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All Journal International Journal of Educational Narratives Journal of Computer Science Advancements Journal of Moeslim Research Technik Journal of Selvicoltura Asean Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Techno Agriculturae Studium of Research Research of Scientia Naturalis Journal of Humanities Research Sustainability
Zain Nizam
Universiti Malaysia Sarawak
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Physics Social Sciences Other

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The Influence of Principal Transformational Leadership on Teacher Performance Zain Nizam; Rashid Rahman; Sun Wei
International Journal of Educational Narratives Vol. 3 No. 2 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijen.v3i2.2154

Abstract

Background. Service learning has gained recognition as a valuable pedagogical approach in higher education, aiming to bridge the gap between academic theory and real-world practice. In the context of educational institutions, the role of leadership, particularly transformational leadership, plays a significant role in shaping the effectiveness of such programs. Purpose. This research explores the influence of principal transformational leadership on teacher performance within the framework of service learning in higher education institutions. Method. The study aims to assess how transformational leadership behaviors of school principals impact the engagement and performance of teachers involved in service learning programs. Using a quantitative research design, this study surveyed 150 teachers across several higher education institutions that implement service learning programs. Data were collected through questionnaires that assessed principals’ leadership styles and teachers’ performance in service learning contexts. Results. The results indicate that transformational leadership has a positive and significant effect on teacher performance, particularly in areas related to motivation, professional development, and commitment to service learning objectives. Teachers reported higher levels of engagement and effectiveness when their principals exhibited transformational leadership behaviors, such as inspirational motivation, individualized consideration, and intellectual stimulation. Conclusion. This study concludes that principals who embrace transformational leadership can significantly enhance teacher performance, thereby strengthening the impact of service learning programs.  
ROBOTIC ARM CONTROL SYSTEM DESIGN FOR HIGH PRECISION WORK Enda Wista Sinuraya; Bambang Winardi; Zain Nizam
Journal of Moeslim Research Technik Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i2.1928

Abstract

The demand for high-precision tasks in various industries, such as manufacturing and healthcare, necessitates the development of advanced robotic systems. Traditional robotic arms often struggle to meet the accuracy and repeatability required for precision work. This research focuses on designing a control system specifically tailored for robotic arms to enhance their performance in high-precision applications. The primary goal of this study is to develop an advanced control system for robotic arms that improves accuracy and reliability during precision tasks. The research aims to evaluate the effectiveness of various control algorithms in optimizing the performance of the robotic arm. A systematic approach was employed, utilizing simulation software to design and test different control strategies, including PID control and adaptive control methods. Performance metrics such as positional accuracy, response time, and stability were analyzed through a series of experiments conducted in both simulated and real-world environments. The implementation of the advanced control system resulted in significant improvements in the robotic arm's performance. The adaptive control method achieved a positional accuracy of 0.1 mm, with a response time reduction of 30% compared to traditional PID control. These findings demonstrate the effectiveness of the proposed control strategies in enhancing precision. The research successfully developed a robust control system for robotic arms, significantly improving their ability to perform high-precision tasks.
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.  
Biodiversity Conservation in the Anthropocene: Challenges and Solutions Aiman Fariq; Zain Nizam; Haziq Idris
Journal of Selvicoltura Asean Vol. 1 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v1i3.1660

Abstract

The Anthropocene epoch is characterized by significant human impact on the Earth's ecosystems, leading to unprecedented biodiversity loss. Rapid urbanization, climate change, and habitat destruction pose severe challenges to conservation efforts. Understanding these challenges is critical for developing effective strategies to preserve biodiversity. This study aims to identify the key challenges to biodiversity conservation in the Anthropocene and propose actionable solutions. By examining current conservation practices and their limitations, the research seeks to highlight innovative approaches that can enhance biodiversity protection. A comprehensive literature review was conducted, analyzing case studies and existing conservation strategies across various ecosystems. The study employs qualitative and quantitative methods to assess the effectiveness of these strategies in addressing biodiversity loss. Findings indicate that habitat degradation, climate change, and invasive species are the primary threats to biodiversity. Successful conservation initiatives, such as community-based management and the establishment of protected areas, demonstrate potential pathways for enhancing biodiversity resilience. Additionally, integrating traditional ecological knowledge with scientific approaches has shown promise in improving conservation outcomes. This research underscores the urgent need for adaptive and collaborative conservation strategies in the Anthropocene. By addressing the identified challenges and implementing proposed solutions, stakeholders can work towards more effective biodiversity conservation, ensuring the protection of ecosystems for future generations.
The Effect of Counselor Training Programs on the Quality of Interventions in Schools Siska Putri Belangi; Zain Nizam; Rashid Rahman; Nong Chai
Research Psychologie, Orientation et Conseil Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v2i1.1852

Abstract

The quality of school counseling interventions is a critical factor in supporting students’ academic, social, and emotional development. Effective counselor training programs are essential for equipping school counselors with the skills and knowledge needed to address diverse student needs. Despite the importance of training, there is limited empirical evidence on the direct impact of such programs on the quality of counseling interventions. This study examines the effect of counselor training programs on intervention quality in schools, focusing on professional competence, intervention outcomes, and counselor confidence. The research aims to evaluate how participation in structured training programs influences the effectiveness of school counselors in delivering interventions. A mixed-methods approach was employed, combining pre- and post-training surveys, in-depth interviews, and observational analysis. The study involved 150 school counselors across 20 schools, with data analyzed using thematic coding and statistical comparison of intervention outcomes. The findings reveal that participation in training programs significantly improves counselors’ confidence and professional competence, leading to enhanced intervention quality. Key improvements were observed in communication skills, problem-solving strategies, and the ability to tailor interventions to individual student needs. The study concludes that investing in counselor training programs has a substantial positive impact on the overall quality of school counseling services.
The Role of Executive Functions in Early Mathematics Achievement: A Cognitive Psychology Perspective Busnawir Busnawir; Zain Nizam; Nurul Huda
Research Psychologie, Orientation et Conseil Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v2i4.2524

Abstract

Early mathematics achievement is a critical predictor of long-term academic success, and understanding the cognitive mechanisms underlying mathematical learning is essential for educational psychology. Executive functions (EF) including working memory, inhibitory control, and cognitive flexibility play a pivotal role in supporting problem-solving, numerical reasoning, and the acquisition of mathematical concepts. This study aims to examine the contribution of executive functions to early mathematics achievement from a cognitive psychology perspective. A mixed-methods approach was employed, combining standardized EF assessments with mathematics performance tests in a sample of children aged 5–7 years. Data were analyzed using correlational and regression techniques to determine the predictive power of specific executive function components. Results indicate that working memory and inhibitory control are strongly associated with early numeracy skills, while cognitive flexibility contributes to adaptive problem-solving in novel mathematical tasks. Children with higher EF scores demonstrated significantly better performance in arithmetic, pattern recognition, and applied problem-solving. The study concludes that integrating EF training into early education curricula could provide a foundation for sustained mathematical competence and cognitive growth.
Enhancing the Efficiency of a Quantum Heat Engine Beyond the Carnot Limit Through Coherence-Assisted Bath Coupling Zain Nizam; Fatima Malik; Sara Hussain
Journal of Tecnologia Quantica Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i6.3205

Abstract

The Carnot limit has long been considered the upper bound for the efficiency of heat engines, a fundamental concept in classical thermodynamics. However, in quantum systems, the possibility exists to surpass this classical boundary by exploiting quantum phenomena such as coherence. This study investigates the enhancement of a quantum heat engine's efficiency beyond the Carnot limit through coherence-assisted bath coupling. The primary objective of the research is to explore how quantum coherence between the system and its thermal bath can be used to reduce dissipation, optimize energy transfer, and increase efficiency. The research employs both theoretical modeling and computational simulations to analyze the performance of a quantum heat engine under varying coherence times and bath coupling strengths. By adjusting these parameters, the study examines the effects of coherence-assisted bath coupling on engine efficiency. The results demonstrate that, through careful manipulation of the coherence time and bath coupling strength, the quantum engine can exceed the Carnot efficiency, achieving a maximum efficiency of 78.7%. This finding indicates that quantum coherence can be used as a resource to enhance the performance of quantum heat engines. In conclusion, this study presents a new approach to quantum thermodynamics, showing that coherence-assisted bath coupling provides a viable path to enhancing quantum heat engine efficiency beyond classical limits.  
AGRICULTURAL WASTE PROCESSING TECHNOLOGY FOR RENEWABLE ENERGY IN MEXICO Siti Aisyah; Liz Yanti Andriyani; Syaifullah Rahim; Zain Nizam
Techno Agriculturae Studium of Research Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i1.1990

Abstract

This study aims to examine agricultural waste treatment technology as a renewable energy source in Mexico, focusing on the potential, challenges, and obstacles faced in its implementation. The background of this research is based on the large amount of agricultural waste produced by Mexico's agricultural sector, most of which has not been optimally utilized to produce renewable energy. The method used in this study is a descriptive-qualitative approach through secondary data analysis, interviews with experts, and case studies in some of the largest agricultural waste producing areas. The results show that although the energy potential of agricultural waste is huge, the rate of technology adoption is still low, due to technological, economic, and policy constraints. The conclusions of this study emphasize the importance of stronger policy support and the provision of adequate infrastructure to encourage wider adoption of waste treatment technology. Education to farmers and rural communities is also needed to increase awareness about the benefits of agricultural waste as an energy source.
MOBILE APPLICATION DESIGN BASED ON NATURAL LANGUAGE PROCESSING TO IMPROVE THE QUALITY OF HEALTH SERVICES Achmad Ridwan; Zain Nizam; Daniyar Satybaldy
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.v3i1.1626

Abstract

The increasing demand for efficient and personalized health services has driven the integration of advanced technologies into healthcare systems. Mobile applications leveraging natural language processing (NLP) offer promising solutions to improve patient communication, diagnostic accuracy, and service delivery. Despite advancements, challenges remain in developing user-friendly applications that address diverse healthcare needs. This research focuses on designing a mobile application based on NLP to enhance the quality of health services, emphasizing usability, accuracy, and accessibility. The study employs a user-centered design approach combined with experimental evaluation. The application was developed using Python-based NLP libraries, integrating features such as symptom analysis, medical query responses, and appointment scheduling. A prototype was tested with 150 participants, including patients and healthcare professionals, to evaluate performance metrics such as response accuracy, user satisfaction, and system reliability. The findings indicate that the NLP-based application achieved an 85% accuracy rate in interpreting medical queries and a 90% user satisfaction rate. Participants reported improved communication with healthcare providers and faster access to relevant medical information. However, challenges such as handling complex medical terminology and ensuring data privacy were noted. The study concludes that NLP-powered mobile applications have significant potential to improve health service quality by enabling efficient and accurate communication between patients and providers. Addressing challenges related to data security and expanding linguistic capabilities will be essential for future development. The research underscores the importance of integrating advanced technologies to meet the evolving needs of the healthcare sector.
BIG DATA ANALYTICS FOR SUSTAINABLE GREEN SUPPLY CHAIN MANAGEMENT OPTIMIZATION MODELS Zain Nizam; Rashid Rahman; Muhammad Arif Abdul Hakim
Journal of Computer Science Advancements Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i2.3789

Abstract

The growing need for sustainable practices in global supply chains has driven the adoption of Big Data Analytics (BDA) to optimize performance and reduce environmental impact. Traditional supply chain management systems often fail to balance operational efficiency with sustainability goals, leading to increased waste and resource inefficiency. Big Data Analytics, by providing real-time insights, predictive models, and data-driven decision-making, offers a solution to this challenge. This research explores the application of BDA in the optimization of Sustainable Green Supply Chain Management (GSCM) models, focusing on how data-driven strategies can enhance both environmental and operational performance. The study employs a mixed-methods approach, combining case studies, performance metrics, and interviews with key industry stakeholders to assess the impact of BDA on supply chain efficiency, resource utilization, and waste reduction. The results show that BDA significantly improves key performance indicators, including a 20% increase in resource efficiency, a 25% reduction in waste, and a 15% decrease in operational costs. The study concludes that BDA is a crucial enabler for sustainable supply chains, providing organizations with the tools to optimize operations while minimizing their environmental footprint.
Co-Authors Achmad Ridwan Aiman Fariq Bambang Winardi Busnawir Busnawir Chevy Herli Sumerli Daniyar Satybaldy Enda Wista Sinuraya Fatima Malik Haziq Idris Hazmi, Muhammad Liz Yanti Andriyani Muhammad Arif Abdul Hakim Nina Anis Nong Chai Nurul Huda Rashid Rahman Rina Farah Sara Hussain Siska Putri Belangi Siti Aisyah Sun Wei Syaifullah Rahim Wijaya Wijaya