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All Journal Georafflesia : Artikel Ilmiah Pendidikan Geografi Green Engineering: Journal of Engineering and Applied Science
Bentar Priyopradono
Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Social Sciences

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Visualization of Science Literacy in Learning Based on STEM at Natural Schools, Bengkulu Indonesia Dodo Sutardi; Widya Kartika Sari; Bentar Priyopradono
Jurnal Georafflesia: Artikel Ilmiah Pendidikan Geografi Vol 7 No 2 (2022)
Publisher : Universitas Prof. Dr. Hazairin, S.H

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32663/georaf.v7i2.3166

Abstract

One of the problems of education in Indonesia today is the low literacy of mathematics, science and reading. STEM (Science Technology Engineering Math) education is an effort made by almost all countries to improve scientific, technological, and mathematical literacy. Indonesia developed the 2013 curriculum to improve literacy. Several research results on STEM education in Indonesia conclude that STEM education has been well understood by most teachers in Indonesia. However, in the implementation of learning, not many have implemented it. This paper will visualize the implementation of STEM education in improving students' literacy skills, and help achieve the Alam Bengkulu school's vision, "Being a reference for the world of education in Bengkulu in forming a generation that loves science." The material is packaged in multimedia in the form of text, animation, sound, and video. , in accordance with the demands of learning materials. The research was conducted at the Bengkulu Nature School. The goal is to popularize the implementation of STEM education which has not been widely applied by teachers. For example, implementing learning by integrating Science, Technology, Engineering and Mathematics, increasing scientific literacy, through local content in natural schools.
Development of a Digital Twin Based Smart Green Building Energy Management Model Integrating IoT Sensors and Predictive Sustainability Analytics Asro Asro; Solihin Solihin; John Chaidir; Febri Adi Prasetya; Tuti Susilawati; Muhamad Furqon; Bentar Priyopradono
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.287

Abstract

Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.
Co-Authors Asro Asro DODO SUTARDI Febri Adi Prasetya John Chaidir Muhamad Furqon Solihin Solihin TUTI SUSILAWATI widya kartika sari