Abdul Rahman; Norhayati Baharun; Syed Redzwan Sayed Rohani; Abdullah Sani Abd Rahman
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.5037

Abstract

Nowadays, various innovative educational and instructional tools have been created to deliver learning material including video content. One of the important issues with video-based learning is to devise effective teaching strategies to ensure higher level of learning can be achieved by the students. Getting insight and predicting the students’ video-based learning adoption will help the educators. Thus, this study aims to examine the potential of using machine learning prediction models on video-based learning adoption in higher education institutions. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT), and support vector machine (SVM). The performance of each machine learning algorithm in predicting the students’ learning adoption with video-based learning has been observed based on the attributes of task-technology fit theory. The findings indicated that the task-technology fit is useful in helping the machine learning algorithm to achieve high accuracy in the prediction of video-based learning adoption. The GBT is the best outperforming algorithm, followed with RF and SVM. This paper presents a fundamental research framework useful for helping educators and researchers to enhance student interest and retention on video-based learning.
Machine learning in predicting whistle-blowing intention of academic dishonesty with theory of planned behaviour Suraya Masrom; Nor Hafiza Abdul Samad; Rahayu Abdul Rahman; Farah Husna Mohd Fatzel; Siti Marlia Shamsudin
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp909-916

Abstract

The COVID-19 pandemic and its aftermath have caused most higher educations to choose to implement remote learning as a new method of instruction and assessment. Nevertheless, remote learning has been criticized by having adverse impact on academic integrity. Whistle-blowing has been regarded as an effective mechanism in limiting such unethical behavior. Thus, the main objective of this study is to identify the influence attributes of whistle-blowing intention among university students. The effectiveness of the whistle-blowing attributes was observed in prediction models based on machine learning technique. This paper presents the fundamental knowledge on evaluations of tree-based machine learning algorithms namely decision tree, random forest, to be compared with logistics regression and gradient linear model. A rigorous evaluation reports are provided that includes the area under curve (AUC) as a supplementary metric to measure the model accuracy. Additionally, to provide a clearer insight on the whistle-blowing prediction models, the pattern of influences from the whistle-blowing attributes based on the adoption of theory of planned behavior (TPB) and demography are presented. The findings revealed that both TPB and demography attributes contain some degree of impressive knowledge for the machine learning to generate a good prediction result.
Co-Authors Abdullah Sani Abd Rahman Evi Meutia Farah Husna Mohd Fatzel Gemi Ruwanti Masurah Mohamad Nor Hafiza Abd Samad Nor Hafiza Abdul Samad Norbaiti Norhayati Baharun Rizky Nastiti Rulfah M. Daud Saifhul Anuar Syahdan Siti Marlia Shamsudin Suraya Masrom Syed Redzwan Sayed Rohani