Articles
<![CDATA[ISLAMIC ECONOMICS AND SUSTAINABLE DEVELOPMENT: TOWARDS AN INCLUSIVE AND ETHICAL GLOBAL ECONOMY ]]>
<![CDATA[Abdur Rohman]]>;
<![CDATA[Amir Raza]]>;
<![CDATA[Khalil Zaman]]>
<![CDATA[ Journal of Noesantara Islamic Studies Vol. 3 No. 3 (2026) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/jnis.v3i3.3831
<![CDATA[The growing challenges of economic inequality, environmental degradation, and social exclusion have prompted a search for alternative economic systems. Islamic economics, grounded in principles of justice, equity, and responsible resource management, offers a promising solution to these global issues. This study aims to explore how Islamic economics can contribute to sustainable development by fostering an inclusive and ethical global economy. The research employs a qualitative approach, utilizing interviews with key informants and case studies from countries that have implemented Islamic economic principles. Data collected highlights the alignment of Islamic economics with the goals of sustainable development, emphasizing social welfare, environmental stewardship, and equitable wealth distribution. The study reveals that Islamic economic principles not only promote economic growth but also ensure that such growth benefits all segments of society without compromising environmental integrity. Barriers to the widespread adoption of Islamic economics, such as institutional resistance and limited awareness, are also identified. The findings suggest that integrating Islamic economic principles into global economic systems could significantly contribute to achieving the United Nations' Sustainable Development Goals. This research provides valuable insights for policymakers, business leaders, and scholars seeking to incorporate Islamic economics into development strategies.]]>
<![CDATA[INCLUSIVE EDUCATION IN PRIMARY SCHOOLS: EVIDENCE-BASED STRATEGIES FOR SUPPORTING LEARNERS WITH SPECIAL EDUCATIONAL NEEDS ]]>
<![CDATA[Jamil Khan]]>;
<![CDATA[Razia Khan]]>;
<![CDATA[Khalil Zaman]]>;
<![CDATA[Dewi Ismu Purwaningsih]]>
<![CDATA[ International Journal of Educatio Elementaria and Psychologia Vol. 3 No. 1 (2026) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/ijeep.v3i1.3424
<![CDATA[Inclusive education in primary schools has become a central priority in global education agendas, emphasizing the right of learners with special educational needs to access quality education within mainstream classrooms. Despite strong policy commitments, effective classroom-level implementation remains uneven, highlighting the need for evidence-based strategies that translate inclusion principles into practice. This study aims to identify and examine evidence-based instructional and organizational strategies that effectively support learners with special educational needs in inclusive primary school settings. The study employed a mixed-methods design combining systematic evidence review, classroom observations, semi-structured interviews, and analysis of school documents across multiple inclusive primary schools. Quantitative descriptive and inferential analyses were integrated with qualitative thematic analysis to examine strategy implementation and learner outcomes. The findings indicate that differentiated instruction, individualized support services, and collaborative teaching practices significantly enhance learner participation, engagement, and classroom inclusion. Inferential analysis demonstrates that higher levels of strategy implementation are associated with stronger learner engagement, while qualitative findings reveal improved peer interaction, confidence, and instructional responsiveness. The study concludes that inclusive education is most effective when evidence-based strategies are implemented coherently and systematically. Sustainable inclusion in primary schools requires alignment between empirical evidence, instructional practice, and institutional support to ensure equitable learning opportunities for learners with special educational needs.]]>
<![CDATA[Topological Quantum Computation Using Majorana Fermions in Nanowire Networks: A Theoretical Feasibility Study ]]>
<![CDATA[Loso Judijanto]]>;
<![CDATA[Khalil Zaman]]>;
<![CDATA[Zara Ali]]>
<![CDATA[ Journal of Tecnologia Quantica Vol. 3 No. 1 (2026) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/quantica.v2i4.2793
<![CDATA[Topological quantum computation offers a promising pathway toward fault-tolerant quantum information processing, with Majorana fermions emerging as key quasiparticles capable of encoding quantum states protected from local decoherence. Nanowire networks engineered to host Majorana zero modes have been widely proposed, yet their practical feasibility requires rigorous theoretical assessment under realistic physical constraints. This study aims to evaluate the theoretical viability of implementing topological quantum computation using Majorana fermions in semiconductor–superconductor nanowire networks. A modeling framework incorporating Bogoliubov–de Gennes equations, topological phase diagrams, non-Abelian braiding protocols, and disorder-induced perturbations is employed to assess stability and control requirements. Simulations investigate parameter regimes involving magnetic field strength, spin–orbit coupling, proximity-induced superconductivity, and wire-junction geometries. The results show that stable Majorana modes can be achieved within narrow but experimentally accessible parameter windows, and that non-Abelian braiding operations remain topologically robust against moderate disorder and quasiparticle poisoning. The study concludes that while significant engineering challenges persist—particularly regarding temperature constraints, material uniformity, and junction coherence—Majorana-based topological quantum computation remains theoretically feasible with current technological progress.]]>
<![CDATA[Cyber Activism and Digital Identity: Navigating the Politics of Representation in Cyberspace ]]>
<![CDATA[Khalil Zaman]]>;
<![CDATA[Zara Ali]]>
<![CDATA[ Journal of Loomingulisus ja Innovatsioon Vol. 2 No. 1 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/innovatsioon.v2i1.1972
<![CDATA[In an increasingly digital world, cyber activism and digital identity have become central to the discourse surrounding online participation and representation. The intersection of these concepts raises questions about how individuals and groups navigate the complex politics of visibility, power, and representation in cyberspace. This research explores how cyber activism influences the construction of digital identities and the political implications of online self-presentation. The study aims to analyze the relationship between cyber activism and the formation of digital identities, focusing on how these identities are constructed, contested, and performed in online platforms. It seeks to understand the impact of digital activism on political representation and personal agency in cyberspace. A qualitative research approach was employed, using case studies of prominent cyber activism movements, interviews with digital activists, and content analysis of social media campaigns. The study also draws on theoretical frameworks of digital culture, identity politics, and power relations in cyberspace. The findings reveal that cyber activism significantly shapes digital identities by providing platforms for marginalized voices and enabling new forms of political expression. However, challenges such as surveillance, cyberbullying, and digital censorship also complicate these efforts. Activists' online identities often face tension between authenticity and performative aspects of representation.]]>
<![CDATA[ARTIFICIAL INTELLIGENCE AND GLOBAL DIPLOMACY: REDEFINING POWER STRUCTURES IN THE 21ST CENTURY ]]>
<![CDATA[Unggul Sagena]]>;
<![CDATA[Khalil Zaman]]>;
<![CDATA[Shazia Akhtar]]>
<![CDATA[ Cognitionis Civitatis et Politicae Vol. 2 No. 4 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/politicae.v2i4.2183
<![CDATA[The rapid advancement of Artificial Intelligence (AI) has significantly impacted various sectors, including global diplomacy and international relations. In the 21st century, AI is redefining power structures by influencing decision-making processes, national security strategies, and diplomatic negotiations. However, the extent to which AI reshapes global power dynamics and the implications for traditional diplomacy are not yet fully understood. This research explores how AI technologies are altering the balance of power among nations and international institutions. The study aims to examine the role of AI in reshaping global diplomacy and its potential to redefine power hierarchies in international relations. Specifically, it focuses on analyzing the influence of AI on strategic decision-making, cybersecurity, and the geopolitical landscape. The research also seeks to assess the ethical challenges and opportunities that AI presents in the context of diplomacy. A qualitative research method is used, combining case studies of countries actively integrating AI into their diplomatic strategies and expert interviews with diplomats, policymakers, and AI specialists. Data is analyzed thematically to identify emerging patterns in how AI affects power structures and global governance. The findings indicate that AI is both empowering and disrupting traditional diplomatic practices. Countries with advanced AI capabilities gain strategic advantages, while those lagging behind face increased vulnerabilities. AI also introduces new ethical dilemmas in diplomacy, particularly in decision-making transparency and accountability. In conclusion, AI is redefining global power structures by reshaping diplomatic strategies and altering geopolitical alliances. Future research should focus on addressing the ethical implications and ensuring equitable access to AI technologies across nations to prevent widening global disparities.]]>
<![CDATA[THE USE OF ARTIFICIAL INTELLIGENCE FOR PREDICTING COFFEE BEAN QUALITY BASED ON DIGITAL IMAGES AND SENSOR DATA ]]>
<![CDATA[Eddy Silamat]]>;
<![CDATA[Khalil Zaman]]>;
<![CDATA[Shazia Akhtar]]>
<![CDATA[ Techno Agriculturae Studium of Research Vol. 2 No. 3 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/agriculturae.v2i3.2442
<![CDATA[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.]]>
<![CDATA[Recent Progress in Electrochemical Sensors for Environmental Monitoring ]]>
<![CDATA[Melly Angglena]]>;
<![CDATA[Khalil Zaman]]>;
<![CDATA[Zara Ali]]>
<![CDATA[ Research of Scientia Naturalis Vol. 1 No. 4 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v1i4.1575
<![CDATA[The increasing demand for real-time monitoring of environmental pollutants has driven advancements in electrochemical sensors. These sensors offer high sensitivity, selectivity, and the ability to operate in diverse conditions, making them ideal for environmental applications. Recent developments in materials and technologies have further enhanced their performance. This research aims to review the latest advancements in electrochemical sensors specifically designed for environmental monitoring. The focus is on evaluating their effectiveness in detecting various pollutants, including heavy metals, pesticides, and gases. A systematic literature review was conducted, analyzing recent studies and innovations in electrochemical sensor technology. Key parameters such as sensitivity, detection limits, and response times were compared across different sensor types. Advances in nanomaterials and miniaturization techniques were also examined to assess their impact on sensor performance. The findings indicate significant improvements in electrochemical sensors, with many achieving detection limits in the nanomolar range. Sensors utilizing nanostructured materials demonstrated enhanced sensitivity and faster response times. Additionally, the integration of wireless technologies allows for real-time data transmission, facilitating more efficient environmental monitoring. Recent progress in electrochemical sensors represents a vital advancement in environmental monitoring technology. These sensors offer promising solutions for detecting pollutants with high precision and reliability. Future research should focus on further improving sensor robustness and expanding their applicability across various environmental contexts.]]>
<![CDATA[Design and Fabrication of Microfluidic Biochips for Early Detection of Sexually Transmitted Diseases ]]>
<![CDATA[Khalil Zaman]]>;
<![CDATA[Omar Khan]]>;
<![CDATA[Jamil Khan]]>
<![CDATA[ Journal of Biomedical and Techno Nanomaterials Vol. 1 No. 4 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/jbtn.v1i4.1761
<![CDATA[Sexually transmitted diseases (STDs) remain a global health problem that requires early detection and rapid treatment. This study aims to design and fabricate microfluidic biochips for the early detection of several PMS-causing pathogens, such as Chlamydia trachomatis, Neisseria gonorrhoeae, and Trichomonas vaginalis. This research method involves designing chips with microfluidic technology, fabrication using lithography techniques, and testing the sensitivity and specificity of blood, urine, and cervical fluid samples. The results show that the biochip developed has a sensitivity of up to 92% and a specificity of 95%, with a detection time of less than 10 minutes. The biochip is also capable of detecting a variety of pathogens in a single device, making it an efficient diagnostic tool. In conclusion, this microfluidic biochip has the potential to be a fast, cheap, and effective PMS detection tool for use in the field. Further research needs to be conducted to test the sustainability of chip performance under real-world conditions and for further development in the detection of various other pathogens.]]>
<![CDATA[AI ASSISTED PERSONALIZED VACCINE DESIGN USING MULTI-OMICS CANCER DATA ]]>
<![CDATA[Khalil Zaman]]>;
<![CDATA[Shazia Akhtar]]>;
<![CDATA[Sofia Lim]]>;
<![CDATA[Ardi Azhar Nampira]]>
<![CDATA[ Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/jbtn.v2i3.2381
<![CDATA[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.]]>
<![CDATA[Social Networks and Social Support in the Success of Creative Startups ]]>
<![CDATA[Khalil Zaman]]>;
<![CDATA[Omar Khan]]>;
<![CDATA[Jamil Khan]]>
<![CDATA[ Journal of Social Entrepreneurship and Creative Technology Vol. 1 No. 4 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/jseact.v1i4.1729
<![CDATA[The success of creative startups is influenced by various factors, including the role of social networks and social support. As these ventures typically involve innovative ideas and high uncertainty, the social connections entrepreneurs establish can provide essential resources, knowledge, and emotional support. Understanding the impact of these social relationships on startup success is critical for entrepreneurs, investors, and policymakers aiming to foster a conducive environment for growth in creative industries. This study aims to explore the influence of social networks and social support on the success of creative startups. It seeks to identify the types of social support that entrepreneurs rely on, the networks they engage with, and how these factors contribute to their business growth and sustainability. A mixed-methods approach was employed, combining both quantitative and qualitative research methods. Data was collected through surveys of 100 creative entrepreneurs in Indonesia, followed by in-depth interviews with 15 startup founders. The analysis involved statistical techniques to identify patterns in network usage and social support, alongside thematic coding for qualitative insights. The findings reveal that social support, particularly from professional networks and mentorship, is crucial for overcoming challenges in the early stages of startup development. Entrepreneurs with strong connections to industry peers, investors, and mentors reported higher levels of success in securing funding and achieving market growth. Emotional support from family and friends was also found to play a significant role in maintaining resilience and motivation. Social networks and support systems are essential drivers of success for creative startups. Entrepreneurs who actively cultivate these networks tend to navigate challenges more effectively and sustain long-term business growth.]]>
