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INDONESIA
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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Implementation of Bidirectional Encoder Representations from Transformers in a Content-based Music Recommendation System for Digital Music Platform Users Suyudi, Fadil Abdillah; Furqon, Muhammad Ariful; Ar Ruhimat, Qurrota A'yuni
Jurnal Elektronika dan Telekomunikasi Vol 25, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.660

Abstract

Digital music platform users today have unlimited access to millions of songs from various genres and artists through music streaming services. However, with so many music platforms available, users often need help finding songs that suit their preferences. This study presents a music recommendation system that utilizes lyrical analysis to provide users with relevant song suggestions based on selected lyrics. The system employs a two-pronged approach: the Term Frequency-Inverse Document Frequency (TF-IDF) method for initial feature extraction and the IndoBERT model for advanced contextual representation of song lyrics. A dataset of 8,944 Indonesian language songs was compiled using scraping techniques from various sources. The recommendation process is driven by cosine similarity calculations between the lyrics of the selected songs and the entire dataset, enabling the identification of songs with similar themes and messages. Model evaluation through a five-fold Multi-Class Cross-Validation (MCCV) approach yielded promising results, indicating high precision, recall, and F1 scores. The study results show that the system built can provide recommendations with good precision performance with Precision@k values varying between 0.7965 to 0.8371, Recall@k values ranging from 0.8017 to 0.8204, and F1-score@k values varying between 0.8083 up to 0.8190. Overall, the model shows strength in providing accurate recommendations and good performance stability
A Robust SMO-PLL Estimation Algorithm for Enhancing Rotor Position Accuracy and Reducing Chattering Issues in Sensorless FOC of SPMSM Cahayasabda, Nektar; Ishak, Sekhul; Wibowo, Danang Suryo; Salsabila, Aulia Rahmah; Az Zahra, Syifa Fajry; Hani’ah, Isyatul; Fathoni, Khoirudin; Syah, Mario Norman
Jurnal Elektronika dan Telekomunikasi Vol 25, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.704

Abstract

Recent advancements in sensorless Field-oriented Control (FOC) of Surface Permanent Magnet Synchronous Motors (SPMSMs) have improved system reliability and cost-effectiveness. However, limitations such as speed chattering and inaccurate rotor position estimation remain problematic for Electric Vehicle (EV) applications. This study developed a sliding mode observer-phase locked loop (SMO-PLL) algorithm applied to sensorless FOC in SPMSMs. The SMO predicts the back EMF of the SPMSM, which the PLL then uses for precise rotor position and speed estimation. Simulations conducted in MATLAB Simulink demonstrate that the SMO-PLL significantly reduces chattering and achieves a rotor position estimation error of only 1 rad/min. While the quantitative integral error criteria for SMO-PLL (IAE: 0.0868, ITAE: 0.3069, ISE: 0.0229, ITSE: 0.0834) are slightly higher than those of Field Observer (FO) and Extended Electromagnetic Field Observer (EEMFO), speed control analysis confirms that SMO-PLL delivers a rapid steady-state response with minimal overshoot and oscillation. These findings are crucial for applications where speed stability is essential for passenger comfort and safety, highlighting the SMO-PLL's potential as a robust sensorless control solution for future EVs.
Back Cover Vol. 25 No. 1 Prini, Salita Ulitia
Jurnal Elektronika dan Telekomunikasi Vol 25, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.792

Abstract

Advanced State Estimations for Gravitational Oil/Water Separator Tanks using a Kalman Filter and Multi-Model Hypothesis Testing Cahya, Zaid; Siregar, Parsaulian; Ekawati, Estiyanti; Bahiuddin, Irfan; Cahya, Dito Eka; Nugroho, Tsani Hendro; Taufiqurrohman, Heru; Boudaoud, Mohammed
Jurnal Elektronika dan Telekomunikasi Vol 25, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.682

Abstract

This paper presents a new application of the Kalman filter with Hypothesis testing for a fast and robust model-based estimator for measuring level interfaces of atmospheric gravitational oil-water separator tanks. A newly developed semi-empirical linearized model is applied in the estimator algorithm. A multi-model hypothesis-testing algorithm for covering more scenarios was deployed. The proposed method provides a cost-effective and straightforward solution for estimating all state variables in an oil-water separator. Our evaluation results demonstrate that the proposed algorithm achieves high accuracy with an observation error of less than 2% and a false alarm rate of 3.3% under 50-70% working conditions. Furthermore, the estimator can effectively handle process noise with a 10% feed offset. The proposed platform requires only a few installed sensors yet can accurately estimate unknown parameters. The proposed approach offers a robust and practical soft sensor solution for gravitational oil/water separators
Cardiac Imaging with Electrical Impedance Tomography (EIT) using Multilayer Perceptron Network Ristyawardani, Amelia Putri; Baidillah, Marlin Ramadhan; Adityawarman, Yudi; Busono, Pratondo; Rachmadi, Mochamad Adityo; Yantidewi, Meta; Rahmawati, Endah
Jurnal Elektronika dan Telekomunikasi Vol 25, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.705

Abstract

This research explores the enhancement of Electrical Impedance Tomography (EIT) for cardiac imaging using Multilayer Perceptron (MLP) networks, focusing on supervised and semi-supervised learning approaches. Using synthetic thoracic datasets simulating dynamic cardiac and respiratory conditions, the study demonstrates that supervised learning achieves lower mean squared error (MSE) values (minimum 4.76) and more stable predictions compared to semi-supervised learning (minimum MSE 5.08). However, semi-supervised learning excels in edge accuracy and noise reduction, particularly in regions with sharp conductivity gradients, making it viable for scenarios with limited labeled data. Dropout regularization at 0.3 provided optimal balance, enhancing model generalization and robustness. While supervised learning outperformed semi-supervised methods in overall accuracy, the latter showed potential for cost-effective and scalable applications in EIT-based cardiac imaging. These findings suggest that integrating advanced machine learning with EIT can improve diagnostic accuracy and enable efficient use of sparse labeled data, paving the way for future optimizations and clinical applications.
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.

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