Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal Aptisi Transactions on Management International Transactions on Artificial Intelligence (ITALIC)
Archa Erica
Adi Journal Incorporation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science

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Application of AI in Optimizing Energy and Resource Management: Effectiveness of Deep Learning Models Agus Kristian; Thomas Sumarsan Goh; Ahmad Ramadan; Archa Erica; Sondang Visiana Sihotang
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.530

Abstract

In the era of globalization and rapid industrial growth, energy efficiency and resource management are crucial to addressing complex environmental and economic challenges. Efficient management reduces costs and contributes to sustainability. Technological advancements in Artificial Intelligence (AI) enhance energy efficiency and resource management through faster data analysis, better predictions, and automation. Despite progress, challenges like inaccurate energy demand predictions and inefficient resource allocation persist. This study explores AI's role in improving energy and resource management efficiency, focusing on prediction, optimization, and automation using Deep Learning approaches, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The findings show that AI models significantly enhance efficiency and sustainability by providing accurate predictions and automation recommendations. This research underscores AI's practical relevance, suggesting companies integrate these technologies to optimize energy use and achieve sustainability goals.
Gen Z Consumer Self Protection Against AI Algorithm Manipulation in E-Commerce Ependi Ependi; Mona Karina; Fery Hernaningsih; Hasan Basri; Archa Erica
APTISI Transactions on Management (ATM) Vol 10 No 3 (2026): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/7mh3jm73

Abstract

AI integration in e-commerce has significantly impacted consumer decision-making through tailored algorithms. Research Gap However, existing literature heavily focuses on quantitative acceptance models in Western contexts, leaving a significant gap in understanding how emerging market consumers, particularly Generation Z students, actively exercise autonomy against non-transparent AI practices and algorithmic manipulation. This study explores the self-protection strategies of Generation Z consumers in Jakarta navigating AI-driven platforms. Methodology Utilizing a Grounded Theory Approach, data were gathered through in-depth interviews with 11 participants to construct an inductive framework. Review & Theoretical Contribution The research introduces the Theory of Consumer Digital Self-Protection and the Capability-Trust-Context (CTC) Adaptation Model, demonstrating that digital self-protection is an adaptive, multidimensional process driven by cognitive capacity, trust mechanisms, and behavioral control. Practical Implications for HEIs. The findings highlight critical implications for Higher Education Institutions (HEIs) to advance SDG 4 (Quality Education) by integrating critical algorithmic literacy and AI ethics into university curricula, thereby empowering students to maintain decision-making autonomy in an AI-dominated digital economy.
Co-Authors Agus Kristian Ahmad Ramadan Ependi Ependi Fery Hernaningsih Hasan Basri Mona Karina Sondang Visiana Sihotang Thomas Sumarsan Goh