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All Journal Journal International Inspire Education Technology (JIIET) Al Irsyad: Jurnal Studi Islam Journal Markcount Finance International Journal of Educational Narratives 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 Journal of Biomedical and Techno Nanomaterials
Akhtar, Shazia
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Physics Social Sciences Other

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Fostering Academic Resilience: The Efficacy of Solution-Focused Brief Counseling (SFBC) in Alleviating Test Anxiety Among High-Achieving Adolescents Souza, Felipe; Costa, Bruna; Akhtar, Shazia
Research Psychologie, Orientation et Conseil Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v2i6.2332

Abstract

Test anxiety is a significant barrier to academic success, particularly among high-achieving adolescents who face high academic pressures. The impact of test anxiety on students’ performance and psychological well-being necessitates effective interventions. Solution-Focused Brief Counseling (SFBC), a short-term, goal-oriented approach, has shown promise in addressing various psychological concerns, yet its specific efficacy in alleviating test anxiety among high-achieving adolescents remains under-explored. This study aims to investigate the effectiveness of SFBC in reducing test anxiety and enhancing academic resilience in this population. A quasi-experimental design was employed, with 60 high-achieving adolescents randomly assigned to either an experimental group (SFBC intervention) or a control group. Pretest and posttest measures were taken using the Test Anxiety Inventory (TAI) and the Academic Resilience Scale (ARS). The experimental group received five SFBC sessions, while the control group received no intervention. Results indicated that the SFBC intervention significantly reduced test anxiety and enhanced academic resilience among the experimental group. The mean TAI score decreased significantly, and the ARS score increased, indicating improved coping strategies and greater academic perseverance.
THE USE OF ARTIFICIAL INTELLIGENCE FOR PREDICTING COFFEE BEAN QUALITY BASED ON DIGITAL IMAGES AND SENSOR DATA Silamat, Eddy; Zaman, Khalil; Akhtar, Shazia
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.
Human-Centered Artificial Intelligence Model in Classroom Management in Early Childhood Islamic Education Study Programs Idris, Meity H.; Riza, Eva; Manfaatin, Eva; Chai, Som; Akhtar, Shazia
Al Irsyad: Jurnal Studi Islam Vol. 5 No. 1 (2026): Al Irsyad: Jurnal Studi Islam
Publisher : STAI Publisistik Thawalib Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54150/alirsyad.v5i1.967

Abstract

Digital transformation demands adaptive classroom management; however, the implementation of Artificial Intelligence in Islamic higher education has not yet been structured into a human-centered model that considers pedagogical, ethical, and humanistic values. This study aims to analyze the needs of digital classroom management and to formulate a contextual Human-Centered Artificial Intelligence model for Islamic higher education. The research employed a qualitative approach with conceptual development studies through interviews, classroom observations, academic documentation, and interpretive thematic data analysis. The findings suggest that classroom management is confronted with difficulties stemming from diverse student capabilities, fluctuations in learning engagement, and inadequate utilization of learning data. To mitigate these issues, a Human-Centered Artificial Intelligence Model was developed, incorporating learning analytics, adaptive recommendation systems, and humanistic pedagogical tenets grounded in Islamic educational principles. This model designates educators as the principal decision-makers, with AI functioning as a data-driven support system. The model's implementation could potentially augment learning efficacy, personalize the learning experience, and sustainably enhance the quality of pedagogical interactions. Consequently, the integration of Human-Centered Artificial Intelligence fortifies adaptive classroom management by harmonizing technology, humanistic values, and the strategic function of educators. This study offers a conceptual model of AI-driven classroom management that is humanistic, contextually relevant, and pertinent to Islamic higher education within the digital age.
Co-Authors Ali, Zara Amin, Rafiullah Arif Muzayin Shofwan, Arif Muzayin Arnes Yuli Vandika Aziz, Safiullah Chai, Som Costa, Bruna Donny Juliandri Prihadi Eddy Silamat, Eddy EVA RIZA, EVA idris, Meity H. Judijanto, Loso Khan, Jamil Lim, Sofia Manfaatin, Eva Mokoena, Lesedi Muhammad Mustofa Nampira, Ardi Azhar purwanti, rina Raza, Amir Souza, Felipe Yanti Budiasih, Yanti Zaman, Khalil