Pengembangan Kerangka Kerja Manajemen Energi Berbasis Cloud Real-Time Menggunakan Industrial Energy Performance Index untuk Implementasi ISO 50001 pada Industri Tekstil
DOI:
https://doi.org/10.58344/locus.v5i8.6164Keywords:
Sistem Manajemen Energi, Komputasi Awan, Industrial Internet of Things, Industrial Energy Performance Index, ISO 50001Abstract
Efisiensi energi menjadi tantangan penting pada sektor manufaktur akibat meningkatnya biaya energi, tuntutan efisiensi operasional, serta kebutuhan terhadap praktik industri berkelanjutan. Industri tekstil merupakan salah satu sektor dengan konsumsi energi listrik yang tinggi karena memiliki proses produksi berkelanjutan dan karakteristik beban listrik yang kompleks. Namun, sistem monitoring energi konvensional masih banyak bergantung pada pencatatan manual serta belum mampu menyediakan evaluasi performa energi secara terintegrasi dan real-time. Penelitian ini bertujuan untuk mengembangkan dan memvalidasi Real-Time Cloud-Based Energy Management Framework (RCEMF) yang terintegrasi dengan Industrial Energy Performance Index (IEPI) untuk mendukung pemantauan energi, evaluasi performa energi, serta pengambilan keputusan dalam implementasi ISO 50001 pada industri tekstil. Penelitian menggunakan metode Research and Development (R&D) dengan pendekatan eksperimental yang mencakup perancangan arsitektur sistem, pengembangan akuisisi data otomatis, implementasi komunikasi Modbus RTU, integrasi basis data berbasis cloud, pengembangan IEPI, dan validasi framework. Hasil penelitian menunjukkan bahwa RCEMF mampu mengintegrasikan digital power meter, IoT Gateway, komputasi awan, analitik energi, dan sistem pendukung keputusan dalam satu platform terpadu. Framework ini mampu mengurangi waktu akuisisi data lebih dari 96%, mencapai tingkat keandalan komunikasi sebesar 99,9%, serta melakukan evaluasi performa energi secara real-time. IEPI juga mampu merepresentasikan perubahan kondisi operasi melalui integrasi indikator SEC, PF, LF, dan MDU. Kesimpulannya, RCEMF menjadi solusi digital yang efektif untuk meningkatkan sistem manajemen energi, mendukung implementasi ISO 50001, dan meningkatkan efisiensi energi berkelanjutan pada industri tekstil.
References
Afrin, S. (2025). Industrial Internet of Things: Implementations, challenges, and potential solutions across various industries. Computers in Industry. https://doi.org/10.1016/j.compind.2025.104317
Agustiarmi, W. (2025). Real-Time IoT-based Energy Power Consumption Monitoring System for Smart Homes. 15(5).
Ahmed, A., ul Hasan, E., & Hasseni, S. E. I. (2025). Smart and Sustainable: A Global Review of Smart Textiles, IoT Integration, and Human-Centric Design. Sensors. https://doi.org/10.3390/s25237267
Awad, A. H. (2024). Low-cost IoT-Based sensors dashboard for monitoring the state of health of mobile harbor cranes: Hardware and software description. Heliyon, 10(22). https://doi.org/10.1016/j.heliyon.2024.e40239
Bonavolontà, F., Liccardo, A., Mottola, F., & Proto, D. (2025). Real-Time Monitoring of Energy Contributions in Renewable Energy Communities Through an IoT Measurement System. Sensors, 25(5). https://doi.org/10.3390/s25051402
Choudhary, A. (2024). Internet of Things: a comprehensive overview, architectures, applications, simulation tools, challenges and future directions. Discover Internet of Things. https://doi.org/10.1007/s43926-024-00084-3
Eber, R., Kollmann, D., Aschenbrenner, D., Hentsch, M., Schwarzer, S., & Stricker, N. (2023). IIOT visualization applications based on augmented reality - practical approach for easy implementation. Procedia CIRP, 964–967. https://doi.org/10.1016/j.procir.2023.09.108
Ficili, I., Giacobbe, M., Tricomi, G., & Puliafito, A. (2025). From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI. Sensors, 25(6). https://doi.org/10.3390/s25061763
Fitzgerald, P., Therkelsen, P., Sheaffer, P., & Rao, P. (2023). Deeper and persistent energy savings and carbon dioxide reductions achieved through ISO 50001 in the manufacturing sector. Sustainable Energy Technologies and Assessments, 57. https://doi.org/10.1016/j.seta.2023.103280
G., K. (2024). Smart energy management: real-time prediction and optimization for IoT-enabled smart homes. Cogent Engineering, 11(1). https://doi.org/10.1080/23311916.2024.2390674
Hanifi, S., Alkali, B., Lindsay, G., & McGlinchey, D. (2025). Optimizing Energy and Air Consumption in Smart Manufacturing: An Industrial Internet of Things-Based Monitoring and Efficiency Enhancement Solution. Applied Sciences (Switzerland), 15(6). https://doi.org/10.3390/app15063222
Harsanto, B., Primiana, I., Sarasi, V., & Satyakti, Y. (2023). Sustainability Innovation in the Textile Industry: A Systematic Review. Sustainability (Switzerland). https://doi.org/10.3390/su15021549
Ke, M.-T., Yeh, C.-H., & Su, C.-J. (2017). Cloud computing platform for real-time measurement and verification of energy performance. Applied Energy, 188, 497–507. https://doi.org/10.1016/j.apenergy.2016.12.034
Kraus, S., Jones, P., Kailer, N., Weinmann, A., Chaparro-Banegas, N., & Roig-Tierno, N. (2021). Digital Transformation: An Overview of the Current State of the Art of Research. Sage Open, 11(3). https://doi.org/10.1177/21582440211047576
Lamas, V., Cortiñas, A., & Luaces, M. R. (2025). Low-code framework for IoT data warehousing and visualization. Computers & Geosciences, 205. https://doi.org/10.1016/j.cageo.2025.105998
Magara, T., & Phahlane, M. (2026). Unlocking IIoT Potential: A Systematic Review of AI Applications, Adoption Drivers, and Implementation Barriers. Applied AI Letters, 7(1). https://doi.org/10.1002/ail2.70017
Mansouri, M., Eskandari, M., Asadi, Y., & Savkin, A. (2024). A cloud-fog computing framework for real-time energy management in multi-microgrid system utilizing deep reinforcement learning. Journal of Energy Storage, 97. https://doi.org/10.1016/j.est.2024.112912
Mirani, A. A., Awasthi, A., O’Mahony, N., & Walsh, J. (2024). Industrial IoT-Based Energy Monitoring System: Using Data Processing at Edge. Internet of Things, 5(4), 608–633. https://doi.org/10.3390/iot5040027
Onu, P., Pradhan, A., & Mbohwa, C. (2023). The potential of industry 4.0 for renewable energy and materials development – The case of multinational energy companies. Heliyon, 9(10). https://doi.org/10.1016/j.heliyon.2023.e20547
Preface. (2026). Journal of Physics: Conference Series, 3246(1), 11001. https://doi.org/10.1088/1742-6596/3246/1/011001
Rajagopalan, A. (2024). Empowering power distribution: Unleashing the synergy of IoT and cloud computing for sustainable and efficient energy systems. Results in Engineering. https://doi.org/10.1016/j.rineng.2024.101949
Sakti, S., Sopha, B. M., & Saputra, E. S. T. (2021). Energy Efficiency Analysis in a Textile Company Using DMAIC Approach. IOP Conference Series: Materials Science and Engineering, 1096(1), 12007. https://doi.org/10.1088/1757-899x/1096/1/012007
Sequeira, H., Carreira, P., Goldschmidt, T., & Vorst, P. (2014). Energy Cloud: Real-Time Cloud-Native Energy Management System to Monitor and Analyze Energy Consumption in Multiple Industrial Sites. 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, 529–534. https://doi.org/10.1109/UCC.2014.79
Singh, A. R. (2025). A scalable cloud-integrated AI platform for real-time optimization of EV charging and resilient microgrid energy management. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-21531-3
Witczak, D., & Szymoniak, S. (2024). Review of Monitoring and Control Systems Based on Internet of Things. Applied Sciences (Switzerland). https://doi.org/10.3390/app14198943
Zahid, A., Leclaire, P., Hammadi, L., Costa-Affonso, R., & El Ballouti, A. (2025). Exploring the potential of industry 4.0 in manufacturing and supply chain systems: Insights and emerging trends from bibliometric analysis. Supply Chain Analytics, 10. https://doi.org/10.1016/j.sca.2025.100108
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