Article Per Year (5 Year)
p-Index From 2021 - 2026
1.819
P-Index
This Author published in this journals
All Journal Journal International Inspire Education Technology (JIIET) 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
Unknown Affiliation
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 Physics Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
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.
Co-Authors Ali, Zara Amin, Rafiullah Arif Muzayin Shofwan, Arif Muzayin Arnes Yuli Vandika Aziz, Safiullah Costa, Bruna Donny Juliandri Prihadi Eddy Silamat, Eddy Judijanto, Loso Khan, Jamil Lim, Sofia Mokoena, Lesedi Muhammad Mustofa Nampira, Ardi Azhar purwanti, rina Raza, Amir Souza, Felipe Yanti Budiasih, Yanti Zaman, Khalil