Claim Missing Document
Check
Articles

Found 22 Documents
Search

AIoT Driven Smart Solar System for Real Time Predictive Sustainable Energy Management Indrawan, Rizki; Very, Eka Dawn; Tribuana, Dhimas; Nabila, Efa Ayu
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.968

Abstract

The rapid expansion of solar photovoltaic (PV) technologies has increased the demand for intelligent, adaptive, and data-driven energy management systems. However, conventional and IoT only solar infrastructures still face limitations, including inefficient energy distribution, delayed fault detection, and an inability to respond dynamically to fluctuating environmental conditions. This study proposes an AIoT-based Smart Solar System that integrates IoT-enabled sensing modules with artificial intelligence for real-time monitoring, predictive analytics, and autonomous control. The system employs a distributed architecture consisting of edge devices, cloud analytics, and machine learning models particularly Long Short-Term Memory (LSTM) networks and regression-based predictors to enhance forecasting accuracy and operational responsiveness. The objective of this research is to improve power utilization, predictive reliability, and maintenance efficiency within solar energy systems. Experimental results demonstrate a 22.8% increase in power utilization, a 17% reduction in maintenance downtime, and a forecasting accuracy of 95.2% (R2 = 0.952). These findings indicate that AIoT integration significantly enhances energy intelligence, system reliability, and sustainability. Overall, the proposed architecture establishes a scalable foundation for next generation renewable energy systems capable of self learning, adaptive optimization, and real-time decision making.
Utilizing Blockchain for Trustworthy and Transparent AI Decision Making Herman Herman; Rohim Rohim; Rizki Indrawan; Chua Toh Hua
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v6i1.1031

Abstract

The increasing adoption of AI in critical sectors such as healthcare, finance, transportation, and public services raises significant challenges related to transparency, accountability, and trust in automated decision-making processes, particularly since many AI models still operate as black boxes that are difficult to interpret and audit. This study investigates the potential of integrating blockchain technology to enable trustworthy and transparent AI decision-making and is conducted under the framework to systematically design, implement, and evaluate the proposed solution. The proposed framework records AI inference results and relevant metadata onto the blockchain through smart contracts to ensure data immutability and traceability. A prototype system is developed and evaluated using a mixed-method approach, combining qualitative analysis of transparency and auditability with quantitative measurements of system performance such as latency and overhead. The results demonstrate that blockchain integration significantly enhances auditability, data integrity, and user trust compared to conventional AI systems. However, several limitations are identified, including scalability issues, transaction costs, and increased latency caused by on-chain recording processes. Despite these challenges, the proposed approach shows strong potential to improve the accountability of AI systems in high-risk environments and contributes a practical framework along with empirical insights for organizations seeking to adopt transparent and reliable AI, while also opening opportunities for further development through architectural optimization and the adoption of layer-2 blockchain technologies.