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All Journal IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology International Journal for Applied Information Management International Journal of Advanced Science and Computer Applications Journal of Educational Technology and Learning Creativity Data Science Insights Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Maidin, Siti Sarah
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Analyzing the Evolution of AIGenerated Art Styles Using Time Series Analysis: A Trend Study on NFT Artworks Maidin, Siti Sarah; Yang, Qingxue; Samson, A Sunil
Journal of Digital Market and Digital Currency Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i2.32

Abstract

This study investigates the development of AI-generated art styles within the growing non-fungible token (NFT) market. Using time series analysis, the research identifies key trends and shifts in art styles from 2022 to 2024, revealing how various art forms, algorithms, and mediums evolved in response to technological advancements and market forces. Data was collected from a sample of 10,000 NFT artworks, categorized by creation date, style, and algorithm usage. Exploratory Data Analysis (EDA) techniques, including line graphs and heatmaps, were employed to visualize and interpret trends across different art styles and AI tools. Results indicate a significant increase in the popularity of styles like surrealism and realism, with deepdream and GANpaint algorithms being frequently associated with these styles. Stacked area charts further highlighted the proportional growth of art styles over time, providing insights into both short-term popularity spikes and long-term trends. The findings suggest that the integration of AI algorithms significantly influenced the rise of specific art genres, with certain algorithms correlating strongly with particular styles. Practical implications for artists and collectors include the potential for data-driven insights to guide creative choices and investment strategies. The study's limitations, such as the lack of broader market data, provide a foundation for future research to explore the intersection of AI-generated art, NFT marketplaces, and cultural influences. The paper concludes that AI and NFTs are reshaping the traditional art market, presenting new opportunities for creativity, ownership, and artistic value in a digital age.
Price Trend Prediction and Discount Optimization for Video Games in Online Stores Using XGBoost and Time-Series Analysis: A Data Mining Approach for Metaverse-Driven Market Insights Maidin, Siti Sarah; Yahya, Norzariyah
International Journal Research on Metaverse Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i4.41

Abstract

This research explores the application of data mining techniques, specifically XGBoost, to predict game pricing trends and optimize discount strategies within the digital gaming market. Game prices are influenced by various factors, including production costs, market demand, and promotional strategies. This study analyzes historical pricing data from multiple online stores to identify key pricing patterns and factors that influence price changes over time. The model developed in this study predicts game prices by incorporating features such as retail price, discount percentages, past price trends (lags), and other time-based features. The findings reveal that retail price and recent price trends (e.g., 7-day rolling averages) are the most influential features in predicting future prices. Additionally, discount strategies significantly impact game sales, with certain discount ranges showing higher effectiveness in driving consumer purchases. The model also demonstrates variability in prediction accuracy, particularly at higher price points, highlighting the challenges of capturing complex price fluctuations in a dynamic digital marketplace. The significance of this study extends to the Metaverse market, where pricing and the use of digital assets like non-fungible tokens (NFTs) play a critical role. The model's application could aid in optimizing pricing strategies within virtual economies, enhancing both the consumer experience and retailer profitability. Future work includes integrating additional features such as user reviews and exploring its application to Metaverse game platforms. The practical implications of this research are significant for online game retailers looking to leverage data-driven insights for more effective pricing and promotional strategies.
AI-Driven Text Analysis and Generation for Green Energy Applications Ahmed, Saif Saad; Mahdi, Mohammed Fadhil; Hammad, Qudama Khamis; Mahdi, Ammar Falih; Alfalahi, Saad.T.Y.; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1745

Abstract

The rapid growth of the green energy sector has produced a massive volume of textual data, creating significant challenges for information extraction and decision support. This study investigates the application of state-of-the-art Natural Language Processing (NLP) models, specifically BERT and GPT-4, to automate and enhance policy drafting, market analysis, and academic research clustering. We evaluated these models on a corpus of over 200,000 energy-related documents, using a structured computational workflow to measure performance on semantic coherence, factual reliability, and processing efficiency. The results demonstrate substantial improvements over manual methods. The AI-driven approach reduced policy drafting time by 39% and error rates by over 58%, while increasing semantic alignment to 93.5%. In market report synthesis, the models improved topic extraction accuracy by over 10% and reduced summary generation time by 38%. For academic literature, thematic clustering accuracy reached 92.3%, with a 44% reduction in processing time. These findings validate that fine-tuned NLP models can serve as powerful analytical tools in the sustainable energy domain, enabling institutions to navigate complex regulatory and technical information more effectively. By providing a practical demonstration of how automated NLP solutions can augment human expertise, this work contributes to the applied use of AI in achieving global green energy objectives, while also considering the associated methodological and ethical implications.
Artificial Intelligence, Robotics, and Automation in Renewable Energy Systems Ismail, Laith S.; Faraj, Lydia Naseer; Mohammed, Doaa Thamer; Taher, Nada Adnan; Hafedh, Milad Abdullah; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1746

Abstract

The transition to clean energy requires intelligent solutions to mitigate resource intermittency, grid instability, and operational inefficiencies. This paper presents and validates an integrated framework that leverages Artificial Intelligence (AI), robotics, and automation to optimize the performance and sustainability of renewable energy assets. The study employs machine learning models (LSTM, SVM, ANN) for energy forecasting, autonomous robotic platforms for real-time inspection, and advanced algorithms (MPC, Reinforcement Learning) for grid control. The framework's transparency and ethical compliance were validated using explainability techniques (SHAP, LIME) and cybersecurity protocols. Experimental results demonstrate significant performance gains across all domains. The AI models achieved high forecasting accuracy, with the LSTM model for wind power reaching a Mean Absolute Percentage Error (MAPE) of just 2.41%. Robotic inspections improved system uptime by nearly 30% and accelerated fault detection. In grid management simulations, a Reinforcement Learning-based control strategy proved most effective, reducing energy losses by 10.6% and control costs by 17.5%. This cross-disciplinary research illustrates the powerful synergy between intelligent software and advanced hardware in creating more reliable, efficient, and ethically grounded energy systems. The findings establish a scalable and validated foundation for next-generation renewable energy operations and highlight future pathways for enhancing human-machine collaboration in the pursuit of global sustainability targets.
Big Data and Data Mining for Efficient Energy Storage and Management Nazar, Mustafa; Ali, Zaid Ghanim; Adnan, Kahtan Mohammed; Khalil, Ibraheem Mohammed; Nassar, Waleed; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1759

Abstract

The rapid expansion of decentralized and renewable energy systems necessitates intelligent strategies for energy storage and management. This paper presents a comprehensive framework that leverages big data analytics and data mining to optimize energy storage systems within smart grid architectures. By integrating high-frequency data from IoT-enabled Li-Ion batteries, flow batteries, supercapacitor arrays, and hybrid systems, our methodology enhances storage efficiency, predictive accuracy, and fault detection. The approach uniquely combines an ensemble forecasting model (Random Forest and XGBoost), which achieved a 97% R² score in predicting energy demand, with Gaussian Mixture Models for consumer pattern clustering and canonical correlation analysis to model the impact of environmental variables. Validation on real-world datasets demonstrates significant performance gains without additional hardware. For instance, algorithmic optimization improved the round-trip efficiency of a Hybrid Battery Energy Storage System from 86.7% to 93.3% and a Li-Ion battery by 7%. The study underscores the critical influence of contextual variables like temperature and humidity on state-of-charge stability. Furthermore, the analytical framework demonstrated a 50% increase in system throughput (from 34 to 51 tasks/sec) after optimization. This research provides a replicable, data-driven model for deploying intelligent analytics in both microgrid and industrial-scale settings, paving the way for more adaptive and resilient energy infrastructures. Future work will explore edge computing and reinforcement learning to further enhance scalability and autonomy.
Blended Learning for Character Education: Integrating Tri Hita Karana Wisdom to Develop Graduate Competencies Aligned with SDGs Astawan, I Gede; Utama, I Dewa Gede Budi; Paramartha, Wayan Eka; Abdurahman, Ayi; Maidin, Siti Sarah
Journal of Educational Technology and Learning Creativity Vol. 3 No. 2 (2025): December
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v3i2.2498

Abstract

Purpose of the study: This study examines the effectiveness of a technology-enhanced character education model based on the Balinese Tri Hita Karana philosophy in developing primary students’ competencies aligned with SDG 4. Using blended learning, it addresses gaps in research on culturally grounded technology integration in character education. Methodology: This quasi-experimental study involved 92 fifth-grade students from four primary schools in Karangasem, Bali, comparing a Technology-Enhanced Tri Hita Karana Character Education Model with conventional instruction. Using LMS, digital portfolios, gamification, and learning analytics, competencies were assessed through questionnaires, portfolios, and observations, with data analyzed using t-tests, ANCOVA, and thematic analysis. Main Findings: The experimental group demonstrated significantly higher graduate competency scores (M=81.89, SD=9.78) compared to the control group (M=70.02, SD=10.02), t(90)=5.98, p<.001, Cohen's d=1.25, indicating a large effect size. Analysis of digital portfolio data revealed enhanced self-regulated learning behaviors and deeper engagement with character development activities. Qualitative analysis showed that students appreciated the gamification elements and found the LMS-mediated learning more engaging and meaningful. Learning analytics data indicated consistent progress tracking and timely teacher interventions. Novelty/Originality of this study: The technology-enhanced Tri Hita Karana character education model effectively develops primary students’ competencies through culturally grounded digital pedagogy. This study strengthens evidence on technology-mediated character education and shows how local wisdom supports global educational goals. It highlights the need for teacher professional development and policy support to implement culturally responsive, technology-enhanced, and sustainable blended learning in primary education.
A Hybrid Ensemble Framework Combining Transformer Networks, CNN-LSTM, and Prophet for Multi-Horizon Bitcoin Price Prediction Using 1-Minute Time Series Data Maidin, Siti Sarah; Hemalatha, M.; Sun, Jing
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.57

Abstract

Bitcoin price forecasting at one-minute frequency presents significant challenges due to rapid volatility and noise in high-frequency markets. This study proposes a hybrid ensemble framework integrating a CNN-LSTM model, a Transformer architecture, and a Prophet-based component to perform multi-horizon prediction using 500,000 one-minute BTC/USD observations. The model is evaluated across 5-minute, 15-minute, and 30-minute horizons. The results show that the ensemble achieves the best performance for the 5-minute horizon with MAE = 41.565 USD, RMSE = 60.722 USD, and MAPE = 0.156. This outperforms the CNN-LSTM model (MAE = 47.838 USD) and the Transformer model (MAE = 53.733 USD). Performance decreases at the 15-minute horizon due to Transformer instability, where the ensemble reaches MAE = 269.347 USD and the Transformer reaches MAE = 530.429 USD. At the 30-minute horizon, performance stabilizes, with the ensemble producing MAE = 84.481 USD, close to the CNN-LSTM result (MAE = 84.186 USD) and better than the Transformer (MAE = 153.887 USD). These findings indicate that the hybrid ensemble is highly effective for ultra-short-term forecasting but requires horizon-specific tuning to remain stable for medium-range intervals.
Co-Authors Abbas, Elaf Sabah Abbas, Intesar Abdul Radhi, Rafah Hassan Abdul-Kareem, Bushra Jabbar Abdullah Abdullah Adnan, Kahtan Mohammed Ahmed, Mohsen Ali Ahmed, Saif Saad Ajitha, Ajitha Al Hilfi, Thamer Kadum Yousif Al-Dosari, Ibraheem Hatem Mohammed Alfalahi, Saad.T.Y. Ali, Taghreed Alaa Mohammed Ali, Zaid Ghanim Arthi, R. Attarbashi, Zainab S. Ayi Abdurahman Ayyasy, Yahya Bakar, Normi Sham Awang Abu Binti Abdul Rahim, Yusrina Dhilipan, J. Fallah, Dina Faraj, Lydia Naseer Fauzi, Muhammad Ashraf bin Fauri Ge, Wu Gelar Budiman Govindaraju, S Guangfa, Wu Hadi, Shahd Imad Hafedh, Milad Abdullah Hammad, Qudama Khamis Hamodi Aljanabi, Yaser Issam Hao Wu Haodic, Gao Hemalatha, M. I Dewa Gede Budi Utama I Gede Astawan Indirani, M Indrarini Dyah Irawati Ishak, Wan Hussain Wan Ismail, Laith S. Jafar, Qusay Mohammed Jaleel Maktoof, Mohammed Abdul Jamil, Abeer Salim Janan, Ola Jaya, M. Izham Jeyaboopathiraja, J. Jing Sun Khalil, Baker Mohammed Khalil, Ibraheem Mohammed Kowthalam, Vijay Rathnam Kumar, B.L. Shiva Lie, Ye Luo Jun Mahdi, Ammar Falih Mahdi, Mohammed Fadhil Mahendiran, N Majeed, Adil Abbas Mariajohn, Princess Mohammed, Doaa Thamer Murad, Nada Mohammed Nassar, Waleed Nayef, Hamdi Abdullah Nazar, Mustafa Nivetha, N. Praneesh, M. Priscilla, G Maria Priscilla, G. Maria Rahmafadilla, Rahmafadilla Rizal, Mochammad Fahru Sajid, Wafaa Adnan Salman, Khdier Samson, A Sunil Selvaraj, Poovarasan Shaker, Alhamza Abdulsatar Shanmugam, D.B. Shing, Wong Ling Shivakumar, B L Shivakumar, B. L. Shnain, Saif Kamil Subramanian, Devibala Sumathi, N Sumathi, V. Taher, Nada Adnan Thavamani, S. Triasari, Biyantika Emili Varun, S. T. Vidhya, B. Vijayalakshmi, N. Wan Ishak, Wan Hussain Wayan Eka Paramartha Wei, Jingchuan Wider, Walton Yahya, Norzariyah Yamin, Fadhilah Yang, Qingxue Yi, Ding Yilin, Li Yousif Al Hilfi, Thamer Kadum YULI SUN HARIYANI Zhang Xing Zhao, Zhong Zhaoji, Fu