cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dayat Kurniawan
Contact Email
Dayat Kurniawan
Phone
-
Journal Mail Official
redaksi@jurnalet.com
Editorial Address
-
Location
Kota adm. jakarta selatan,
Dki jakarta
INDONESIA
Jurnal Elektronika dan Telekomunikasi
Published by Lembaga Ilmu Pengetahuan Indonesia
ISSN : 14118289     EISSN : 25279955     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering Engineering
Jurnal Elektronika dan Telekomunikasi (JET) is an open access, a peer-reviewed journal published by Research Center for Electronics and Telecommunication - Indonesian Institute of Sciences. We publish original research papers, review articles and case studies on the latest research and developments in the field of electronics, telecommunications, and microelectronics engineering. JET is published twice a year and uses double-blind peer review. It was first published in 2001.
Arjuna Subject : -
Articles 14 Documents
 1   2 
Search results for , issue "Vol 21, No 1 (2021)" : 14 Documents clear
Speech Enhancement Using Deep Learning Methods: A Review Asri Rizki Yuliani; M. Faizal Amri; Endang Suryawati; Ade Ramdan; Hilman Ferdinandus Pardede
Jurnal Elektronika dan Telekomunikasi Vol 21, No 1 (2021)
Publisher : LIPI Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v21.19-26

Abstract

Speech enhancement, which aims to recover the clean speech of the corrupted signal, plays an important role in the digital speech signal processing. According to the type of degradation and noise in the speech signal, approaches to speech enhancement vary. Thus, the research topic remains challenging in practice, specifically when dealing with highly non-stationary noise and reverberation. Recent advance of deep learning technologies has provided great support for the progress in speech enhancement research field. Deep learning has been known to outperform the statistical model used in the conventional speech enhancement. Hence, it deserves a dedicated survey. In this review, we described the advantages and disadvantages of recent deep learning approaches. We also discussed challenges and trends of this field. From the reviewed works, we concluded that the trend of the deep learning architecture has shifted from the standard deep neural network (DNN) to convolutional neural network (CNN), which can efficiently learn temporal information of speech signal, and generative adversarial network (GAN), that utilize two networks training.
Improving Neural Network Based on Seagull Optimization Algorithm for Controlling DC Motor Widi Aribowo; Supari Muslim; Fendi Achmad; Aditya Chandra Hermawan
Jurnal Elektronika dan Telekomunikasi Vol 21, No 1 (2021)
Publisher : LIPI Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v21.48-54

Abstract

This article presents a direct current (DC) motor control approach using a hybrid Seagull Optimization Algorithm (SOA) and Neural Network (NN) method. SOA method is a nature-inspired algorithm. DC motor speed control is very important to maintain the stability of motor operation. The SOA method is an algorithm that duplicates the life of the seagull in nature. Neural network algorithms will be improved using the SOA method. The neural network used in this study is a feed-forward neural network (FFNN). This research will focus on controlling DC motor speed. The efficacy of the proposed method is compared with the Proportional Integral Derivative (PID) method, the Feed Forward Neural Network (FFNN), and the Cascade Forward Backpropagation Neural Network (CFBNN). From the results of the study, the proposed control method has good capabilities compared to standard neural methods, namely FFNN and CFBNN. Integral Time Absolute Error and Square Error (ITAE and ITSE) values from the proposed method are on average of 0.96% and 0.2% better than the FFNN and CFBNN methods.
Front Cover Vol. 21 No. 1 Chaeriah Bin Ali Wael
Jurnal Elektronika dan Telekomunikasi Vol 21, No 1 (2021)
Publisher : LIPI Press

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Design and Realization of Band Pass Filter in K-Band Frequency for Short Range Radar Application Arie Setiawan; Taufiqqurrachman Taufiqqurrachman; Adam Kusumah Firdaus; Fajri Darwis; Aminuddin Rizal; Winy Desvasari; Hana Arisesa; Sulistyaningsih Sulistyaningsih; Prasetyo Putranto; Nasrullah Armi; Dharu Arseno
Jurnal Elektronika dan Telekomunikasi Vol 21, No 1 (2021)
Publisher : LIPI Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v21.1-7

Abstract

Short range radar (SRR) uses the K-band frequency range in its application. The radar requires high-resolution, so the applied frequency is 1 GHz wide. The filter is one of the devices used to ensure only a predetermined frequency is received by the radar system. This device must have a wide operating bandwidth to meet the specification of the radar. In this paper, a band pass filter (BPF) is proposed. It is designed and fabricated on RO4003C substrate using the substrate integrated waveguide (SIW) technique, results in a wide bandwidth at the K-band frequency that centered at 24 GHz. Besides the bandwidth analysis, the analysis of the insertion loss, the return loss, and the dimension are also reported. The simulated results of the bandpass filter are: VSWR of 1.0308, a return loss of -36.9344 dB, and an insertion loss of -0.6695 dB. The measurement results show that the design obtains a VSWR of 2.067, a return loss of -8.136 dB, and an insertion loss of -4.316  dB. While, it is obtained that the bandwidth is reduced by about 50% compared with the simulation. The result differences between simulation and measurement are mainly due to the imperfect fabrication process.

Page 2 of 2 | Total Record : 14

 1   2 

Filter by Year

2021 2021


Reset
Filter By Issues
All Issue Vol 25, No 2 (2025) Vol 25, No 1 (2025) Vol 24, No 2 (2024) Vol 24, No 1 (2024) Vol 23, No 2 (2023) Vol 23, No 1 (2023) Vol 22, No 2 (2022) Vol 22, No 1 (2022) Vol 21, No 2 (2021) Vol 21, No 1 (2021) Vol 20, No 2 (2020) Vol 20, No 1 (2020) Vol 19, No 2 (2019) Vol 19, No 1 (2019) Vol 18, No 2 (2018) Vol 18, No 2 (2018) Vol 18, No 1 (2018) Vol 18, No 1 (2018) Vol 17, No 2 (2017) Vol 17, No 2 (2017) Vol 17, No 1 (2017) Vol 17, No 1 (2017) Vol 16, No 2 (2016) Vol 16, No 2 (2016) Vol 16, No 1 (2016) Vol 16, No 1 (2016) Vol 15, No 2 (2015) Vol 15, No 2 (2015) Vol 15, No 1 (2015) Vol 15, No 1 (2015) Vol 14, No 2 (2014) Vol 14, No 2 (2014) Vol 14, No 1 (2014) Vol 14, No 1 (2014) Vol 13, No 2 (2013) Vol 13, No 2 (2013) Vol 13, No 1 (2013) Vol 13, No 1 (2013) Vol 01-12 (Printed Issues) More Issue