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All Journal Journal of Natural Science and Integration Buana Information Technology and Computer Sciences (BIT and CS) Nusantara Hasana Journal Journal of Technology and Informatics (JoTI) TIERS Information Technology Journal Journal of Communication Studies and Society AMPLITUDO: Journal of Science & Technology Innovation MANDALIKA: Journal of Social Sciences Journal of Advanced Computer Knowledge and Algorithms EDUTREND: Journal of Emerging Issues and Trends in Education Journal of Social Science Utilizing Technology
Musawer Hakimi
Samangan University
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Other

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Exploring the Integration of AI and Cloud Computing: Navigating Opportunities and Overcoming Challenges Musawer Hakimi; Ghulam Ali Amiri; Safiullah Jalalzai; Farid Ahmad Darmel; Zakirullah Ezam
TIERS Information Technology Journal Vol. 5 No. 1 (2024)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v5i1.5496

Abstract

This research seeks to establish how the integration of cloud computing and artificial intelligence identifies opportunities across operational efficiency, cost reduction, and innovation acceleration. This study seeks to establish how this integration is revolutionizing traditional business models and dealing with emerging security, privacy, and regulatory challenges. The applied method in this research was a systematic review strategy whose sources of data will be chosen from IEEE Xplore, Wiley Online Library, Springer, and ScienceDirect. The literature review focused on publications from 2019 to 2024 to deduce current findings that remain relevant. Results have shown that artificial intelligence, when integrated with cloud computing, would significantly enhance operational efficiency through process optimization and reduced cost using scalable cloud solutions. This also provides a greater pace of innovation by allowing real-time data processing and advanced analytics. However, such integration has a specific set of security and privacy concerns related to breaches and compliance with regulations in continuous evolution. It concludes that, though large, the benefits of AI and cloud computing integration must be reined in by strong security measures, updating regulatory frameworks, and continued research into ethical implications.
Exploring the Impact of Artificial Intelligence on Women's Empowerment: A Comprehensive Survey Hafizullah Shahbazi; Musawer Hakimi; Helena Ulusi; Behnaz Rahimi; Tamanna Quraishi
EDUTREND: Journal of Emerging Issues and Trends in Education Vol. 1 No. 2 (2024): EDUTREND: Journal of Emerging Issues and Trends in Education
Publisher : Lembaga Riset dan Inovasi Masyarakat Madani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59110/edutrend.333

Abstract

Artificial intelligence (AI) can significantly empower women and promote gender equality globally. However, to effectively utilize AI to promote women's empowerment, it is crucial to comprehend its influence, possibilities, and difficulties comprehensively. This study examines the various aspects of how AI contributes to the advancement of women's empowerment. It explores the extent to which AI is integrated into initiatives aimed at empowering women, the perceived impact of AI on women's empowerment on a global scale, and the obstacles women face in accessing AI opportunities. An integrated research methodology, including surveys and literature evaluation, was utilized to collect data from a diverse sample of 88 people. The results indicate a substantial degree of AI incorporation in projects aimed at empowering women, with varying perspectives on the impact of AI. Additionally, the study revealed difficulties in accessing AI opportunities and observed differing levels of knowledge among women. This study highlights the significance of ethical issues and inclusive policies in utilizing AI to promote women's empowerment. The findings provide significant knowledge for policymakers, researchers, and practitioners who aim to utilize AI's revolutionary capacity to promote gender equality and empower women globally.
Consumer Trust in AI-Generated Advertisements. A Comparative Study of AI and Human-Created Advertising Content Musawer Hakimi; Nazar Mohammad Parsa; Mohammad Nawab Turan
COMMUSTY Journal of Communication Studies and Society Vol. 5 No. 1 (2026)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/commusty.v5i1.7734

Abstract

The rise of generative artificial intelligence in advertising has created pressing questions about whether consumers can or will trust content they know a machine has crafted. This study examined how 412 adult consumers evaluated AI-generated versus human-created advertisements across six product sectors, investigating the mechanisms of perceived authenticity, cognitive engagement, and emotional response as mediators, and AI literacy as a boundary condition. Using a mixed experimental-survey design followed by structural equation modelling, mediation analysis, and cluster analysis, results showed that AI-generated advertisements received significantly lower trust ratings (d = 0.89), yet this gap narrowed substantially among participants with higher AI literacy. Perceived authenticity emerged as the strongest mediator, accounting for 31.2% of the indirect effect. Importantly, trust in AI advertising increased with repeated exposure, suggesting that familiarity attenuates initial scepticism. These findings yield actionable implications for practitioners and advance theoretical understanding of technology-mediated persuasion.
The Effectiveness of Assemblr Edu-Based Augmented Reality and Audio Media in Enhancing Science Concepts Understanding: A Quasi Experimental Design Safrizal Safrizal; Kavita Arafah; Reva Selpia; Musawer Hakimi
Journal of Natural Science and Integration Vol. 9 No. 1 (2026): Journal of Natural Science and Integration
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jnsi.v9i1.38994

Abstract

Conventional science instruction often relies on audio-based media, which often fails to help elementary students visualize complex, abstract scientific concepts, leading to suboptimal conceptual understanding. This study aimed to examine the effectiveness of Augmented Reality (AR) media in enhancing elementary students' conceptual understanding of science compared to audio-based media. A quasi-experimental design with a non-equivalent control group was employed, involving 50 fifth-grade students who were evenly assigned to experimental and control classes. Data were collected through pretests and posttests and analyzed using N-gain scores, effect size calculations, normality and homogeneity tests, and t-tests. The results showed that the experimental class achieved a mean pretest score of 41.6 and a posttest score of 67.2, while the control class achieved mean pretest and posttest scores of 42.6 and 49, respectively. The N-gain analysis showed greater improvement in the experimental class (0.44, moderate category) than in the control class. The effect size of 1.49 demonstrated a strong influence of AR-based media on students' learning outcomes. Furthermore, the t-test results confirmed significant differences both within groups (pretest–posttest) and between groups (posttest), emphasizing the superiority of AR media in science learning. It can therefore be concluded that Assemblr Edu-based AR media significantly improve elementary students' conceptual understanding of science. Keywords: augmented reality, students’ science concepts, science learning, audio media learning
Green Artificial intelligence Foundations, Applications, and Pathways to Sustainable Development Musawer Hakimi; Omid Tarashtwal; Hamayoon Ghafory
AMPLITUDO : Journal of Science and Technology Innovation Vol. 5 No. 1 (2026): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v5i1.524

Abstract

The fast evolution of artificial intelligence (AI) systems has worried people about their environmental impact thus prompting the rise of Green AI. In the present systematic review, we are going through the 32 articles published in peer-reviewed journals that were analyzed based on PRISMA standards regarding the conceptual bases, applications, and the future of Green AI. The review identified three paradigms: Green AI (computational efficiency), Sustainable AI (holistic socio-technical responsibility), and AI for Green (AI applied to sustainability challenges). A large part of the resources that would be used for the environments, monitoring, agriculture, and smart city applications can be saved by 15-30% through Green AI. The main difficulties are performance and efficiency balancing, limiting budget, and a research mentality that values precision more than sustainability. The research points out the dual function of AI in environmental matters as that of polluter and of a device for making the planet greener through humane practices and technologies. To sustainable AI, efficient algorithm design, regulatory support, the establishment of carbon-aware metrics, and collaboration among different disciplines to create the adoption of AI that is both economical and ethical are needed
Quantum-Enhanced Artificial Intelligence: Bridging Quantum Physics and Machine Learning for Next-Generation Computing Paradigms: Author's Country: Afganistan Mohammad Wali Khurami; Musawer Hakimi
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 2 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/5cdcfp74

Abstract

Quantum computing and artificial intelligence (AI) are converging into a distinct research frontier commonly referred to as quantum-enhanced artificial intelligence, or quantum machine learning (QML). This paper presents a conceptual and integrative review of how principles from quantum physics superposition, entanglement, and interference can be embedded into machine learning pipelines to reshape computational paradigms for classification, optimization, and representation learning. Using a structured narrative-review methodology, the study synthesizes theoretical foundations, algorithmic building blocks (quantum feature maps, variational quantum circuits, quantum kernel methods), and application domains spanning drug discovery, finance, materials science, and natural language processing. The review develops a hybrid quantum-classical architecture model and a complexity-comparison framework contrasting classical algorithms with their quantum counterparts, including Grover's search and Shor's factoring algorithm. Findings indicate that while theoretical speedups are well established, practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware remains constrained by decoherence, barren plateaus, and limited qubit connectivity. The paper contributes a synthesized taxonomy of quantum-enhanced AI methods and an evidence-based research agenda emphasizing error mitigation, hardware-aware ansatz design, and hybrid workload partitioning. The discussion further situates these developments within the broader trajectory of next-generation computing, arguing that near-term value will accrue primarily through hybrid quantum-classical systems rather than fully quantum pipelines. Implications for researchers, industry practitioners, and policymakers are discussed, alongside limitations inherent to a literature-synthesis approach.
Optimizing Traditional Games as Learning Media: A Literature Review on Cultural and Character Education Deya Harpina; Musawer Hakimi
MANDALIKA : Journal of Social Science Vol. 3 No. 1 (2025): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/mandalika.v3i1.180

Abstract

Using basic, environment-appropriate equipment and the results of cultural research that have been passed down from generation to generation in an area, traditional games may amuse kids. In this instance, integrating traditional games into education may be a unique set of values in the preservation, comprehension, and upkeep of cultural values while educating youngsters and having an effect on their characteristics. To successfully and meaningfully include conventional games into a child's educational environment. This entails modifying and improving conventional games so that kids may utilize them as useful teaching aids. Therefore, the goal of this essay is to further study the theory around the optimization of classic games as learning tools. This research's data was gathered by employing the literature study research method from a variety of literature sources that are pertinent to the subject or research issue under consideration. A thorough review and synthesis of pertinent studies has also been done in this study's literature study. The findings demonstrate that we may make learning interesting, participatory, and relevant for kids by utilizing the possibilities of traditional games. Challenges that could appear include a centralized curriculum, a modern viewpoint that disregards traditional games, logistical and facility issues, as well as community acceptance and involvement. These obstacles may be addressed, though, through understanding, and cooperation between parents, educators, and the community, as well as initiatives to imaginatively incorporate traditional games into children's learning.
Comparative Performance of Machine Learning Algorithms for Diabetes Prediction I Made Ardi Sudestra; Adie Wahyudi Oktavia Gama; Gede Humaswara Prathama; I Gusti Ngurah Darma Paramartha; Musawer Hakimi
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1195

Abstract

Early detection of diabetes mellitus is crucial to prevent severe complications. This study evaluates three machine learning algorithms for diabetes prediction using a quantitative comparative experimental design. The algorithms are k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Random Forest. These methods were chosen to compare distinct learning paradigms. k-NN is distance-based, SVM is margin-based, and Random Forest is an ensemble method. The goal is to find the optimal model for clinical use. The Pima Indians Diabetes dataset was used. It includes 390 patients and 15 clinical features. Performance was measured by accuracy, precision, recall, and F1-score. Random Forest had the highest accuracy (89.7%) and F1-score, providing the most balanced classification. SVM followed with 84.6%, and k-NN achieved 76.9%. Although k-NN had the highest recall (0.750), its precision was low (0.375), showing a high false-positive rate. Feature importance analysis pointed to blood glucose levels as the most significant predictor, which matches clinical knowledge. In summary, ensemble techniques like Random Forest offer the most reliable results. This highlights the importance of selecting the right algorithm for early diabetes detection in clinical applications.
A COMPREHENSIVE REVIEW OF BIAS IN AI ALGORITHMS Abdul Wajid Fazil; Musawer Hakimi; Amir Kror Shahidzay
Nusantara Hasana Journal Vol. 3 No. 8 (2024): Nusantara Hasana Journal, January 2024
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v3i8.1052

Abstract

This comprehensive review aims to analyze and synthesize the existing literature on bias in AI algorithms, providing a thorough understanding of the challenges, methodologies, and implications associated with biased artificial intelligence systems.Employing a narrative synthesis and systematic literature review approach, this study systematically explores a wide array of sources from prominent databases such as PubMed, Google Scholar, Scopus, Web of Science, and ScienceDirect. The inclusion criteria focused on studies that distinctly defined artificial intelligence in the education sector, were published in English, and underwent peer-review. Five independent reviewers meticulously evaluated search results, extracted pertinent data, and assessed the quality of included studies, ensuring a rigorous and comprehensive analysis. The synthesis of findings reveals pervasive patterns of bias in AI algorithms across various domains, shedding light on the nuanced aspects of discriminatory practices. The systematic review highlights the need for continued research, emphasizing the intricate interplay between bias, technological advancements, and societal impacts. The comprehensive analysis underscores the complexity of bias in AI algorithms, emphasizing the critical importance of addressing these issues in future developments. Recognizing the limitations and potential consequences, the study calls for a concerted effort from researchers, developers, and policymakers to mitigate bias and foster the responsible deployment of AI technologies. Based on the findings, recommendations include implementing robust bias detection mechanisms, enhancing diversity in AI development teams, and establishing transparent frameworks for algorithmic decision-making. The implications of this study extend beyond academia, informing industry practices and policy formulations to create a more equitable and ethically grounded AI landscape.
AI-based Phishing Attacks on University Networks: A Systematic Literature Review and Defense Framework Saidamin Sajid; Abdul Wajid Fazil; Musawer Hakimi
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.26650

Abstract

Phishing continues to be a major issue not only affecting the internet users but also being a big problem for the cybersecurity world; university networks are the most probable attack targets due to their open infrastructures, diverse user population, and limited security resources. The breakthrough in artificial intelligence (AI), notably large language models, has not only made phishing attacks more sophisticated and realistic but also envisioning new defense techniques based on machine learning and natural language processing. This current research report is a systematic literature review (SLR) of 53 academic studies that examine the dual aspect of AI in promoting and hindering phishing attacks in higher education institutions (HEIs). The review reveals three prominent points: emails sent by AI are increasingly real and adaptable; AI-based detection systems are very effective in laboratory-like conditions but struggle against new and adversarial attacks; and human factors like lack of user awareness and slow incident reporting are still the main vulnerabilities. The research then proposes a multi-layered defense framework that includes infrastructure strengthening, AI detection, human-centered awareness training, incident response mechanisms, and governance policies. This framework provides a practical roadmap for HEIs to boost their cybersecurity resilience and play a part in the sustainable growth of the university.
Co-Authors Abdul Rahman Mojadadi Abdul Wajid Fazil Adie Wahyudi Oktavia Gama Amir Kror Shahidzay Ansarullah Hasas Behnaz Rahimi Deya Harpina Ezatullah Ahmady Farid Ahmad Darmel Gede Humaswara Prathama Ghulam Ali Amiri Hafizullah Shahbazi Hafizullah Shahbazi Hamayoon Ghafory Helena Ulusi I Gusti Ngurah Darma Paramartha I Made Ardi Sudestra Kavita Arafah Khoshal Rahman Rahmani Mohammad Fawad Hussaini Mohammad Mustafa Quchi Mohammad Nawab Turan Mohammad Wali Khurami Nazar Mohammad Parsa Omid Tarashtwal Reva Selpia Safiullah Jalalzai Safrizal Safrizal Saidamin Sajid Sayed Mohammad Kazim Rajaee Tamanna Quraishi Zakirullah Ezam