cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Natalita Maulani Nursam
Contact Email
jurnal@brin.go.id
Phone
+6281221671367
Journal Mail Official
jet@brin.go.id
Editorial Address
National Research and Innovation Agency (BRIN), KST Samaun Samadikun Jl. Sangkuriang, Bandung, Indonesia, 40135
Location
Kota tangerang selatan,
Banten
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 14 Documents
 1   2 
Search results for , issue "vol. 24 no. 1 (2024)" : 14 Documents clear
Two-Stage Object Detection for Autonomous Vehicles With VGG-16 Based Faster R-CNN Arnetta Listiana Dewi; Hilman F. Pardede; Endang Suryawati; Hasih Pratiwi; Ana Heryana; Asri R Yuliani; Ade Ramdan
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.551

Abstract

The implementation of object detection for autonomous vehicles is essential as it is necessary to identify common object on the street so proper response could be designed. While single stage object may be smaller in computations, two-stage object detection is preferred due to the ability to localize the object. In this paper, we propose to use Faster R-CNN with VGG-16 backbone for detections of object on the street. We evaluate the method with open image subset by selecting objects that are common on street. We explore several hyper-parameters setup such as learning rate and the number of ROI regions to find the optimum set-up. We found that the use of learning rate 10-6 with Adam optimizer to be the optimum value for this task. We also found that increasing the number of ROI may benefit the performance. This shows that there is potential for getting a higher mAP with increase the amount of RoI.
Enhancing Urban Waste Management: An IoT-based Automated Trash Volume Monitoring System Ayu Latifah; Dede Kurniadi; Muhammad Sanusi
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.560

Abstract

Industrial development nowadays affects the increase in types of packaging waste which causes the accumulation of waste that has the potential to damage the environment. This research uses an Internet of Things for Automatic Waste Volume Monitoring System, so that waste management in an area can be improved. The purpose of this research is to make it easier for trashman to monitor the volume of the garbage collector on the notification feature. The research method used is the Rapid Application Development methodology with the Requirement Planning stage to analyze and identify the purpose of the system, then design the tools and system and create tools and system. Testing is used to evaluate the results of the tools and system. The result of the research is a prototype of an Internet of Things-based for Automatic Waste Volume Monitoring System tool equipped with a website-based monitoring system and for each user. Apart from that, the system which is equipped with a sensor that can detect the volume of the garbage collector is also equipped with an automatic opening and closing sensor to maintain the health of its users to provide answers to the problem of waste, especially in urban areas. Keywords: Internet of Things, Monitoring System, Waste, Waste Volume.
Nutrition, pH, Temperature, and Humidity Monitoring Hydroponics System based on Android Prihatin Oktivasari; Muda Wali Samudra Pasai; Mustofa Mustofa; Royhan Royhan; Asep Kurniawan
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.585

Abstract

Agriculture plays a very important role in the lives of Indonesian people. With technological advances and the increasing limitation of agricultural land, the patterns of matching crops in societies have changed. Innovations have been implemented, one of which is the use of technology such as hydroponic systems. One of the main factors that influence the success of hydroponic methods is temperature and humidity. The research aims to design and develop an autonomic control system that uses Fuzzy Logic to regulate the temperature and moisture of hydroponic plants, as well as to design the control of the nutrition and pH supply of hydrogen plants. Hydroponics plant control systems are implemented using microcontrollers and DHT22, TDS sensors, and pH sensors. In addition, an Android-based interface has been developed to monitor and control the system remotely via an internet connection. In this study the accuracy of the TDS sensor is 96.5%, the pH sensor was 98.19%, and the precision of the Fuzzy logic system at temperature and humidity is 100%
Screen-Printed Carbon Electrode Modified GNPs/ZnO For Electrochemical Biosensing Atik Dwi Oktaviani; Robeth Viktoria Manurung
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.593

Abstract

Screen-printed carbon electrodes (SPCEs) modified with graphene nanoplatelets (GNPs) and zinc oxide (ZnO) are widely used in electrochemical sensors due to their enhanced electrochemical properties and biocompatibility. Screen-printed carbon electrodes modified with Graphene nanoplatelets (GNPs) /Zinc oxide (ZnO) nanocomposite are described. Thus, in this study, GNPs/ZnO nanocomposite was synthesized, characterized, and applied to an electrochemical sensor. The formation of GNPs/ZnO nanocomposite was characterized by UV-Vis spectroscopy and scanning electron microscopy. Moreover, SPCE-GNPs/ZnO nanocomposite were characterized using cyclic voltammetry to optimize the concentration of nanocomposite. Then, the analytical performance of the sensor was studied by measuring methylparaben as an organic compound using differential pulse voltammetry (DPV) as a preliminary study before using it for biosensing. The result showed a significant improvement in electrocatalytic activity and reproducibility. The ratio of GNPs/ZnO nanocomposite with a concentration of 1 mg/mL produced the highest current response. Moreover, the detection of methylparaben showed high sensitivity with a limit of detection (LOD) around 9.7 μM, indicating high selectivity and good reproducibility of SPCE-GNPs/ZnO. Hence, the proposed sensor of SPCE-GNPs/ZnO displayed good performance, sensitivity, and reproducibility. 
Designing Human-Robot Communication in the Indonesian Language Using the Deep Bidirectional Long Short-Term Memory Algorithm Suci Dwijayanti; Ahmad Reinaldi Akbar; Bhakti Yudho Suprapto
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.595

Abstract

Humanoid robots closely resemble humans and engage in various human-like activities while responding to queries from their users, facilitating two-way communication between humans and robots. This bidirectional interaction is enabled through the integration of speech-to-text and text-to-speech systems within the robot. However, research on two-way communication systems for humanoid robots utilizing speech-to-text and text-to-speech technologies has predominantly focused on the English language. This study aims to develop a real-time two-way communication system between humans and a robot, with data collected from ten respondents, including eight males and two females. The sentences used adhere to the standard rules of the Indonesian language. The speech-to-text system employs a deep bidirectional long short-term memory algorithm, coupled with feature extraction via the Mel frequency cepstral coefficients, to convert spoken language into text. Conversely, the text-to-speech system utilizes the Python pyttsx3 module to translate text into spoken responses delivered by the robot. The results indicate that the speech-to-text model achieves a high level of accuracy under quiet-room conditions, with noise levels ranging from 57.5 to 60 dB, boasting an average word error rate (WER) of 24.99% and 25.31% for speakers within and outside the dataset, respectively. In settings with engine noise and crowds, where noise levels range from 62.4 to 86 dB, the measured WER is 36.36% and 36.96% for speakers within and outside the dataset, respectively. This study demonstrates the feasibility of implementing a two-way communication system between humans and a robot, enabling the robot to respond to various vocal inputs effectively. 
Performance Comparison of PID, FOPID, and NN-PID Controller for AUV Steering Problem Osen Fili Nami; Afif Widaryanto; Muhammad Putra Rasuanta; Tinova Pramudya; Muhammad Yusha Firdaus; Peni Laksmita Widati; Sakinah Puspa Anggraeni; Hanifah Dwiyanti; Maristya Rahmadiansyah; Michael Andreas Purwoadi; Sasono Rahardjo; Teddy Alhady Lubis
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.596

Abstract

This study examines and compares three Autonomous Underwater Vehicles (AUV) steering control techniques utilizing the following three control algorithms: Proportional-Integral-Derivative (PID), Fractional Order PID (FOIPD), and Neural Network-PID (NN-PID). The objective of this investigation is to gain a comprehensive understanding of each controller's response in terms of step input scenarios, trajectory changes, and when encountering disturbances. The response analysis will evaluate the strengths and weaknesses of the controller by examining parameters such as Rise Time, Settling Time, Settling Min, Settling Max, Overshoot, Peak, and Peak Time for each controller response. To determine the accuracy performance of each controller strategy, the root mean square error (RMSE) technique will be applied, allowing users to confidently select the most suitable controller option. FOPID displays the best settling time of 3.2218 seconds, and PID stands out in rise time, achieving 0.4725 seconds. The results indicate that NN-PID is the top performer as it reduces overshoot to 0.3022%. Among the three controllers that were tested, FOPID had the smallest RMSE value, while the NN-PID control's slower response and larger error resulted in a smaller overshoot than PID and FOPID. This factor is due to the online learning process on NN-PID, which requires time. Based on the simulation results, FOPID outperforms PID in settling time and produces the smallest error due to the inclusion of parameters λ and μ, leading to improved control performance.
The Effect of Window Size and Window Shape in STFT for Pre-Processing FMCW Radar Data in Human Activity Recognition Based on Bi-LSTM Figo Azzam De Fitrah; Fiky Y. Suratman; Istiqomah Istiqomah
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.601

Abstract

Many studies use radars for Human Activity Recognition (HAR), and numerous techniques for preprocessing FMCW radar data have been explored to improve HAR performances. Our approach employs 1-D radar to classify four human activities, i.e., walking, standing, crouching, and sitting.  We use Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT) with Kaiser window to generate range-time and Doppler-time data from inphase and quadrature radar signal. The choice of windowing parameters, i.e., window size and window shape represented by the beta parameter in Kaiser window, is considered to have significant impacts on the performances of deep learning LSTM models, including the F1-score. However, our study in this paper, including statistical analysis using t-tests, shows otherwise. Our results consistently support the null hypothesis, which mean that variations in window size and window shape do not significantly affect the F1-score. In essence, our findings underscore the robustness of our preprocessing methodology, emphasizing the stability and reliability of the selected configurations. This research provides valuable insights into the preprocessing techniques for radar data in the context of human activity recognition, enhancing the consistency and credibility of deep learning models in this domain.
Comparison of YOLOv3-tiny and YOLOv4-tiny in the Implementation Handgun, Shotgun, and Rifle Detection Using Raspberry Pi 4B Faris zulkarnain S. Hi. Rauf; Djati Handoko; Ilham S Pradana; Dimas Alifta
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.602

Abstract

Criminal activities frequently involve carryable weapons such as handguns, shotguns, and rifle classes. Frequently, the targets of these weapons that are captured are concealed from plain sight by the people of the crowd. The detection process for these weapons can be assisted by using deep learning. In this case, we intend to identify the model of the firearm that was detected. This research aims to apply one of the deep learning concepts, namely You Only Look Once (YOLO). The authors use versions of YOLOv3-tiny and Yolov4-tiny for the detection and classification of types of weapons, which are one of the fastest and most accurate methods of object detection, outperforming other detection algorithms. However, both require heavy computer architecture. Therefore, YOLOv3-tiny and YOLOv4-tiny, lighter versions of YOLOv3, can be solutions for smaller architectures. YOLOv3-tiny and YOLOv4-tiny have higher FPS, which is supposed to yield faster performance. Since YOLOv3-tiny and YOLOv4-tiny are modified versions of YOLOv3, the accuracy is improved, and YOLOv3 is already outperforming Faster Single Shot Detector (SSD) and Faster Region with Convolutional Neural Network (R-CNN). The authors employ YOLOv3-tiny and YOLOv4-tiny due to the fact that the Frame Per Second (FPS) and Mean Average Precision (mAP) performance of both approaches are superior in object detection. The study found that YOLOv3-tiny had a high FPS and low mAP performance: an average Intersection over Union (IoU)  score of 71.54%, an accuracy of 90%, a recall score of 78%, an F1 score of 84%, and an mAP of 86.7%. While YOLOv4-tiny has low FPS and high mAP: an average IoU score of 73.19%, an accuracy of 90%, a recall score of 84%, an F1 score of 87%, and an mAP of 90.7%.
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).
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

Page 1 of 2 | Total Record : 14

 1   2 

Filter by Year

2024 2024


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. 1 (2018) Vol. 17 No. 2 (2017) Vol. 17 No. 1 (2017) Vol. 16 No. 2 (2016) Vol. 16 No. 1 (2016) Vol. 15 No. 2 (2015) Vol. 15 No. 1 (2015) Vol. 14 No. 2 (2014) Vol. 14 No. 1 (2014) Vol. 13 No. 2 (2013) Vol. 13 No. 1 (2013) Vol 01-12 (Printed Issues) More Issue