Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
P-Index
This Author published in this journals
All Journal Journal International of Lingua and Technology Journal of Computer Science Advancements Journal of Moeslim Research Technik Techno Agriculturae Studium of Research Journal of Social Entrepreneurship and Creative Technology
James Scott
University of Alberta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
HUMAN-COMPUTER INTERACTION (HCI) DESIGN FOR DIGITAL QUR’AN APPLICATIONS: A STUDY ON ENHANCING USER EXPERIENCE FOR RECITATION AND STUDY Lutfiyah Dwi Setia; Sara Hussain; James Scott
Journal of Moeslim Research Technik Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i4.2500

Abstract

The increasing popularity of digital Qur'an applications highlights the need for improved human-computer interaction (HCI) design to enhance user experience, particularly for recitation and study. While these applications provide valuable tools for accessing and learning the Qur'an, users often encounter challenges related to usability, navigation, and engagement. Effective HCI design is crucial to ensure that digital platforms are intuitive, user-friendly, and promote an enriching experience for Qur'an recitation and study. This research aims to explore HCI design principles tailored to digital Qur'an applications, with a focus on enhancing accessibility, usability, and user engagement. The study utilizes a mixed-methods approach, including user surveys, expert interviews, and usability testing, to identify key issues in existing applications and propose design improvements. The findings reveal that users value features such as audio controls for recitation, easy navigation, and integration with tafsir (interpretation). Additionally, personalized study modes and accessibility features significantly enhance user engagement. The study concludes that applying HCI design principles to digital Qur'an applications can improve user experience by making recitation and study more accessible, efficient, and engaging. The research offers actionable insights for developers aiming to create more user-centric Qur'an applications.
CRISPR/CAS9-MEDIATED GENETIC ENGINEERING FOR DEVELOPING SALINITY-TOLERANT RICE VARIETIES FOR INDONESIAN COASTAL AGRICULTURE James Scott; Sarah Williams; David Martin
Techno Agriculturae Studium of Research Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i6.2964

Abstract

Salinity intrusion in coastal agricultural areas has become a major constraint to rice production in Indonesia, driven by climate change, sea-level rise, and unsustainable land management practices. High soil salinity adversely affects rice growth, yield stability, and food security, particularly in coastal regions that depend heavily on rice cultivation. Conventional breeding approaches for developing salinity-tolerant rice varieties are often time-consuming and limited by genetic complexity. Advances in genome editing technologies, particularly CRISPR/Cas9, offer a precise and efficient alternative for accelerating crop improvement. The objective of this study is to develop salinity-tolerant rice varieties suitable for Indonesian coastal agriculture through CRISPR/Cas9-mediated genetic engineering targeting key genes associated with salt stress tolerance. This research employed an experimental laboratory-based design combined with controlled greenhouse evaluation. Specific salinity-responsive genes were identified and edited using the CRISPR/Cas9 system. Transgenic rice lines were generated and screened for successful gene edits using molecular analysis techniques. Edited lines were subsequently evaluated under saline and non-saline conditions to assess physiological responses, growth performance, and yield-related traits. The results demonstrate that CRISPR/Cas9-edited rice lines exhibited enhanced tolerance to saline stress, indicated by improved germination rates, higher chlorophyll content, better ion homeostasis, and increased biomass compared to non-edited controls. Several edited lines maintained stable growth and yield under moderate to high salinity levels, confirming the effectiveness of targeted gene modification. In conclusion, CRISPR/Cas9-mediated genetic engineering shows strong potential for developing salinity-tolerant rice varieties tailored to Indonesian coastal environments. This approach provides a rapid and precise strategy to enhance rice resilience, support sustainable coastal agriculture, and strengthen national food security under changing climatic conditions.
THE AI ENERGY DILEMMA: FINDING THE MIDDLE GROUND BETWEEN HIGH PERFORMANCE AND ECO-FRIENDLINESS James Scott; Olivia Davis; Jessica Green
Journal of Computer Science Advancements Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i3.3337

Abstract

The exponential escalation of computational requirements for training and deploying Deep Learning models has precipitated an energy crisis, necessitating a critical reevaluation of the trade-off between algorithmic performance and environmental sustainability. This study aims to reconcile these conflicting demands by developing and validating a novel Dynamic Energy-Aware Pruning (DEAP) framework designed to maximize inference efficiency without compromising predictive accuracy. Employing a rigorous quantitative experimental design, we benchmarked state-of-the-art neural architectures, including ResNet-50 and Large Language Models (LLMs), across diverse hardware environments. The research utilized real-time telemetry to measure total energy consumption (Joules), thermal output, and carbon intensity () against standard accuracy metrics. Empirical results demonstrate that the proposed framework achieved a 42% reduction in energy consumption and stabilized hardware thermals, while maintaining predictive performance within a strict 1.5% non-inferiority margin compared to dense baselines. We definitively conclude that algorithmic sparsity effectively decouples high-level intelligence from excessive power usage, establishing a viable engineering paradigm for “Green AI” that aligns the trajectory of artificial intelligence with global decarbonization targets.
SCALING SOCIAL VALUE THROUGH CREATIVE TECHNOLOGIES: STRATEGIC CHALLENGES IN DIGITAL ENTREPRENEURSHIP Sofia Lim; Jaden Tan; James Scott; Ma'rifani Fitri Arisa
Journal of Social Entrepreneurship and Creative Technology Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jseact.v3i1.3475

Abstract

The rapid expansion of creative technologies has transformed digital entrepreneurship into a critical driver of social innovation and inclusive growth. Platform ecosystems, artificial intelligence, and data-driven infrastructures enable ventures to scale rapidly, yet strategic tensions emerge between technological acceleration and sustained social mission integrity. This study aims to develop and empirically validate a strategic framework explaining how digital entrepreneurs scale social value while navigating governance, stakeholder, and measurement challenges. A mixed-methods explanatory sequential design was employed, combining survey data from 214 digital social ventures with in-depth case studies of selected firms operating in technology-intensive sectors. Multiple regression and structural equation modeling were used to test relationships among technological capability, strategic agility, stakeholder integration, impact measurement sophistication, and scaling performance. Findings indicate that technological capability significantly predicts scaling performance (? = 0.41, p < 0.001), with strategic agility acting as a mediating variable. Hybrid ventures balancing innovation with governance and ecosystem collaboration achieved superior scaling outcomes. The study concludes that sustainable scaling of social value requires multidimensional capability alignment rather than technology-driven expansion alone. Integrative strategic management of creative technologies is essential for maintaining legitimacy, adaptability, and measurable social impact in digital entrepreneurship.
UTILIZING ARTIFICIAL INTELLIGENCE (AI) FOR AUTOMATED FEEDBACK ON THE ENGLISH ESSAY WRITING SKILLS OF INDONESIAN UNIVERSITY STUDENTS Nicodemus Rahanra; Ahmed Hossam; James Scott; Nurhayati
Journal International of Lingua and Technology Vol. 4 No. 3 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/jiltech.v4i3.1121

Abstract

Academic writing proficiency is crucial for Indonesian university students navigating global careers, yet the high student-to-teacher ratio severely limits the provision of timely, quality feedback. This constraint impedes skill acquisition and necessitates the exploration of scalable technological solutions. This study aimed to evaluate the validity and causal efficacy of a specialized, localized AI-powered Automated Essay Scoring (AES) system in accelerating students’ writing skill development over one academic semester. A quasi-experimental, pretest-posttest control group design (N=120) was used. The experimental group received continuous AI feedback, while the control group received traditional manual feedback. ANCOVA was applied to measure skill gain, supported by qualitative data on faculty workload and user acceptance. Findings showed the AI group achieved superior learning gains (13.5 vs. 5.5 raw gain) with a statistically significant main effect (F=22.45, p < 0.001). The system demonstrated high scoring reliability (r=0.88) and reduced lecturer routine grading time by 65%, successfully driving improvement in higher-order skills like Structural Coherence. The research confirms that the customized AI-Feedback Model is a pedagogically transformative tool that provides a sustainable solution to structural constraints in EFL education. It establishes a new paradigm for instructional practice by positioning localized AI as a highly consistent and effective mechanism for mass academic skill development in resource-constrained educational environments.
Co-Authors Ahmed Hossam David Martin Jaden Tan Jessica Green Lutfiyah Dwi Setia Ma'rifani Fitri Arisa Nurhayati Olivia Davis Rahanra, Nicodemus Sara Hussain Sarah Williams Sofia Lim