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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.
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Articles 15 Documents
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Search results for , issue "Vol 25, No 2 (2025)" : 15 Documents clear
Dual-Function Aperture-Coupled Spiral Resonator Antenna with Integrated Impedance Matching Network for Enhanced Radiation Performance Yunus, Mochamad; Yamato, Yamato; Rijadi, Bloko Budi; Waryani, Waryani; Maulana, Muhammad Farhan; Firmansyah, Teguh; Munir, Achmad
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.804

Abstract

This paper proposes the design and implementation of a dual-function aperture-coupled spiral resonator (SR) antenna integrated with a compact impedance matching network (IMN) to achieve enhanced radiation performance and miniaturization. The antenna uses a two-layer FR4 substrate, where the SR is printed on the top layer as the radiating element and excited through a slotted aperture on the ground plane. To maximize power transfer, the IMN, consisting of an inter-digital capacitor (IDC) and a meandered inductor (MI), is embedded into the feed line on the bottom substrate. A comparative study between the conventional SR antenna and the proposed dual-function SR with IMN was conducted. Electromagnetic simulations and experimental measurements demonstrate that the integrated IMN improves the reflection coefficient (S11) by 43.64%, increases radiation efficiency from ~72% to ~87%, and enhances gain from ~3.2 dBi to ~4.8 dBi, while maintaining a compact footprint. The aperture-coupled feeding also contributes to bandwidth enhancement and isolation between the feed and radiating element. This dual-function design effectively resolves the trade-off between miniaturization and radiation performance, demonstrating its applicability for IoT, 5G, and wearable wireless devices.
2.4 GHz Energy Harvester for Ultra-Low Power IoT Sensor Applications Vauzia, Farrah; Sulaeman, Enceng; Taryana, Yana
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.679

Abstract

IoT is a technology that integrates various devices and can be controlled remotely via the internet. Currently, IoT is rapidly developing in sectors such as health, agriculture, housing, and more. Sensors play an essential role in IoT devices to collect information from the surrounding environment. The sensors rely on batteries as a power source, which affects their performance. Recent technologies have developed ultra-low power sensors to extend the battery life. However, using batteries for IoT devices over a long period is not cost-effective and efficient in terms of installation. To address this issue, an Energy Harvester system has been developed. This system collects energy from the surrounding environment and converts it into electrical energy. The focus of this research is to design and implement an energy harvester powered by Radio Frequency (RF), specifically in the 2.4 GHz frequency band for ultra-low power IoT sensor applications. The RF energy harvester (RFEH) was designed and simulated using ADS 2011.11 software. The RFEH was fabricated on FR4 epoxy PCB and the measurement was conducted in two conditions: directly connected to the signal generator and in a far-field area. The harvester achieved a maximum output current of 32.6µA under a received power of -3 dBm, satisfying the requirements for ultra-low power IoT sensors.
Appendix Vol. 25 No. 2 Prini, Salita Ulitia
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.835

Abstract

Comparative Performance of Regression and Ensemble Learning Algorithms in Precision Irrigation Forecasting of Sweet Potato Rahmah, Muthia; Maulana, Indra
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.799

Abstract

Precision irrigation is essential for sustainable agriculture under increasing water scarcity. This study compared regression and ensemble learning algorithms for forecasting irrigation requirements in sweet potato, a crop characterized by high variability in water demand. An Internet of Things (IoT)-based prototype was deployed to collect real-time data on soil moisture, temperature, humidity, light intensity, and atmospheric pressure over 42 hours and 50 minutes (August 4-5, 2025), encompassing two complete diurnal cycles at 10-minute intervals and yielding 243 temporal observations. Following preprocessing and feature engineering with lag-based temporal features, the final dataset comprised 240 samples (192 training, 48 testing) using chronological time-based splitting to prevent data leakage. Five algorithms, Support Vector Regression (SVR), AdaBoost, Extreme Gradient Boosting (XGBoost), Random Forest Regressor (RFR), and CatBoost, were evaluated under default and hyperparameter-tuned configurations using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) as evaluation metrics. Tuned Random Forest achieved superior performance (R² = 0.9802, RMSE = 9.58, MAE = 6.08), followed by default Random Forest (R² = 0.9786) and default CatBoost (R² = 0.9687). XGBoost demonstrated strong performance (R² = 0.9670 tuned) but exhibited overfitting tendencies with near-perfect training scores. SVR improved substantially after tuning (R² = 0.328 to 0.797), although it remained inferior to ensemble methods. Overall, ensemble methods, particularly XGBoost and Random Forest, demonstrated superior efficacy for sweet potato irrigation forecasting. These findings underscore the potential of IoT-integrated machine learning to enhance water-use efficiency and support sustainable smart farming practices.
Design, Fabrication, and Experimental Evaluation of a 435 MHz Helical Antenna for 433 MHz IoT Modules Rusfa, Rusfa; Pramana, Rozeff; Bavitra, Bavitra; Oktavia, Ferly; Alajuri, M. Hasbi Sidqi; Simannulang, Andreas M
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.813

Abstract

The rapid growth of wireless communication demands within Internet of Things (IoT) applications requires antennas that exhibit high efficiency, compact dimensions, and reliable performance in the UHF band. This study aims to design, simulate, fabricate, and evaluate the performance of a helical antenna operating at 435 MHz, with its results compared against a slot antenna. The design process was conducted using CST Studio Suite with parameter optimization to achieve an optimal configuration. The prototype was fabricated using copper wire as the radiating element and an aluminum ground plane. Experimental testing was carried out with a UHF Antenna Demonstrator, followed by validation through a 433 MHz RF module integrated with Arduino. The simulation results indicated that the optimized helical antenna achieved aVoltage Standing Wave Ratio (VSWR) of 1.8 and a gain of 11.5 dBi. In contrast, the measurement results demonstrated improved performance, with a VSWR of 1.05, a return loss of −32.4 dB, and a bandwidth of 41 MHz. Comparative analysis revealed that the helical antenna outperformed the slot antenna in terms of efficiency, directional radiation pattern, and transmission distance, reaching up to 25 m compared to 15 m for the slot antenna. These findings confirm that the helical antenna is a more suitable and effective solution for UHF IoT communication systems, providing reliable performance for modern wireless applications.
Implementation of Internet of Things-Based Autofeeder to Maintain Koi Pond Water Quality Helmy, Helmy; Pernanda, Suko Tyas; Nursaputro, Septiantar Tebe; Pratiwi, Mona Inayah; Rahmaditya, Brian; Anggreini, Clara Silvia
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.717

Abstract

Koi fish farming requires careful monitoring of water temperature and pH to prevent adverse impacts on the fish. This study presents a prototype IoT-based autofeeder that integrates real-time water quality monitoring and automatic feeding, controllable via both an Android application and local device buttons. The system allows users to configure feeding schedules, feed throw levels, and durations, as well as set pH thresholds. When the pH exceeds the safe range, the system automatically stops feeding and sends notifications, enabling the user to inspect and maintain pond water quality. The findings demonstrate that the dispensing level significantly influences the feed-throwing distance; higher dispensing levels result in longer distances. Small-sized feed (S) consistently produced the highest output, followed by medium-sized (M) and large-sized (L). Increasing the feeding duration enhanced the weight of the released feed. Additionally, the average delay in sensor data transmission to the database was recorded at 5.48 seconds. The data loss rate during the testing period was 1.72%, which is considered acceptable and does not adversely affect system operations. The data transmission system demonstrated good and stable performance with relatively low data loss.
Phase-Sensitive Radar Using ADALM-Pluto SDR and Cantenna for Sub-Millimeter Displacement Measurement Mozef, Eril; Rasyid, Ridho Shofwan; Sulaeman, Enceng; Mulyana, Tiyo Rizky; Al Farik, Fahrizal; Junjunan, Thaskia Qolbi
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.757

Abstract

The capability of phase-sensitive radar to detect sub-millimeter displacement has been widely demonstrated, enabling a range of applications such as structural vibration monitoring, human vital-sign detection, gesture sensing, and precision motion tracking. In these domains, particularly in non-contact human respiratory monitoring, conventional phase-sensitive radar systems offer key advantages, including high phase stability, robust performance under non-ideal lighting or environmental conditions, and the ability to operate without physical contact. These strengths make them effective for capturing small periodic chest movements required for accurate respiratory assessment. However, conventional hardware implementations often suffer from limited flexibility, higher development cost, and increased design complexity. These constraints motivate the shift toward software-defined radio (SDR) solutions, which provide reconfigurability, simplified prototyping, and significantly lower cost while retaining the essential phase-sensitive capabilities. This motivation forms the basis of the present research. This study realizes a phase-sensitive radar using an ADALM-Pluto SDR operating at 2.45 GHz with a cantenna antenna configuration. Compared with previous SDR-based works that focus primarily on Doppler vital-sign extraction or require more elaborate RF front-ends, the proposed system emphasizes displacement-resolution enhancement through careful phase processing while maintaining minimal hardware complexity. The combination of a compact SDR platform, simple antenna structure, and optimized signal processing pipeline yields a practical and accessible radar prototype. Experimental results demonstrate that the proposed system achieves a displacement resolution of 0.5 mm, meeting the requirements for developing a reliable respiratory-monitoring application and confirming the suitability of SDR-based phase-sensitive radar for low-cost biomedical sensing.
Front Cover Vol. 25 No. 2 Prini, Salita Ulitia
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.833

Abstract

Feature Selection and Class Imbalance Machine Learning for Early Detection of Thyroid Cancer Recurrence: A Performance-Based Analysis Wantoro, Agus; Caesarendra, Wahyu; Syarif, Admi; Soetanto, Hari
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.758

Abstract

Early detection of thyroid cancer recurrence is a crucial factor in patient survival and treatment effectiveness. Misdetection results in disease severity, high cost, recovery time, and decreased service quality. In addition, the main challenges in developing a Machine Learning (ML)-based detection decision support system are class imbalance in medical data and high feature dimensions that can affect model accuracy and efficiency. This study proposes a feature selection-based approach and class imbalance handling to improve the performance of early detection of Thyroid cancer. Several feature selection techniques, such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Chi-Square (CS), can select features based on weighted ranking. In addition, to overcome the imbalanced class distribution, we use the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as k-NN, Tree, SVM, Naive Bayes, AdaBoost, Neural Network (NN), and Logistic Regression (LR) are tested and evaluated based on a confusion matrix, including accuracy, precision, recall, time, and log loss. Experimental results show that the combination of imbalanced class handling strategies significantly improves the prediction performance of ML algorithms. In addition, we found that the combination of CS+NN feature selection techniques consistently showed optimal performance. This study emphasizes the importance of data pre-processing and proper algorithm selection in the development of a machine learning-based thyroid cancer detection system.
Comparative Analysis and Mitigation of Extremely Low Frequency (ELF) Magnetic Field Exposure from Smartphone Internal Yudha, Bayu Wira; Zakaria, Hasballah
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.781

Abstract

While public concern regarding smartphone electromagnetic field (EMF) exposure is largely focused on radio frequency (RF) emissions, this study investigates the overlooked extremely low frequency (ELF) magnetic fields originating from internal hardware circuits, such as the Power Management Integrated Circuit (PMIC). This research employs a quantitative experimental methodology to characterize and compare the near-field emissions of two smartphone models with distinct internal architectures: the Xiaomi Redmi Note 8 Pro (12nm mid-range chipset) and the Samsung Galaxy S23 (4nm premium chipset). Magnetic field intensity measurements were conducted using a Hall-effect Gaussmeter, both in free space and with a 3D-printed cubic head phantom fabricated from PETG and filled with a conductive saline-based tissue-simulating liquid (TSL). The primary findings reveal unique ELF emission "fingerprints," where the premium-engineered device exhibits a peak exposure of 0.269 mT—nearly three times lower than its mid-range counterpart at 0.799 mT. Theoretical analysis utilizing the Biot-Savart Law attributes this reduction to the minimized current loop areas inherent in advanced 4nm process nodes compared to older 12nm architectures. Quantitative analysis of mitigation strategies demonstrates that spatial separation (a 15 cm distance) is the most dominant factor, achieving up to 90.7% attenuation, which surpasses the material shielding provided by the phantom (82.0%). Although peak contact exposure can exceed the ICNIRP reference level, the rapid near-field decay ensures compliance at minimal practical distances. This study concludes that ELF exposure is a function of engineering quality rather than network technology, and mitigation is most effectively achieved through physical distance.

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