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All Journal Journal of Moeslim Research Technik Journal of Selvicoltura Asean Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Journal of Loomingulisus ja Innovatsioon Cognitionis Civitatis et Politicae Techno Agriculturae Studium of Research Research of Scientia Naturalis Journal of Biomedical and Techno Nanomaterials Journal Markcount Finance Journal Neosantara Hybrid Learning Journal of World Future Medicine, Health and Nursing Journal Emerging Technologies in Education International Journal of Educational Narratives
Shazia Akhtar
Nangarhar University
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Physics Social Sciences Other

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THE USE OF ARTIFICIAL INTELLIGENCE FOR PREDICTING COFFEE BEAN QUALITY BASED ON DIGITAL IMAGES AND SENSOR DATA Eddy Silamat; Khalil Zaman; Shazia Akhtar
Techno Agriculturae Studium of Research Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i3.2442

Abstract

The increasing global demand for high-quality coffee requires more efficient and objective methods to evaluate bean quality. Traditional sensory and manual inspection techniques are time-consuming, subjective, and prone to inconsistency. This study aims to develop and validate an Artificial Intelligence (AI)-based predictive model for assessing coffee bean quality using digital image processing and sensor data. The research employs a quantitative experimental approach by integrating convolutional neural networks (CNNs) for visual analysis and machine learning regression models to process multispectral sensor data related to moisture, color, and aroma parameters. A dataset of 5,000 labeled coffee bean samples from three regional plantations was used for training and validation. The results demonstrate that the hybrid AI model achieved an accuracy rate of 96.8% in predicting bean grades compared to expert cupping scores, outperforming traditional visual grading methods by 18%. Furthermore, the integration of digital imaging and IoT-based sensors significantly reduced evaluation time and human error. The findings highlight AI’s potential to revolutionize coffee quality control by enabling automated, consistent, and scalable assessment systems that support sustainable agricultural practices.
IMPACT OF CLIMATE CHANGE ON MARINE BIODIVERSITY AND FISHERIE Donny Juliandri Prihadi; Shazia Akhtar; Zara Ali
Research of Scientia Naturalis Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v2i1.2007

Abstract

Climate change poses significant threats to marine biodiversity and fisheries, impacting ecosystems and the livelihoods that depend on them. Rising sea temperatures, ocean acidification, and altered salinity levels are among the key environmental changes affecting marine life. Understanding these impacts is crucial for developing effective management strategies. This study aims to investigate the effects of climate change on marine biodiversity and the resulting implications for fisheries. The research seeks to identify vulnerable species and ecosystems, as well as assess the economic consequences for fishing communities. A comprehensive literature review was conducted, analyzing existing studies on climate change impacts on marine ecosystems. Data from various regions were synthesized to evaluate changes in species distribution, abundance, and community composition. Economic assessments of fisheries were incorporated to understand the socio-economic implications. Findings indicate significant shifts in marine biodiversity due to climate change, with some species migrating to cooler waters while others face population declines. These changes have direct implications for fisheries, leading to altered catch patterns and economic instability for fishing communities. Vulnerable species were identified, highlighting the need for targeted conservation efforts. This research underscores the urgent need for adaptive management strategies to mitigate the impacts of climate change on marine biodiversity and fisheries. Collaborative efforts between scientists, policymakers, and fishing communities are essential to ensure the sustainability of marine resources in the face of ongoing environmental changes.
AI ASSISTED PERSONALIZED VACCINE DESIGN USING MULTI-OMICS CANCER DATA Khalil Zaman; Shazia Akhtar; Sofia Lim; Ardi Azhar Nampira
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2381

Abstract

The development of personalized cancer vaccines represents a promising frontier in oncology, yet traditional approaches struggle with the complexity and volume of multi-omics data. This study addresses this challenge by introducing an AI-assisted framework for the design of personalized vaccines. The primary objective was to leverage machine learning models to identify and prioritize neoantigens from integrated genomic, transcriptomic, and proteomic data of cancer patients. The methodology involved a deep learning pipeline to analyze multi-omics datasets, predicting tumor-specific mutations and their immunogenicity. This was followed by an algorithm to select the most potent neoantigen peptides for vaccine formulation, optimizing for both MHC binding affinity and T-cell activation potential. Our results demonstrate that the AI-driven approach significantly improved the speed and accuracy of neoantigen identification compared to conventional methods. The framework successfully predicted a set of high-quality vaccine candidates for individual patients, which showed strong in silico binding to patient-specific MHC molecules. We conclude that this AI-assisted methodology provides a powerful and scalable solution for personalized vaccine design, accelerating the translation of multi-omics data into clinically actionable immunotherapies.
Corporate Social Responsibility (CSR) and Cost of Capital: Evidence from the Indonesian Capital Market Yanti Budiasih; Rafiullah Amin; Shazia Akhtar
Journal Markcount Finance Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jmf.v3i1.2098

Abstract

Corporate Social Responsibility (CSR) has become a critical component of corporate strategy, with growing evidence suggesting its impact on financial performance and cost of capital. In Indonesia, where sustainable business practices are increasingly prioritized, understanding the relationship between CSR and cost of capital is essential for both firms and investors. This study examines the influence of CSR activities on the cost of capital for firms listed on the Indonesian Stock Exchange, focusing on how CSR initiatives affect investor perceptions and risk assessments. The research aims to provide empirical evidence on whether CSR can serve as a strategic tool to reduce the cost of capital and enhance firm value. Using a quantitative approach, this study analyzes financial data and CSR disclosures from 150 firms listed on the Indonesian Stock Exchange over a five-year period. Regression analysis is employed to assess the relationship between CSR performance and cost of capital, measured by weighted average cost of capital (WACC). The findings reveal that firms with higher CSR performance tend to have a lower cost of capital, indicating that CSR initiatives can reduce perceived risk and attract socially responsible investors. The study concludes that CSR activities positively influence the cost of capital, providing firms with a financial incentive to invest in sustainable practices. This research contributes to the discourse on CSR and corporate finance by offering practical insights for firms seeking to enhance their financial performance through responsible business practices.
THE ARCHITECTURE OF HYBRID LEARNING: DESIGNING "SMART CLASSROOMS" THAT SEAMLESSLY INTEGRATE PHYSICAL AND VIRTUAL COLLABORATION Razia Khan; Khalil Zaman; Shazia Akhtar
Journal Neosantara Hybrid Learning Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnhl.v4i1.2811

Abstract

The rapid evolution of educational technology has transformed the classroom into a dynamic space that blends physical and digital experiences. This study investigates the architecture of hybrid learning environments, focusing on the design of “smart classrooms” that seamlessly integrate face-to-face instruction with virtual collaboration. The purpose of this research is to develop a conceptual and practical framework for optimizing spatial, technological, and pedagogical elements to support active learning and inclusive participation. A mixed-method approach was employed, combining architectural design analysis, classroom observations, and interviews with teachers and students from three Indonesian universities that have adopted hybrid learning systems. Quantitative data were gathered through surveys measuring user satisfaction, technological usability, and collaboration efficiency. Results indicate that smart classroom design significantly enhances interaction, flexibility, and engagement across both physical and virtual participants. Data analysis revealed that 87% of respondents perceived the hybrid infrastructure as improving communication, while 82% reported increased motivation due to interactive tools such as smart boards, AR integration, and digital feedback systems. The study also found that spatial layout—especially seating arrangement and acoustic design—played a critical role in facilitating seamless collaboration. The findings conclude that the effectiveness of hybrid learning depends not only on digital infrastructure but also on the spatial and human-centered architecture of the learning environment. The proposed design framework offers guidelines for creating smart classrooms that align technological innovation with pedagogical needs, contributing to the development of sustainable and inclusive hybrid education models.
STUDENT DATA PRIVACY IN AI-DRIVEN PERSONALIZED LEARNING PLATFORMS: AN ETHICAL FRAMEWORK FOR HYBRID SCHOOLS Zara Ali; Shazia Akhtar; Rafiullah Amin
Journal Neosantara Hybrid Learning Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnhl.v4i1.2819

Abstract

The integration of artificial intelligence (AI) in personalized learning platforms has transformed hybrid education by enabling adaptive instruction, data-driven assessment, and individualized student support. However, this advancement has raised critical ethical concerns regarding student data privacy, transparency, and accountability. The unregulated collection, processing, and storage of learning data risk compromising students’ autonomy and confidentiality, particularly in hybrid schools where both digital and physical systems intersect. This study aims to develop an ethical framework that ensures responsible AI implementation in personalized learning environments while safeguarding student data integrity in Indonesian hybrid schools. A qualitative-descriptive research design was employed, involving document analysis, expert interviews, and focus group discussions with educators, AI developers, and policymakers. The research adopted a grounded theory approach to construct the framework, emphasizing ethical dimensions such as informed consent, algorithmic transparency, data minimization, and institutional accountability. Findings reveal that existing school policies often lack clarity in regulating third-party AI systems and data-sharing practices. The proposed ethical framework integrates three key components: governance principles, operational safeguards, and digital literacy strategies for teachers and students. The results suggest that adopting this framework can promote ethical awareness and responsible data stewardship, strengthening trust between institutions and learners. The study concludes that balancing innovation and ethical responsibility is essential to achieving equitable and secure AI-driven hybrid education.
ARTIFICIAL INTELLIGENCE IN EARLY DISEASE DETECTION: REVOLUTIONIZING DIAGNOSTIC PRACTICES IN MEDICINE Safiullah Aziz; Shazia Akhtar; Chen Mei
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i1.3535

Abstract

The integration of Artificial Intelligence (AI) in medicine has the potential to revolutionize early disease detection, improving diagnostic practices and patient outcomes. Early detection of diseases such as cancer, cardiovascular conditions, and neurological disorders significantly enhances treatment efficacy and survival rates. However, traditional diagnostic methods often suffer from limitations such as diagnostic errors, delayed results, and subjectivity. AI technologies, particularly machine learning (ML) and deep learning (DL), have demonstrated the ability to analyze large datasets, recognize patterns, and predict outcomes with greater accuracy and speed than conventional methods. This study aims to explore the impact of AI on early disease detection, focusing on its applications in diagnostic medicine. The research employs a systematic review of AI-based diagnostic tools and their clinical performance across various diseases. Data from peer-reviewed journals and clinical trials are analyzed to assess the accuracy, efficiency, and clinical implementation of AI technologies. The findings reveal that AI has the potential to significantly improve diagnostic accuracy, reduce diagnostic errors, and expedite disease detection, particularly in resource-limited settings. However, challenges remain regarding data privacy, algorithm transparency, and integration into clinical practice. In conclusion, AI stands poised to transform early disease detection, but careful consideration of ethical and technical challenges is essential for its widespread adoption.
Cybersecurity Challenges in Educational Technology: Protecting Student Data in Digital Learning Platforms Rafiullah Amin; Jamil Khan; Shazia Akhtar; Rustiyana Rustiyana
Journal Emerging Technologies in Education Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jete.v3i4.2797

Abstract

Background. The widespread adoption of educational technology has transformed teaching and learning, yet it has simultaneously exposed students to heightened cybersecurity risks due to increased data collection, online interactions, and dependence on digital learning platforms. Purpose. This study aims to analyze the key cybersecurity challenges faced by educational institutions and to evaluate the effectiveness of current protection mechanisms in safeguarding student data.   Method. A mixed-method approach was employed, combining a quantitative assessment of security vulnerabilities across 15 widely used learning platforms with qualitative interviews involving IT administrators, teachers, and cybersecurity specialists. Results. The results reveal significant inconsistencies in data encryption standards, inadequate authentication protocols, and limited cybersecurity awareness among platform users. Findings further indicate that institutional policies often lag behind technological advancements, creating systemic exposure to privacy threats. Conclusion. The study concludes that strengthening student data protection requires an integrated framework that combines technological safeguards, user training, and continuous policy updates. These insights underscore the urgency for educational institutions to adopt proactive cybersecurity governance aligned with emerging digital learning demands.
The Influence of Parenting Patterns on the Mental Health of School-Age Children Arif Muzayin Shofwan; Shazia Akhtar; Rafiullah Amin
International Journal of Educational Narratives Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijen.v3i1.2152

Abstract

Background. Parenting patterns play a critical role in the mental health and well-being of children, particularly during the school-age years when they experience significant emotional and social development. The way parents interact with their children, provide support, and set boundaries can either foster resilience or contribute to mental health challenges. Purpose. This study examines the influence of various parenting patterns authoritative, authoritarian, permissive, and neglectful on the mental health of school-age children. Method. The primary objective is to investigate how these parenting styles affect children’s emotional regulation, self-esteem, anxiety levels, and overall mental well-being. A quantitative research design was employed, using surveys administered to 400 parents of school-age children, complemented by psychological assessments of their children’s mental health. Results. The results indicate that authoritative parenting is positively associated with better mental health outcomes, including higher self-esteem and lower anxiety levels. In contrast, authoritarian and neglectful parenting were linked to increased anxiety and lower emotional regulation in children. Conclusion. The study concludes that parenting patterns significantly influence the mental health of school-age children, highlighting the importance of supportive, balanced parenting approaches. Interventions aimed at promoting authoritative parenting could contribute to improved mental well-being in children, particularly in academic and social contexts.
Co-Authors Amir Raza Ardi Azhar Nampira Arif Muzayin Shofwan Arnes Yuli Vandika Bruna Costa Chen Mei Donny Juliandri Prihadi Eddy Silamat Felipe Souza Jamil Khan Khalil Zaman Loso Judijanto Muhammad Mustofa Nur Achmad Rafiullah Amin Razia Khan Rustiyana, Rustiyana Safiullah Aziz Saut Gracer Sijabat Sofia Lim Thiago Rocha Unggul Sagena Wijaya Wijaya Yanti Budiasih Zara Ali Zhou Hui