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Jurnal Elektronika dan Telekomunikasi
Published by BRIN Publishing
ISSN : 14118289     EISSN : 25279955     DOI : https://doi.org/10.55981/jet.717
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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.
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Articles 309 Documents
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Front Cover Vol. 23 No. 2 Salita Ulitia Prini
Jurnal Elektronika dan Telekomunikasi Vol. 23 No. 2 (2023)
Publisher : National Research and Innovation Agency

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Abstract

Back Cover Vol. 23 No. 2 Salita Ulitia Prini
Jurnal Elektronika dan Telekomunikasi Vol. 23 No. 2 (2023)
Publisher : National Research and Innovation Agency

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Abstract

Computational Analysis of Electrical Impedance Spectroscopy for Margin Tissue Detection in Laparoscopic Liver Resection Sulistia Sulistia; Riyanto Riyanto; Pratondo Busono; Affandi Faisal Kurniawan; Joko Saefan; Wawan Kurniawan; Marlin Ramadhan Baidillah
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.630

Abstract

Margin tissue detection during intraoperative laparoscopic liver resection (LLR) is required to prevent tumor recurrence and reduce the likelihood of further surgery. This study proposes an electrical impedance spectroscopy (EIS) method for margin tissue detection in LLR to determine the boundary interface of normal and cancerous tissue. The proposed method of this study has three objectives: (1) designing the electrode array configuration to collect multiple EIS impedance measurements, (2) implementing the Feedforward Neural Network (FNN) to classify the orientation of margin tissue relative to the electrode array by using time-difference impedance indexes, and (4) governing the inflection point method based on impedance indexes to detect the margin tissue location. The proposed method is evaluated by a 3D numerical simulation of liver tissue composed of cancerous lumps with Iac = 1 mA alternating injection current  at frequencies: lf = 1 kHz and hf = 100 kHz. The electrode array is composed of 16 electrode pairs each for injection current and voltage measurements. The variation of margin tissue orientation relative to the electrode array direction was considered to occur in unidirectional, perpendicular, and diagonal direction with noise variations (Signal-to-Noise-Ratio: 50 to 90 dB). The FNN trained on 2,400 data points achieves True Positive Rate (TPR) value as 90.2%, 99.4%, and 96.6% for diagonal, perpendicular, and unidirectional respectively in margin tissue orientation classification, while the inflection point method detects margin tissue location with 75% location at the unidirectional orientation (y-axis).
Power Regulator Design Using LM317 for Precise and Efficient Power Management Budihardja Murtianta; Atyanta Nika Rumaksari
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.632

Abstract

The Indonesian government plans to transition its transportation sector to electric vehicles (EVs) by 2025. Achieving this ambitious target will necessitate advancements in power management technologies. Therefore, the government is boosting research on energy efficiency, cutting power dissipation, and enhancing the reliability and lifespan of EV components. This study focuses on designing a power-efficient linear voltage regulator using the LM317, which is essential for EV power management. The regulator employs a voltage comparator to monitor feedback voltage and select the correct input voltage, ensuring efficient and stable output power. We tested the LM317 against the LM338 and LM350 in various setups. The results showed that the LM317 performed better in terms of voltage precision, efficiency, power dissipation, and temperature stability. Moreover, the LM317 achieved 75% efficiency in single-source setups and 85% in multi-source configurations, with a voltage precision of ±0.1%. The system’s ability to dynamically select input sources ensures optimal performance for small-signal EV applications.
Compact Dual Port UWB MIMO Antenna with WLAN Band Rejection Firdaus Firdaus; Intan Aprillia Ikhsan; Rahmadi Kurnia; Ikhwana Elfitri
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.635

Abstract

This research presents the design of a compact dual-port UWB MIMO antenna. The primary challenge in designing UWB MIMO antennas lies in achieving a low-frequency band of 3.1 GHz while maintaining a small size. By modifying the patch shape to a tapered configuration and incorporating an inset feed and a slit for WLAN notch band, a rectangular monopole patch antenna successfully overcomes these limitations. The MIMO configuration of this antenna achieves a wide UWB bandwidth of 3.1-12 GHz with a compact dimension of 20×28.5×1.6 mm. The antenna exhibits excellent characteristics, including low mutual coupling (-15 dB), maximum gain of 3 dBi, low ECC (<0.01), high diversity gain (<9.95), low TARC (< -20 dB), and nearly omnidirectional radiation pattern. These results demonstrate the suitability of the proposed antenna design for UWB applications.
The Implementation of Mamdani Fuzzy Logic Control on a Hexapod Robot as a Guide for Visually Impaired People Mohamad Agung Prawira Negara; Fikri Mulyadi; Ali Rizal Chaidir; Khairul Anam
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.638

Abstract

The constraints faced by visually impaired individuals have spurred various human-created innovations to aid them. One such innovation is employing robots as guides for the blind. Numerous studies have delved into utilizing robots as guides for visually impaired individuals. Nevertheless, these robots still encounter limitations, particularly in navigating rough and uneven terrain. To tackle this issue, there's a necessity for a hexapod robot capable of traversing uneven surfaces more effectively than wheeled robots. The hexapod robot developed in this research is an autonomous robot that employs fuzzy logic as its control method. The resultant hexapod robot has showcased outstanding performance, attaining a 100% success rate in navigating the specified path and demonstrating a reliability of 79.78%.
Deep Neural Network Classifier for Analysis of the Debrecen Diabetic Retinopathy Dataset Cucu Ika Agustyaningrum; Haryani Haryani; Agus Junaidi; Iwan Fadilah
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.640

Abstract

Diabetic retinopathy (DR) is a serious complication that can occur in individuals who have diabetes. This disease affects the blood vessels in the retina, a part of the eye that is important for vision. Early detection of DR is key to preventing further complications and saving the patient’s vision. The goal of Diabetic Retinopathy Debrecen Data Set Analysis is to get the best, most accurate results for medical professionals to receive appropriate Diabetic Retinopathy Debrecen prediction results through the stages of data collection, evaluation, and classification.   Data is collected from existing secondary sources, then assessed using a deep neural network algorithm with various variations. The classification algorithm in this research uses the Python programming language to measure accuracy, F1-Score, precision, recall, and ROC AUC. The test results show that the accuracy of the deep neural network algorithm is 79.94%, the F1 score reaches 79.16%, the precision is 79.58%, the recall is 79.60%, and the AUC is 79.56%. Thus, based on this research, the deep neural network data mining technique with variations of the four hidden layer encoder-decoder, sigmoid activation function, Adam optimizer, learning rate 0.001, and dropout 0.2 is proven to be effective. When compared with other variations   such as decoder-encoder, 3-8 hidden layers, learning rate 0.1 and 0.01, the average difference in values between this variation and the others is 0.07% accuracy, 2.03% F1 score, 0.25% precision, 0.80% recall, and 0.90% AUC. Therefore, the deep neural network algorithm with the variation used shows significant dominance compared to other variations.Diabetic retinopathy (DR) is a serious complication that can occur in individuals who have diabetes. This disease affects the blood vessels in the retina, a part of the eye that is important for vision. Early detection of DR is key to preventing further complications and saving the patient’s vision. The goal of Diabetic Retinopathy Debrecen Data Set Analysis is to get the best, most accurate results for medical professionals to receive appropriate Diabetic Retinopathy Debrecen prediction results through the stages of data collection, evaluation, and classification.   Data is collected from existing secondary sources, then assessed using a deep neural network algorithm with various variations. The classification algorithm in this research uses the Python programming language to measure accuracy, F1-Score, precision, recall, and ROC AUC. The test results show that the accuracy of the deep neural network algorithm is 79.94%, the F1 score reaches 79.16%, the precision is 79.58%, the recall is 79.60%, and the AUC is 79.56%. Thus, based on this research, the deep neural network data mining technique with variations of the four hidden layer encoder-decoder, sigmoid activation function, Adam optimizer, learning rate 0.001, and dropout 0.2 is proven to be effective. When compared with other variations   such as decoder-encoder, 3-8 hidden layers, learning rate 0.1 and 0.01, the average difference in values between this variation and the others is 0.07% accuracy, 2.03% F1 score, 0.25% precision, 0.80% recall, and 0.90% AUC. Therefore, the deep neural network algorithm with the variation used shows significant dominance compared to other variations.
Ground Penetrating Radar Data Inversion Using Dual-Input Convolutional Autoencoder for Ferroconcrete Inspection Budiman Putra Asmaur Rohman; Masahiko Nishimoto; Ratna Indrawijaya; Dayat Kurniawan; Iman Firmansyah; Bagus Edy Sukoco
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.642

Abstract

Ground penetrating radar (GPR) is a non-destructive tool for exploring an object buried underground. Currently, GPR is also considered for reinforced concrete inspection. However, the image produced by GPR can not be easily interpreted. Besides, the large observation of building concrete inspection also motivates the researchers to fastening and easing radar image interpretation. Thus,  this research proposes a new method to translate GPR scattering data image to its internal structure visualization. The proposed employs a convolutional autoencoder model using amplitude and phase radar data as input of the algorithm. As evaluation, in this stage, we perform numerical analysis by using finite-difference time-domain-based synthetic data that considers three cases: concrete with rebar, concrete with crack, and concrete with rebar and crack. All of those cases are simulated with randomized dimensions and positions that is possible in the real applications. Compared with the baseline method, our method shows superiority, especially in the semantic segmentation perspective. The parameter size of the proposed model is also much smaller, around one-third of the previous method. Therefore, the method is feasible enough to be implemented in real applications addressing an automatic internal structure reinforced concrete visulaization
Object Detection Approach Using YOLOv5 For Plant Species Identification Billi Clinton; Amperawan Amperawan; Tresna Dewi
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.643

Abstract

In the modern era of agriculture and horticulture, biodiversity conservation requires plant species identification skills, and automatic detection is a challenging and interesting task. However, many factors often make some people mistaken in recognizing plant species that have unique and varied visual characteristics, making manual identification difficult. This problem requires an effective and accurate model for identifying plant species. So this research aims to produce a model to identify plant species that are effective and have a high level of accuracy. This research offers the use of the YOLOv5 algorithm method. The training process with epoch 200 and 53 minutes with a total of 1,220 images. Based on the results of the model performance test, the mAP value was 85.73%, precision 98.27%, and recall 94.36%. During testing, the model can identify plant species accurately on single objects and multiple objects. The results of this research show that the proposed method is successful in identifying plant species accurately.
Enhancing Solar Panels Efficiency: The Impact of Robotic Cleaning and Optimal Trajectory Tracking in the Presence of Disturbances Using Model Reference Adaptive Control Yves Abessolo Mindzie; Joseph Kenfack; Brice Ekobo Akoa; Noé Paulin Frederic Ntouba; Blaise Njoya Fouedjou; Guy M. Toche Tchio; Joseph Voufo; Urbain Nzotcha
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.645

Abstract

The output power of photovoltaic systems (PV) can be significantly reduced by dust accumulation. Among various cleaning methods, robotic cleaning is currently the most popular choice because it minimizes human effort and reduces the risk of damaging PV cells. However cleaning robots can be impacted by various external disturbances, including wind, rain, lightning, snow,  , and vibrations. Additionally, sensor errors related to slip, position, velocity, acceleration, and varying electrical parameters can also affect their performance.Several methods have been proposed in the literature for tracking the robotic cleaning trajectory of PV systems. Nevertheless, most of these methods struggle in the presence of disturbances and often have prolonged convergence times. This paper aims to propose a Model Reference Adaptive Control system to maintain optimal performance and extend the lifespan of PV panels, minimize power losses, reduce convergence time, achieve optimal tracking of the desired cleaning trajectory amidst disturbances, and decrease the dependence on multiple sensors. In our study, we utililized the iRobot solar panel developed by Aravind et al., which has a power capacity of 250 W and weighs 250 kg. This iRobot can effectively clean approximately 930 solar panels of the Kyocera Solar KC 130 GT module, which measures 1.425 m in length and 0.652 m in width. The iRobot operates for 4 hours, covering an area of 864 m², and can clean a surface area of 0.06 m² in one second. We conducted simulations using the proposed MRAC algorithm in Matlab/Simulink software, comparing the results with those obtained from a Proportional Integral Derivative (PID) algorithm. The results demonstrate that the MRAC approach achieves a shorter convergence time and greater precision in following the desired cleaning trajectory of the robot, even in the presence of disturbances, compared to the PID algorithm.

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