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All Journal Journal of Computer Science Advancements Journal of Moeslim Research Technik Journal of Selvicoltura Asean Journal of Loomingulisus ja Innovatsioon Techno Agriculturae Studium of Research Journal of Biomedical and Techno Nanomaterials Journal of Social Entrepreneurship and Creative Technology Journal of Humanities Research Sustainability
Zhang Li
Peking University
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Other

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Journal : journal of computer science advancements

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USE OF ARTIFICIAL INTELLIGENCE IN PREDICTING ELECTRICITY NEEDS IN SMART CITIES Aldi Bastiatul Fawait; Zhang Li; Sara Hussain
Journal of Computer Science Advancements Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i1.1620

Abstract

The rapid urbanization and adoption of smart city technologies have led to increasing complexities in managing electricity demand. Traditional methods of forecasting electricity needs often fail to accommodate the dynamic and real-time nature of energy consumption in smart cities. Artificial Intelligence (AI) offers a promising approach by leveraging machine learning algorithms and predictive analytics to address these challenges. This study explores the use of AI in predicting electricity needs, focusing on its applicability in optimizing energy distribution and reducing inefficiencies in smart city infrastructures. The research aims to develop an AI-based predictive model to forecast electricity demand using historical and real-time data. The methodology involves data collection from smart meters, weather forecasts, and demographic records, followed by training machine learning algorithms such as Random Forest, Support Vector Machines, and Neural Networks. Performance metrics, including prediction accuracy, computational efficiency, and scalability, were analyzed to evaluate the model's effectiveness. Results indicate that AI-based models outperform traditional forecasting methods, achieving an average prediction accuracy of 92%. Neural Networks demonstrated the highest performance, particularly in handling complex and nonlinear data patterns. The AI model also showcased scalability by adapting to increasing datasets without significant degradation in performance. The study concludes that AI is a transformative tool for predicting electricity needs in smart cities. By enhancing forecast accuracy and enabling efficient energy distribution, AI contributes to sustainable urban development and smarter energy management systems.
OPTIMIZATION OF KSM MAJU JAYA OPERATIONS THROUGH INFORMATION TECHNOLOGY INNOVATION FOR DATA MANAGEMENT AND SALES OF ENVIRONMENTALLY FRIENDLY PRODUCTS Indra Satriadi; Ahmad Zarkasih; Krisna Natawijaya; Zhang Li
Journal of Computer Science Advancements Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i2.3668

Abstract

Community-based waste management through Kelompok Swadaya Masyarakat (KSM) Maju Jaya plays a vital role in the circular economy by transforming organic refuse into eco-friendly products like natural dish soap. Manual recording systems and conventional marketing strategies currently hinder operational efficiency, leading to data inaccuracies and limited market reach. This research aims to optimize KSM operations by developing an integrated web-based management information system focused on data management and sales. The study employs a Research and Development (R&D) methodology, incorporating user requirement identification, iterative UI/UX prototyping using Figma, and functional verification through blackbox testing. Results demonstrate that the developed system provides a systematic, transparent, and accurate platform for tracking production, stock, and transactions. The web-mobile responsive design ensures accessibility across various devices, significantly reducing human error and accelerating report generation. This research concludes that digital transformation through user-centric information technology effectively enhances the institutional capacity and sustainability of community-led environmental initiatives. Implementation of this system serves as a scalable model for other grassroots organizations transitioning toward professionalized digital management.
Co-Authors Ahmad Zarkasih Aldi Bastiatul Fawait Fawait Andres Villanueva Ardi Azhar Nampira Ardi Azhar Nampira Arnes Yuli Vandika Carlos Fernandez Chen Mei Indra Satriadi Krisna Natawijaya Li Wei Liu Yang Nurhayati Nurhayati Nurlatipah Nurlatipah Sara Hussain Sun Wei Wang Jing Wang Jun Yang Xiang Zhou Hui