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This Author published in this journals
All Journal International Journal of Public Health Science (IJPHS) Bulletin of Electrical Engineering and Informatics International Journal of Trends in Accounting Research
Abdul Rahman, Rahayu
Unknown Affiliation
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Social Sciences

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The Influence of Risk Preference and Financial Condition on Tax Compliance of Boarding House Tax in Banjarmasin Anuar Syahdan, Saifhul; Abdul Rahman, Rahayu; Nastiti, Rizky; Ruwanti, Gemi; Norbaiti, 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 | Full PDF (214.472 KB) | DOI: 10.54951/ijtar.v3i1.291

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.    
COVID-19 pandemic and mental health of educators in higher education institution: a systematic literature review Abdul Rahman, Rahayu; Mohd Isa, Noor Saatila; Zamri, Norhayati; Pitaloka, Endang; Suyoto, Yohanes Totok; Yunus, Mohd Hadli Shah Mohamad
International Journal of Public Health Science (IJPHS) Vol 12, No 4: December 2023
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v12i4.22832

Abstract

Emergency transformation of the education system due to the COVID-19 increased mental and emotional disorders risks of educators in higher education institutions. Thus, this study aims to examine how COVID-19 impacts the mental well-being of educators, and also explore the contributing factors to these issues. A systematic review was conducted, involving the identification of scientific articles related to mental health, lecturers, professors, and COVID-19. The research was performed on two reputable publication databases, SCOPUS and Web of Science. Following pre-established inclusion and exclusion criteria, this study utilized PRISMA to select and analyze the research articles. Through this process, a total of 59 articles have been identified from the electronic databases, out of which seven articles were selected for evaluation. The findings indicate that a significant number of educators encountered various mental health challenges in the midst of the pandemic, including burnout, anxiety, depression, and stress. Numerous factors, such as an imbalance between job demand and job resources, a lack of support, personal factors, and other emergency remote teaching-related factors, all contribute to the issues. This study offers valuable insights that can be utilized to develop optimal practices for educators to address and manage their mental health and well-being in the future.
Predicting the intention to adopt e-zakat payment services: a machine learning approach Abdul Samad, Nor Hafiza; Abdul Rahman, Rahayu; Masrom, Suraya; Omar, Norliana; Che Hasan, Haslinawati
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8512

Abstract

The technology evolution in the zakat collection and payment services has brought about a profound transformation in the global processes of gathering and distributing charitable contributions. Despite witnessing a positive trend in annual zakat collection in Malaysia, it has yet to reach its optimal level. Therefore, predictions regarding performance and comparisons across multiple models for online zakat collection hold crucial significance in improving the overall collection rate. This paper, utilizing data from 230 zakat payers, presents an empirical assessment of various machine learning algorithms aimed at predicting zakat payer intentions when utilizing online platforms for zakat payments. Additionally, this paper presents the analysis of machine learning features importance to justify the effect of technology acceptance model (TAM) and theory of technology readiness (TR) attributes in the machine learning algorithms for predicting e-zakat payment service adoption intention. The findings show that many of the machine learning models are able to perform for highly accurate results, with most achieving over 80% accuracy. The most crucial attribute influencing these predictions was found to be the TAM. This study's methodology is designed to be easily replicable, allowing for further detailed exploration of both the influencing factors and the machine learning algorithms used.
Co-Authors Abdul Samad, Nor Hafiza Anuar Syahdan, Saifhul Che Hasan, Haslinawati Endang Pitaloka Masrom, Suraya Mohd Isa, Noor Saatila Nastiti, Rizky Norbaiti, Norbaiti Omar, Norliana Ruwanti, Gemi Yohanes Totok Suyoto Yunus, Mohd Hadli Shah Mohamad Zamri, Norhayati