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INDONESIA
Jurnal Elektronika dan Telekomunikasi
Published by BRIN Publishing
ISSN : 14118289     EISSN : 25279955     DOI : https://doi.org/10.55981/jet.717
Core Subject :
Jurnal Elektronika dan Telekomunikasi (JET) aims to publish high-quality articles with a specific focus on the latest research and developments in the field of electronics, telecommunications, and microelectronics engineering. It will provide a platform for academicians, researchers and engineers to share their experience and solution to problems in different areas of electronics and telecommunication engineering.
Arjuna Subject : -
Articles 309 Documents
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Back Cover Vol. 18 No. 2 Chaeriah Bin Ali Wael
Jurnal Elektronika dan Telekomunikasi Vol. 18 No. 2 (2018)
Publisher : National Research and Innovation Agency

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Preface Vol. 18 No. 2 Tajul Miftahushudur
Jurnal Elektronika dan Telekomunikasi Vol. 18 No. 2 (2018)
Publisher : National Research and Innovation Agency

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Appendix Vol. 18 No. 2 Tajul Miftahushudur
Jurnal Elektronika dan Telekomunikasi Vol. 18 No. 2 (2018)
Publisher : National Research and Innovation Agency

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CDF-based Flow Detection for Network Flow Sampling and Packet Capturing Aris Cahyadi Risdianto; Nuryani -
Jurnal Elektronika dan Telekomunikasi Vol. 19 No. 1 (2019)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v19.26-31

Abstract

Providing an appropriate level of flow collection, relying on packet capturing or flow sampling method, is extremely hard due to various practical limitations and resources requirements. To address this challenge, this paper investigated a CDF (Cumulative Distribution Function)-based flow detection to decide between “known” and “unknown” flows. Therefore, a combined flow collection can be achieved to improve the collection’s efficiency by sampling only the known flows and capturing the remaining unknown flows. As a preliminary experiment, detecting known and unknown flows was conducted over a long period by calculating the empirical CDF distance between each flow’s rate and overall packet’s rate distribution, called as FPR (Flow-to-Packet Ratio), with a threshold (FPRmin) based on a significant level of observed data. The result shows that unknown flow is detected for most of the recommended significant level values.
Performance Evaluation of A2-A4-RSRQ and A3-RSRP Handover Algorithms in LTE Network Hendrawan Hendrawan; Ayu Rosyida Zain; Sri Lestari
Jurnal Elektronika dan Telekomunikasi Vol. 19 No. 2 (2019)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v19.64-74

Abstract

In LTE Network, users can move freely in the network through fast and seamless handover (HO). This research focuses on intra-LTE handover which occurs using interface X2 to move an EU between two eNBs, i.e. source eNB and target eNB without any changes in MME and SGW at EPC level. Two popular algorithms of intra-LTE handover namely A2-A4-RSRQ and A3-RSRP were evaluated and compared through simulations as well as direct measurements in the field. Simulation is conducted using NS3 simulation tool where performances of various scenarios from both algorithms were evaluated. The performance metrics studied include the average number of HOs that occur, throughput and optimized ratio. Simulations carried out for various scenarios in term of EU numbers, user speeds, and channel conditions. In addition, the results of one-month measurement of three eNBs were also presented. The measurement results are then compared and used to verify the simulation results. Furthermore, by using the optimizing ratio metric, the optimal pair of parameter values of Threshold as well as Offset and Handover Margin (HOM) along with Time-to-Trigger (TTT) are sought for the A2-A4-RSRQ and A3-RSRP respectively.
Active Filter Analysis on Designing Electronic Stethoscope Prihatin Oktivasari; Riandini Riandini; Rahmah A. Fitri; Sungguh I. Malaon
Jurnal Elektronika dan Telekomunikasi Vol. 19 No. 2 (2019)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v19.51-56

Abstract

Early heart disease detection could be vital and some other diagnostic ways are being developed. In this paper, a lowcost tool for a diagnostic that analyzes the digitized heartbeat sound is given. This can be used to detect heart anomalies. The instrument shows the heart sound and also keeps a patient's long-term record for future use. The signal from the heart provides a lot of knowledge about the heart and offers an initial diagnosis recommendation. The electronic stethoscope uses the condenser microphone, preamplifier circuit, and filter circuit. The optimum filter is Butterworth with a fourth-order Sallen key low pass filter topology with a gain of 0.707 volts, -3.01 dB, and a fourth-order high pass filter with a gain of 0.782 volts, -2.137 dB. The frequency of the heart sound is about 20 Hz – 120 Hz in general. Therefore, the lower cutoff frequency of the filter is set to 20 Hz, while the higher cutoff frequency set to 120 Hz. The evaluation used to measure the performance of an electronic stethoscope is to compare with a conventional stethoscope, the recorded sound is the same.
Deep CNNBased Detection for Tea Clone Identification Ade Ramdan; Endang Suryawati; R. Budiarianto Suryo Kusumo; Hilman F. Pardede; Oka Mahendra; Rico Dahlan; Fani Fauziah; Heri Syahrian
Jurnal Elektronika dan Telekomunikasi Vol. 19 No. 2 (2019)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v19.45-50

Abstract

One factor affecting the quality of tea is the selection of plant material that would be planted on the field. Clonal selection is a common way to produce tea with better quality. However, as a natural cross pollination species, tea often consists of various clones or progenies of cross-pollinated process. This commonly occurs on plantations owned by smallholder farmers. To produce a consistent quality tea, the clones or progenies need to be identified. Usually, human experts distinguish the plants from leaves by visual inspection on the physical attributes of the leaves, such as the textures, the bone structures, and the colors. It is very difficult for non-experts or common farmers to do such identifications. In this, we propose a deep learning-based identification of tea clones. We apply deep convolutional neural network (CNN) to identify 3 types of tea clones of Gambung series, a series of tea clones developed at Research Institute of Tea and Cinchona. Our study indicates that the performance of the CNN systems are affected by the depth of the convolutional layers. VGGNet, a popular CNN architectures with 16 layers, achieves better accuracy compared to AlexNet, a CNN with 6 layers.
Front Cover Vol. 19 No. 1 Natalita Maulani Nursam
Jurnal Elektronika dan Telekomunikasi Vol. 19 No. 1 (2019)
Publisher : National Research and Innovation Agency

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Back Cover Vol. 19 No. 1 Natalita Maulani Nursam
Jurnal Elektronika dan Telekomunikasi Vol. 19 No. 1 (2019)
Publisher : National Research and Innovation Agency

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Appendix Vol. 19 No. 1 Natalita Maulani Nursam
Jurnal Elektronika dan Telekomunikasi Vol. 19 No. 1 (2019)
Publisher : National Research and Innovation Agency

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Page 13 of 31 | Total Record : 309

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