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Journal of Artificial Intelligence and Development
Published by Edujavare Publishing
ISSN : -     EISSN : 30317428     DOI : https://doi.org/10.70610/jaid.v5i02.1693
Core Subject : Science, Education, Social,
Computer Science & IT Environmental Science Mathematics Social Sciences Other
The Journal of Artificial Intelligence and Development (e-ISSN: 3031-7428) is dedicated to the rapid dissemination of important research results to the global artificial intelligence (AI) community. The journal’s scope encompasses all areas of AI, including agents and multi-agent systems, automated reasoning, constraint processing and search, knowledge representation, machine learning, natural language, planning and scheduling, robotics and vision, and uncertainty in AI
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 24 Documents
 1   2   3 
Educational Revolution through the Application of AI in the Digital Era Agus Nursalim; Loso Judijanto; Asfahani Asfahani
Journal of Artificial Intelligence and Development Vol. 1 No. 1 (2022): AI Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v1i1.338

Abstract

The rapid advancement of artificial intelligence (AI) technology has sparked an educational revolution, transforming traditional teaching methodologies and learning approaches in the digital era. This study aims to investigate the impact of AI application in education, focusing on personalized learning experiences, student engagement, critical thinking development, and data-driven decision-making. The research utilizes a Systematic Literature Review (SLR) methodology to gather and analyze relevant empirical evidence and theoretical frameworks. The results highlight significant improvements in personalized learning, increased student engagement, enhanced critical thinking skills, and improved data-driven decision-making processes facilitated by AI technology. The findings contribute to a deeper understanding of the educational revolution driven by AI in the digital era and underscore the importance of responsible AI integration for fostering inclusive, innovative, and effective education systems.
21st Century Economic Transformation: The Impact of Artificial Intelligence on Markets and Employment Loso Judijanto; Asfahani Asfahani; Arnes Yuli Vandika
Journal of Artificial Intelligence and Development Vol. 1 No. 1 (2022): AI Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v1i1.339

Abstract

Artificial intelligence (AI) has transformed the 21st-century economy, shaping markets and employment. This study aims to investigate the impact of AI on the market and employment. This research uses a qualitative approach, which takes data through observation and interview techniques. In contrast, the data analysis technique uses the Miles Humbermen model through data presentation, reduction, and conclusion. This research reveals that although AI increases efficiency and productivity, it also causes job displacement and requires upskilling efforts and new skills. Additionally, AI drives the emergence of new sectors and raises ethical and regulatory challenges that must be overcome for sustainable economic development.
Industrial Revitalization with AI between Opportunities and Challenges for Global Economic Growth Loso Judijanto; Asfahani Asfahani; Anjana Prusty; Nova Krisnawati; Asri Ady Bakri
Journal of Artificial Intelligence and Development Vol. 4 No. 1 (2025): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v4i1.340

Abstract

This study investigates the Industrial Revitalization with AI, exploring both the opportunities and challenges it presents for global economic growth. The aim is to provide a comprehensive understanding of AI's role in reshaping industries, the methods used in the study include qualitative research methods such as in-depth interviews with industry leaders, technology experts, and stakeholders. The findings highlight the significant potential of AI in enhancing productivity and innovation across various sectors, while also emphasizing the need for addressing concerns such as job displacement, data privacy, and regulatory frameworks. In conclusion, the study underscores the transformative impact of AI on industrial revitalization, emphasizing the importance of responsible AI deployment and collaborative efforts for sustainable economic growth.
AI-Supported Management through Leveraging Artificial Intelligence for Effective Decision Making Loso Judijanto; Asfahani Asfahani; Asri Ady Bakri; Edy Susanto; Ummu Kulsum
Journal of Artificial Intelligence and Development Vol. 4 No. 1 (2025): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v4i1.341

Abstract

This study explores integrating artificial intelligence (AI) technology in management practices to improve decision-making effectiveness. This research investigates how AI-powered management systems leverage machine learning, predictive analysis, and scenario modeling to provide real-time data insights and optimize resource allocation. This research uses the Systematic Literature Review (SLR) methodology to analyze existing studies and theoretical frameworks related to AI in management. The research results reveal that AI technology contributes significantly to strategic agility, operational efficiency, and risk assessment, ultimately resulting in better decision outcomes. The study concludes that organizations must invest in talent development, ethical considerations, and cybersecurity measures to fully exploit AI's potential for effective decision-making in today's dynamic business landscape.
The Future of Leadership: Integrating AI Technology in Management Practices Loso Judijanto; Asfahani Asfahani; Nova Krisnawati
Journal of Artificial Intelligence and Development Vol. 1 No. 2 (2022): AI Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v1i2.344

Abstract

With the rapid advancement of artificial intelligence (AI) technology, organizations face a paradigm shift in leadership and management practices. This research aims to explore the integration of AI technology in leadership, focusing on its impact, challenges, and opportunities. This research uses a systematic literature review (SLR) method; this research synthesizes existing knowledge and identifies key findings regarding the role of AI in decision-making, ethical considerations, organizational readiness, and required leadership skills. These findings highlight the potential benefits of AI in improving decision-making processes and organizational efficiency while emphasizing the importance of addressing ethical issues, improving organizational readiness, and developing new leadership competencies. This research contributes to understanding the future leadership landscape in AI technology integration.
Optimization of Organizational Performance by Utilization of AI for Strategic Management Insights Loso Judijanto; Asfahani Asfahani; Syamsul Muqorrobin; Nova Krisnawati
Journal of Artificial Intelligence and Development Vol. 4 No. 1 (2025): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v4i1.345

Abstract

In response to the complexities of modern markets, organizations are increasingly turning to Artificial Intelligence (AI) as a transformative tool for enhancing strategic decision-making and organizational performance. This study investigates the impact of AI utilization on optimizing organizational performance within strategic management. A qualitative research method was employed, utilizing semi-structured interviews to gather insights from key organizational informants. The findings highlight the significant role of organizational culture, leadership support, and collaborative approaches in maximizing AI's potential for agility, informed decision-making, and competitive advantage. The analysis contributes to a deeper understanding of AI's implications for strategic management and underscores the importance of holistic approaches in leveraging AI for sustained performance enhancement.
Emotion-Driven Deep Learning Recommendation Systems: Mining Preferences from User Reviews and Predicting Scores Yadong Shi; Fu Shang; Zeqiu Xu; Shuwen Zhou
Journal of Artificial Intelligence and Development Vol. 3 No. 1 (2024): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v3i1.472

Abstract

This paper presents a novel approach to recommendation systems by integrating emotion analysis from user reviews with deep learning techniques. We propose an Emotion-Driven Deep Learning Recommendation System (ED-DLRS) that mines user preferences and predicts scores by leveraging both the semantic content and emotional context of reviews. Our framework incorporates a dual-perspective emotion modeling strategy, considering both global emotion influence across the user base and localized emotional patterns of individual users. We introduce a deep neural network architecture that effectively fuses these emotion representations with latent user and item features. Extensive experiments on real-world datasets demonstrate that ED-DLRS significantly outperforms state-of-the-art recommendation methods, particularly in addressing the cold-start problem and data sparsity issues. Our results show an average improvement of 12% in prediction accuracy and a 15% increase in recommendation relevance compared to baseline models. Furthermore, we provide insights into the impact of different types of emotions on recommendation quality and user satisfaction. This work opens new avenues for emotion-aware, personalized recommendation systems that can enhance user experience in e-commerce and content delivery platforms
Personalized UI Layout Generation using Deep Learning: An Adaptive Interface Design Approach for Enhanced User Experience Xiaoan Zhan; Yang Xu; Yingchia Liu
Journal of Artificial Intelligence and Development Vol. 3 No. 1 (2024): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v3i1.544

Abstract

This study presents a new approach to personalized UI design using deep learning techniques to improve user experience through interface customization. We propose a hybrid VAE-GAN architecture combining variational autoencoders and generative adversarial networks to create coherent and user-specific UI layouts. The system includes user-friendly electronic models that capture personal preferences and behaviors, enabling real-time personalization of interactions. Our methodology leverages large-scale UI design datasets, and user interaction logs to train and evaluate the model. Experimental results demonstrate significant improvements in layout quality, personalization accuracy, and user satisfaction compared to existing approaches. A customer research study with 200 participants from different cultures proves the effectiveness of the personalization model in real situations. The system achieves a personalization accuracy of 0.89 ± 0.03 and a transfer speed of 1.2s ± 0.1s, the most efficient state-of-the-art UI personalization system. In addition, we discuss the theoretical implications of our approach to UI/UX design principles, potential business applications, and ethical considerations around AI-driven identity. This research contributes to advancing adaptive interface design and opens up new ways to integrate deep learning with UI/UX processes
A Personalized Causal Inference Framework for Media Effectiveness Using Hierarchical Bayesian Market Mix Models Xin Ni; Yitian Zhang; Yanli Pu; Ming Wei; Qi Lou
Journal of Artificial Intelligence and Development Vol. 3 No. 1 (2024): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v3i1.546

Abstract

This study presents a novel framework for personalized causal inference in media effectiveness using Hierarchical Bayesian Market Mix Models (ABM). The proposed approach integrates individual-level data with aggregate market information to estimate personalized media effects while addressing the challenges of data sparsity and high dimensionality. By combining the identity layer and the optimization process in a Bayesian hierarchical model, the model captures heterogeneity across consumers and provides robust predictions of individual causality. Affect different media. The framework is used for e-commerce business data, which includes 500,000 customers across 50 markets in 24 months. The model shows better prediction performance than the integrated business model, with a 30.4% reduction in RMSE. Empirical results reveal significant heterogeneity in media effectiveness across channels and consumer segments. Email marketing emerges as the most effective channel on average, followed by TV advertising, digital display ads, and social media engagements. Sensitivity analyses and robustness checks, including alternative prior specifications and placebo tests, support the validity of the estimated causal effects. The findings provide valuable insights for media planning and marketing strategy, highlighting the importance of tailored budget allocation and campaign design approaches. This research contributes to the growing body of literature on personalized marketing analytics and offers a powerful tool for estimating individualized media effects in complex marketing environments.
Integrating Artificial Intelligence with KMV Models for Comprehensive Credit Risk Assessment Kangming Xu; Vishal Jangir
Journal of Artificial Intelligence and Development Vol. 3 No. 2 (2024): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v3i2.554

Abstract

With the continuous development of artificial intelligence and various new intelligent algorithm technologies, the business contacts between various institutions within financial enterprises are gradually increasing, and traditional financial risk management can no longer adapt to the current status quo in the era of big data. The lack of information sharing among institutions can reduce the efficiency of financial management and adversely affect the operation of enterprises. At present, financial credit risk mainly includes credit risk, market risk and operational risk. Credit risk relates to the possibility that a borrower will not be able to repay loans or debts on time, market risk covers potential losses caused by market volatility, price changes and adverse events, while operational risk includes risks such as internal operational errors, technical failures and fraud, which may adversely affect the normal operations and financial condition of a financial institution. These risk factors need to be integrated and managed in the financial sector to ensure financial stability and customer trust. Therefore, this paper aims to establish a KMV financial credit risk model, continuously strengthen the internal risk management of enterprises, achieve management modeling and a good KMV algorithm mechanism, and realize the cooperation and stickiness between customers and enterprises, so as to avoid unnecessary financial risks

Page 2 of 3 | Total Record : 24

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