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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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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%
Comparison of a Circular Patch Unit Cell Performance for Reflector Applications between Using FR4 and F4BMX220 Substrates at 3.5 GHz Frequency Taufiqqurrachman -; Muhammad Kamal Abdul Rahim; Dadin Mahmudin; R. Priyo Hartono Adji; Deni Permana Kurniadi; Winy Desvasari; Sulistyaningsih -; Fajri Darwis; Arief Nur Rahman; Prasetyo Putranto; Arie Setiawan; Aminuddin Rizal
Jurnal Elektronika dan Telekomunikasi Vol. 23 No. 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.587

Abstract

This paper presents a performance comparison of the circular patch unit cell as a unit cell for reflector application at 3.5 GHz frequency using a dielectric substrate between FR4 and F4BMX220 substrates. A circular patch is chosen as the unit cell of a reflector because it is commonly used, fabricated, and has a wider bandwidth compared to other structures. A performance comparison of the circular patch on both dielectric substrates is presented in a graph of S-parameters, reflection phase, and operating bandwidth, as well as in the table of dimensions, where the result is performed by simulation using CST software. Based on the simulated results, the F4BMX220 has a better performance compared to the FR4 in terms of the reflection value, operating bandwidth, and dielectric substrate thickness. However, a circular patch diameter when using the F4BMX220 is bigger than when using the FR4 substrate because the FR4 substrate has a higher dielectric constant than the F4BMX220, which is twice the F4BMX220 dielectric constant. Also, the F4BMX220 substrate has a narrower bandwidth compared to the FR4 substrate, which is a difference of around 0.1 GHz. The circular patch when using the F4BMX220 substrate has 0.96 of a reflection value, 0.007 of an absorption value, -6.77° of the reflection phase, and 0.24 GHz of the operating bandwidth at the normal incident wave angle (0°). Also, it can be properly worked if the incident wave angle is moving until 60°. The F4BMX220 substrate has the best performance compared to the FR4 substrate because the reflection value is much better value, even at the incident wave angle of 60°.
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.
Colloidal TiO2-Modified Mesoporous Electron Transport Layer in Perovskite Solar Cells Evira Bella Yustiani; Putri Nur Anggraini; Shobih Shobih; Eri Widianto; Lilis Retnaningsih; Syoni Soepriyanto; Imam Santoso; Natalita Maulani Nursam
Jurnal Elektronika dan Telekomunikasi Vol. 23 No. 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.599

Abstract

The electron transport layer (ETL) is a crucial part in perovskite solar cells (PSC) as it specifically governs the charge extraction at the perovskite/ETL interface. In this study, methylammonium lead iodide-based PSCs with an n-i-p structure were fabricated and modified by adding colloidal TiO2 into the mesoporous TiO2 film as ETL. The effect of the colloidal TiO2 addition on the PSC performance was investigated for ETL comprising different types of TiO2 particles, i.e. P25 and anatase TiO2. Despite producing lower performance than the PSC made with commercial paste, the power conversion efficiency of the PSCs could be improved with the introduction of colloidal TiO2 solution. An optimum condition was observed depending on the type of TiO2 particle, where the best performing device was achieved with colloidal TiO2 of 0.4 and 0.2 mL for P25 and anatase TiO2, respectively. The amount of colloidal TiO2 in samples with P25 overall had less impact than the samples with anatase TiO2.
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%.
Preface 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

Appendix 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

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