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All Journal Journal of Computer Science Advancements Journal of Moeslim Research Technik Journal of Selvicoltura Asean Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Journal of Loomingulisus ja Innovatsioon Techno Agriculturae Studium of Research Research of Scientia Naturalis Islamic Studies in the World
Rina Farah
Universiti Teknologi
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Physics Other

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Navigating Patriarchy and Piety: A Case Study of Islamic Feminist Discourse and Women's Leadership in Malaysian NGOs Amina Azhigali; Nina Anis; Rina Farah
Islamic Studies in the World Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/isw.v2i4.2700

Abstract

Patriarchal norms continue to shape socio-religious expectations for Muslim women in Southeast Asia, particularly in Malaysia, where debates around gender, authority, and piety intersect within civil society spaces. Islamic feminist discourse has gained increasing visibility in recent years, yet questions remain regarding how women leaders in Muslim-majority contexts negotiate religious legitimacy while challenging gendered power structures. Malaysian non-governmental organizations (NGOs) provide an important arena for examining the everyday strategies through which women navigate patriarchal constraints and articulate faith-based approaches to gender justice. This study aims to investigate how women leaders in Malaysian Islamic-oriented NGOs engage with Islamic feminist discourse to negotiate authority, cultivate legitimacy, and advance transformative social agendas. The research seeks to identify the discursive, religious, and organizational strategies that enable or hinder women’s leadership within patriarchal environments. A qualitative case-study approach was employed, drawing on in-depth interviews with fifteen female NGO leaders, participant observation of organizational activities, and document analysis of mission statements, program materials, and public advocacy texts. Data were analyzed using thematic coding informed by feminist theory, Islamic gender ethics, and discourse analysis. Findings reveal that women leaders strategically mobilize Qur’anic principles, prophetic narratives, and concepts of justice to challenge patriarchal interpretations while maintaining religious credibility. Participants reported using relational leadership styles, community-based legitimacy, and interpretive flexibility to navigate gendered expectations. The study concludes that Islamic feminist discourse serves as both a protective shield and a transformative tool, enabling women to assert leadership within constraints while promoting more inclusive understandings of Islam in civil society.
MACHINE VISION FOR QUALITY CONTROL IN HALAL FOOD PRODUCTION: A DEEP LEARNING APPROACH Chevy Herli Sumerli A; Rina Farah; Zain Nizam
Journal of Moeslim Research Technik Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i3.2351

Abstract

Ensuring the quality and integrity of halal food products has become increasingly important with the growth of the global halal food industry. Conventional quality control methods, which rely on manual inspection and laboratory testing, are often time-consuming, subjective, and prone to human error. This study aims to develop and evaluate a machine vision system powered by deep learning algorithms to automate quality control processes in halal food production. A convolutional neural network (CNN)-based framework was implemented to classify and detect defects, contamination, and non-halal elements in food products. The system was trained using a dataset of 12,500 labeled images collected from halal-certified production facilities, with data augmentation applied to improve model generalization. Performance metrics, including accuracy, precision, recall, and F1-score, were used to evaluate the system. The results demonstrate that the proposed deep learning model achieved 96.8% classification accuracy, with high precision (95.5%) and recall (97.2%), significantly outperforming conventional machine vision techniques. The findings indicate that deep learning-driven machine vision can provide fast, reliable, and scalable quality control, supporting compliance with halal standards while reducing operational costs. This research highlights the potential of artificial intelligence to modernize quality assurance systems in halal food industries.  
SOFTWARE ENGINEERING FOR ZAKAT MANAGEMENT PLATFORMS: A STUDY ON TRANSPARENCY, SECURITY, AND USER TRUST Zuraida Zuraida; Rina Farah; Hilda Dwi Yunita
Journal of Moeslim Research Technik Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i6.2499

Abstract

The management of zakat is crucial in Islamic finance, and digital platforms have increasingly been adopted to enhance transparency, security, and trust among users. This study examines the software engineering aspects of zakat management platforms, focusing on these critical dimensions. The research aims to identify key software design considerations that can improve transparency, ensure data security, and foster user trust within digital zakat platforms. A mixed-method approach is used, involving both qualitative interviews with zakat management professionals and quantitative analysis of platform users' perceptions. The findings suggest that clear communication regarding financial transactions, robust data protection measures, and user-friendly interfaces are essential for building trust. Furthermore, implementing blockchain technology was found to significantly enhance transparency and security. The study concludes that for zakat platforms to be successful, they must not only comply with Shariah principles but also integrate advanced technology solutions that align with user expectations for security and transparency. This research provides a comprehensive framework for the development of zakat management platforms that can be adopted by stakeholders in the Islamic finance sector.
THE ROLE OF NON-TIMBER FOREST PRODUCTS IN RURAL LIVELIHOODS Faisal Razak; Syafiq Amir; Rina Farah
Journal of Selvicoltura Asean Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i2.2028

Abstract

Non-timber forest products (NTFPs) have long been integral to the livelihoods of rural communities, providing essential resources for food, medicine, income, and cultural practices. Despite their importance, the role of NTFPs in improving rural welfare is often underexplored in scientific research. Understanding their contribution is critical for sustainable development and rural poverty alleviation. This study aims to assess the role of NTFPs in the livelihoods of rural populations, exploring their economic, social, and environmental significance. It further seeks to identify the challenges and opportunities surrounding the sustainable use of these resources. The research adopts a mixed-methods approach, combining qualitative interviews with local communities and quantitative data collection through surveys. Fieldwork was conducted in selected rural areas where NTFPs are a key resource. Data were analyzed using descriptive statistics and thematic analysis. Findings reveal that NTFPs significantly contribute to household income, particularly in communities with limited access to formal employment. They also play a vital role in maintaining cultural practices and providing food security. However, overharvesting and inadequate policy frameworks threaten their sustainability. NTFPs are crucial for rural livelihoods, but their continued availability depends on effective management strategies that balance economic needs with environmental conservation. Policy interventions should focus on promoting sustainable harvesting practices and supporting local communities in managing these resources.  
THE ROLE OF COGNITIVE DEVELOPMENT IN ADOLESCENCE: IMPLICATIONS FOR EDUCATION AND MENTAL HEALTH Rina Farah; Nina Anis; Ruri Angelia Kusuma
Research Psychologie, Orientation et Conseil Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v3i1.3369

Abstract

Adolescence is a critical developmental period marked by rapid cognitive, emotional, and social changes that significantly influence learning processes and mental health outcomes. Cognitive development during this stage plays a central role in shaping adolescents’ academic engagement, self-regulation, and psychological well-being, yet it is often examined separately from educational and mental health perspectives. This study aims to investigate the role of cognitive development in adolescence and its implications for both education and mental health within an integrated framework. The research employed a quantitative cross-sectional design involving adolescents aged 13–18 years enrolled in secondary education. Data were collected using standardized instruments measuring cognitive development, educational engagement, and mental health well-being, and were analyzed using descriptive and inferential statistical techniques. The findings reveal significant positive relationships between cognitive development and educational engagement, as well as between cognitive development and mental health indicators. Adolescents with higher levels of cognitive functioning demonstrated stronger academic engagement and better emotional regulation. These results indicate that cognitive development functions as a key mechanism linking learning and mental health during adolescence. The study concludes that educational practices and mental health interventions should prioritize cognitive development as a foundational element.
Benchmarking Quantum Annealers vs. Classical Solvers for Complex Optimization Problems in Financial Modeling Muh. Nur; Rina Farah; Nina Anis
Journal of Tecnologia Quantica Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i4.2601

Abstract

Quantum annealing has emerged as a promising computational paradigm for solving large-scale combinatorial optimization problems that are traditionally intractable for classical algorithms. The financial modeling sector, characterized by complex portfolio optimization, risk minimization, and option pricing problems, offers a fertile ground for benchmarking the performance of quantum versus classical solvers. This study aims to systematically evaluate the computational efficiency, scalability, and accuracy of quantum annealers specifically the D-Wave Advantage system against leading classical optimization algorithms, including simulated annealing and branch-and-bound methods. A comparative experimental framework was developed to test both solver types on real-world financial datasets encompassing portfolio selection and risk-parity optimization tasks. Quantitative performance metrics such as solution quality, convergence time, and energy landscape exploration were assessed. Results revealed that quantum annealers achieved near-optimal solutions significantly faster for high-dimensional problem instances with non-convex cost functions, whereas classical solvers maintained superior consistency for smaller, well-conditioned models. The findings suggest a complementary paradigm where quantum annealing can accelerate subproblems within hybrid financial optimization pipelines. The study concludes that quantum computing, while not yet universally superior, represents a viable accelerator for specific financial optimization classes under current hardware constraints.
The Role of Business Incubators in Facilitating Startup Growth in Indonesia Elliya Sestri; NIna Anis; Rina Farah
Journal of Loomingulisus ja Innovatsioon Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v2i2.1974

Abstract

Business incubators have emerged as vital institutions in fostering startup growth, providing support, resources, and networking opportunities to early-stage ventures. In Indonesia, a rapidly growing startup ecosystem, incubators play a crucial role in helping entrepreneurs navigate the challenges of scaling their businesses. However, the extent to which these incubators contribute to startup success remains underexplored, particularly in the Indonesian context.. The research aims to identify the specific support mechanisms provided by incubators and assess how these contribute to the scalability and sustainability of startups in the country. This study employs a mixed-methods approach, combining quantitative surveys of startup founders who have participated in incubator programs and qualitative interviews with incubator managers and industry experts. The data collected were analyzed to identify key factors that contribute to startup growth, such as access to funding, mentorship, networking opportunities, and business development services. The findings indicate that business incubators significantly enhance startup growth by providing access to crucial resources, including seed funding, mentoring, and networking with industry professionals. Startups that participated in incubator programs demonstrated higher survival rates and faster growth compared to those that did not. Mentorship and access to strategic partnerships were particularly important for long-term sustainability. Business
Fostering Divergent Thinking in the Classroom: The Impact of Project-Based Learning on Student Creativity Harianta Sembiring; Nofirman Nofirman; Fini Widya Fransiska; Wahju Dyah Laksmi Wardhani; Rina Farah
Journal of Loomingulisus ja Innovatsioon Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v2i5.2513

Abstract

The growing demand for 21st-century skills underscores the importance of nurturing students’ creativity through educational practices that promote divergent thinking. This study investigates the impact of Project-Based Learning (PBL) on fostering divergent thinking and creative performance among high school students. The research aims to analyze how the integration of PBL facilitates idea fluency, flexibility, originality, and elaboration in learners’ creative processes. A quasi-experimental design was employed, involving two groups of students: one receiving traditional instruction and the other exposed to PBL interventions across four project cycles. Data were collected using a validated creativity assessment rubric and analyzed through descriptive and inferential statistics (ANOVA). The findings reveal a significant improvement in divergent thinking indicators among students taught through PBL, particularly in their ability to generate multiple and original ideas. Qualitative observations also highlight that collaborative project environments enhanced motivation, self-expression, and problem-solving capacities. The study concludes that PBL serves as an effective pedagogical framework for cultivating creative and divergent thinking skills essential for innovation-driven learning. Implications emphasize the need for curriculum designers and educators to embed authentic, project-based tasks within classroom instruction.
PLANT HEALTH MONITORING TECHNOLOGY WITH ARTIFICIAL INTELLIGENCE IN FRANCE Faisal Razak; Rina Farah; Haziq Idris; Ardi Azhar Nampira
Techno Agriculturae Studium of Research Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i2.1995

Abstract

This study explores the role of artificial intelligence (AI)-based plant health monitoring technology in France, which is expected to improve the efficiency of early detection of plant diseases and optimize the use of agricultural resources. The background of this research is based on the urgent need to increase agricultural productivity and reduce negative impacts on the environment. The purpose of this study is to test the effectiveness of AI in detecting plant health problems and provide data-driven recommendations for farmers. This study uses a mixed approach, with quantitative data from farmer surveys and qualitative data from interviews and case studies in major agricultural regions in France. The results showed that 80% of farmers reported an increase in early detection of diseases, and 75% reported a reduction in pesticide use. In conclusion, AI is playing an important role in supporting sustainable agriculture in France, although challenges in access to technology still need to be addressed. Further research is needed to explore ways to expand the adoption of this technology among smallholders.
BIG DATA ANALYSIS TO PREDICT CONSUMPTION PATTERNS IN SMART CITIES Anto Susilo; Rachmat Prasetiyo; Bilal Aslam; Rina Farah
Journal of Computer Science Advancements Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i1.1535

Abstract

The rapid development of smart cities has increased the demand for efficient resource management and personalized services, where understanding consumption patterns is crucial. Big data analysis offers a powerful tool for predicting these patterns, enabling city planners and service providers to make data-driven decisions to enhance urban living quality. This study aims to utilize big data analytics to predict consumption patterns across various sectors in smart cities, including energy, water, and transportation. By leveraging large datasets, this research seeks to provide actionable insights for optimizing resource allocation and anticipating future consumption demands. The methodology involves collecting and analyzing data from multiple sources, such as IoT sensors, public utility records, and social media, to identify consumption trends. Machine learning algorithms, including time series analysis and clustering, were applied to detect patterns and forecast demand. Results indicate that big data analytics can accurately predict consumption fluctuations, with an 85% accuracy in energy demand forecasting and a 78% accuracy in water usage prediction. The findings highlight correlations between demographic factors and consumption, providing a comprehensive understanding of urban needs. The study concludes that big data analysis is a valuable approach to managing resources effectively in smart cities. By predicting consumption patterns, city planners can proactively address demand surges, reduce waste, and improve resource distribution, ultimately supporting sustainable urban growth. Implementing these insights could significantly enhance smart city efficiency and resilience.
Co-Authors Amina Azhigali Anto Susilo Ardi Azhar Nampira Bilal Aslam Chevy Herli Sumerli Elliya Sestri Faisal Razak Fini Widya Fransiska Harianta Sembiring Haziq Idris Hilda Dwi Yunita Manurung, Edison Hatoguan Muh. Nur Nina Anis Nofirman, Nofirman Rachmat Prasetiyo Ruri Angelia Kusuma Syafiq Amir Wahju Dyah Laksmi Wardhani Zain Nizam Zuraida Zuraida