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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 470 Documents
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Development of the Android Application for the Landslide Disaster Mitigation Real-time System based on Firebase Server and OneSignal Nawawi, Aif Umar; Pantjawati, Arjuni Budi; Saripudin, Aip; Hakim, Nurul Fahmi Arief; Nurhidayatulloh, Nurhidayatulloh; Karostiani, Novia; Anandayutya, Bagaskara; Darmawan, Alvin Dzaki Pratama
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.674

Abstract

This article presents the development of SIDASIBELO (Landslide Information and Mitigation System), an Android-based application for real-time landslide monitoring and early warning. This system integrates data from several sensors that measure soil moisture, rainfall, slope angle, and ground vibrations at the monitored locations. The Firebase Real-time Database stores and transmits sensor data, while Firebase Cloud Messaging and OneSignal enable push notifications to alert users about potential landslide risks. There are four output status levels: SAFE, ALERT, CAUTION, and WARNING, which are determined based on a comprehensive analysis of the monitored parameters. Mitigation strategies include real-time parameter monitoring, early warnings, evacuation guidance, and user education on landslide preparedness. Testing on several Android devices demonstrated high compatibility, with an average successful server connection rate of 92.86%. Latency tests indicated an average response time of 894 ms from the input device to the database, 1.29 ms from the database to the Android device, and 2725 ms for push notifications. The system's total daily bandwidth usage for real-time data transmission is 27,648 MB, indicating high efficiency without overburdening the server's performance. While the app operates efficiently under ideal conditions, unstable network connections can lead to data retrieval failures or, in worst cases, app malfunctions. This remains a key challenge for our system. This system aims to raise awareness of landslide risks and enable timely evacuation, which could potentially reduce the negative impacts of landslides in vulnerable areas.
Appendix Vol. 24 No. 2 Prini, Salita Ulitia
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.720

Abstract

Deep Neural Network Classifier for Analysis of the Debrecen Diabetic Retinopathy Dataset Agustyaningrum, Cucu Ika; Haryani, Haryani; Junaidi, Agus; Fadilah, Iwan
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.
The Implementation of Mamdani Fuzzy Logic Control on a Hexapod Robot as a Guide for Visually Impaired People Prawira Negara, Mohamad Agung; Mulyadi, Fikri; Chaidir, Ali Rizal; Anam, Khairul
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%.
Front Cover Vol. 24 No. 2 Prini, Salita Ulitia
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.721

Abstract

Object Detection Approach Using YOLOv5 For Plant Species Identification Clinton, Billi; Amperawan, Amperawan; Dewi, Tresna
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.
On the Impact of the Number of Tiles and Partitioning of RISs on the Maximum Achievable Intensity Danufane, Fadil Habibi; Fathnan, Ashif Aminulloh; Adji, Raden Priyo Hartono; Setiawan, Arie; Putranto, Prasetyo
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.687

Abstract

Reconfigurable intelligent surface (RIS) is a key technology to enable the concept of smart radio environment (SRE) which is envisioned to meet the ever-increasing demands of connectivity in the upcoming decade. However, most existing works consider a RIS that is seen as a whole surface. In this paper, we study the performance of a RIS that consists of multiple tiles, each of which is capable of performing certain wave manipulation, by deriving the physics-compliant analytical formulation of the received signal at the receiver. We also introduce the numerical approximation of the signal for different operating regimes and different functionalities. Based on the obtained result, we study the impact of the number of fixed-sized tiles and partitioning of a fixed-sized RIS on the maximum achievable intensity. The validity of our findings is confirmed through extensive simulation results.
Back Cover Vol. 24 No. 2 Prini, Salita Ulitia
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.722

Abstract

Design of Two Phase DC-AC Interleaved Boost Inverter with Voltage Control System using PI Controller Wasiatno, Juan Marco Alexander; Pratomo, Leonardus Heru
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.652

Abstract

DC-DC Interleaved Boost Converter (DC-DC IBC) topology was developed through the interleaving technique since conventional DC-DC Boost Converter has many problems related to complex circuit control, harmonics, and output power. In this research, DC-DC IBC was developed into a Two-Phase AC-AC Interleaved Boost Converter (TP AC-AC IBC), then combined with a Two-Phase Full Bridge Inverter to become a Two-Phase DC-AC Interleaved Boost Inverter (TP DC-AC IBI). TP DC-AC IBI has several advantages, including minimal current and voltage ripples and greater output power because it consists of two AC-AC IBCs. This research aims to meet highly regulated AC voltage needs with the renewable energy source input using the proposed topology, by implementing Proportional Integral (PI) close loop control system. The output voltage is detected using a voltage transducer LV-25P, then compared with a reference voltage and controlled using a PI controller to keep the output voltage consistently stable. The switching signal setting uses the Sinusoidal Pulse Width Modulation (SPWM) technique by modulating the control output with a high frequency. As a verification step, testing was carried out using Power Simulator (PSIM) software and then validated by hardware testing in the laboratory. Testing was carried out using several test signals, and it was found that the proposed method worked well. System efficiency and Total Harmonic Distortion (THD) tests carried out using various load values, and a maximum efficiency of 93.87% and a minimum THD of 2.46% were obtained.
Enhancing Solar Panels Efficiency: The Impact of Robotic Cleaning and Optimal Trajectory Tracking in the Presence of Disturbances Using Model Reference Adaptive Control Abessolo Mindzie, Yves; Kenfack, Joseph; Ekobo Akoa, Brice; Paulin Frederic Ntouba, Noé; Njoya Fouedjou, Blaise; M. Toche Tchio, Guy; Voufo, Joseph; Nzotcha, Urbain
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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