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All Journal JURNAL KAJIAN TEKNIK ELEKTRO Malcom: Indonesian Journal of Machine Learning and Computer Science
Emilia Roza
Universitas Muhammadiyah Prof DR HAMKA
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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PERANCANGAN PEMBANGKIT TENAGA SURYA FAKULTAS TEKNIK UHAMKA Emilia Roza; Mohammad Mujirudin
Jurnal Kajian Teknik Elektro Vol 4, No 1 (2019): JKTE Vol 4 No 1 (Maret-Agustus 2019)
Publisher : Universitas 17 Agustus 1945 Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1076.581 KB)

Abstract

Gedung Fakultas Teknik Universitas Muhammadiyah Prof. DR HAMKA (FT UHAMKA) terletak di jalan merdeka no 6 Jakarta Timur, terdiri atas 5 lantai. Energi listrik FT UHAMAKA disuplay oleh PLN dengan kapasitas daya sebesar 345 kVA. Sedangkan rata-rata penggunaan listrik FT UHAMK per hari adalah 567.20 kWh. Intensitas radiasi harian di Indonesia rata-rata mencapai 4.8 kWh/m2 sangat berpotensi sebagai sumber penghasil energy listrik jika memanfaatkan pembangkit listrik tenaga surya (PLTS) sebagai catu daya tambahan. Sistem PLTS yang direncanakan sebesar10% system PLTS hybrid dengan suplai listrik PLN untuk mensuplai energy listrik di FT UHAMKA yang direncanakan. Daya PLTS yang akan dibangkitkan perhari adalah 56.723 kWp, yang akan dihasilkan dari 92 panel surya dengan kapasitas panel surya adalah 150 Wp.
Optimization of Energy Consumption in 5G Networks Using Learning Algorithms in Reinforcement Learning Daffa Dean Naufal; Harry Ramza; Emilia Roza
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 2 (2023): MALCOM October 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i2.959

Abstract

The 5G network is an evolution of the 4G LTE (Long Term Evolution) fast internet network that is widely adopted in smart phones or gadgets. 5G networks offer faster wireless internet for various purposes. This research is a literature review of several articles related to machine learning, specifically regarding energy consumption optimization with 5G networks and reinforcement learning algorithms.The results show that various techniques have evolved to overcome the complexity of large energy intake including integration with 5G networks and algorithms have been completed by many researchers. Related to electricity consumption, it was found that during 5G use cases, in a low site visitor load scenario and while reducing power intake takes precedence over QoS, power savings can be made by 80% with 50 ms latency, 75% with 20 ms and 10 ms latency, and 20% with 1 ms latency. If QoS is prioritized, then power savings reach a maximum of five percent with minimum impact in terms of latency. Moreover, with regards to power performance, it has been observed that DQN-assisted motion can offer improvements.
Co-Authors Daffa Dean Naufal Harry Ramza Mohammad Mujirudin