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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JCES (Journal of Character Education Society) TEKNOLOGIA AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri Budimas : Jurnal Pengabdian Masyarakat Journal of Computer, Electronic, and Telecommunication (COMPLETE) BEMAS: Jurnal Bermasyarakat Journal of Technology and Informatics (JoTI) Aptekmas : Jurnal Pengabdian Kepada Masyarakat
Aulia Rahma Annisa
Program Studi Teknik Elektro, Universitas Pertamina
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Journal : TEKNOLOGIA

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METODE KOMPUTASI MURAH UNTUK SINKRONISASI FASA OPTIK PADA OPTICAL BEAMFORMING NETWORKS Herminarto Nugroho; Wahyu Kunto Wibowo; Aulia Rahma Annisa
JURNAL TEKNOLOGIA Vol 1 No 2 (2019): Teknologia
Publisher : Aliansi Perguruan Tinggi Badan Usaha Milik Negara (APERTI BUMN)

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Abstract

Optical Beamforming Networks (OBFNs) is used to optimized the received signal power in airplane-satellite communication by controlling the signal beams towards the desired direction. The control of these OBFNs is a difficult and very complex nonlinear problem. The complexity of OBFN can be reduced by lowering the bandwidth of the modulated optical signals at its inputs. It can be achieved by performing optical single-sideband suppressed-carrier (SSB-SC) modulation. However, there is potential problem due to the fact that the phase relation between the optical carrier and the remaining sideband is lost when the group delay response is only optimized for the sideband frequency range. Optical phase synchronization is used to correct this issue by adding extra phase shifters. However, there is not any simple yet effective method to determine the value of the extra phase shifters needed for a certain OBFN. This paper propose a simple and computationally cheap feedback approximation approach to solve this problem.
PENGATURAN TORSI PADA HYBRID ELECTRIC VEHICLE (HEV) MENGGUNAKAN METODE NEURO-FUZZY PREDIKTIF Aulia Rahma Annisa; Wahyu Kunto Wibowo; Nita Indriani Pertiwi
JURNAL TEKNOLOGIA Vol 2 No 1 (2019): Jurnal Teknologia
Publisher : Aliansi Perguruan Tinggi Badan Usaha Milik Negara (APERTI BUMN)

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Abstract

In hybrid electric vehicle, there are two sources of energy, namely Internal Combustion Engine (ICE) and DC motor. ICE as the prime mover has a smaller capacity than conventional vehicles because of the work is assisted by the DC motor. The DC motor acts to help internal combustion engine reach the torque and the speed as desired. Torque control of hybrid electric vehicle provide of how much torque required by the DC motor to assist the performance of ICE. When the ICE are not able to maintain the speed, the DC motor will help to provide the power. To overcome these problems, neuro-fuzzy predictive methods using inverse models are used. Neuro-fuzzy controller has the advantage of adaptability when the parameters in the system change. HEV itself requires a quick response therefore predictive controller used in order to predict the future value of the torque. Testing results showed that neuro-fuzzy predictive method which combines neurofuzzy controller with inverse models, able to assist ICE follows the reference model. The use of neuro-fuzzy predictive showed better control performance. This is shown from the speed response in 0.25 seconds able to produce a torque of 0.161 N-m, so that the HEV system can follow the desired reference model.
Co-Authors Ade Irawan Adhitya, Ryan Yudha Ardiana, Mirza Ardiansyah Al Farouq Ariana Yunita Dio Ifan Auliya Dwi Nur Azizah Hamidah Erwin Setiawan Fayola Liyani Firmansyah, Muhammad Rizal Hanny Megawati Rosalinda Helmy Widyantara Hendy Briantoro Herminarto Nugroho Herminarto Nugroho Khodijah Amiroh Lora Khaula Amifia Mohammad Yanuar Hariyawan Montolalu, Billy Muhaimin, Muhammad Yanuar Muhamad Koyimatu Nanda Mesa Nur Aryabawa Nilna Agnia Nita Indriani Pertiwi Pangestu Widodo, Pangestu Rahayu, Putri Nur Raynor Cavan Suharto Sholikhah, Evi Nafiatus Susanty, Meredita Tasmi Tasmi Teguh Aryo Nugroho Ubaidillah Umar, Ubaidillah Wahyu Kunto Wibowo Wahyu Kunto Wibowo Wahyu Kunto Wibowo Yudha Adhitya, Ryan Yudi Andika Zakariya, Naufal Zunaidi, Rizqa Amelia