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Jurnal Teknik Elektro
Published by Universitas Negeri Semarang
ISSN : -     EISSN : 25491571     DOI : https://doi.org/10.15294/jte
Core Subject : Engineering,
Electrical & Electronics Engineering
Jurnal Teknik Elektro merupakan jurnal yang berisikan tentang artikel dalam bidang Teknik Elektro (Ketenagaan, Elektronika dan Kendali, Pengolahan Isyarat serta Komputer dan Informatika)
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 27 Documents
 1   2   3 
Performance Evaluation of Propagation Model in DVB-T2 Broadcasting: Case Study in Kebumen Cici Tri Eviana; Siti Zulaecha; Ganang Tulus Prananda; Solichah Larasati; Muhammad Panji Kusuma Praja
Jurnal Teknik Elektro Vol. 18 No. 1 (2026)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v18i1.23531

Abstract

The widespread change from analog to digital television broadcasting in Indonesia has led to the emergence of blank spots in some areas. To overcome this problem, an optimal propagation model is needed to improve transmitter coverage, energy efficiency, and field strength through optimal transmit power for DVB-T2 broadcasts. This research addresses the selection and analysis of an appropriate propagation model, focusing on the application of the Longley-Rice and ITU-R.P 1546-6 models in the Kebumen region of Central Java, Indonesia. This study specifically compares the two models to determine the most effective model in reducing blank spots in the area. The results show that the Longley-Rice model produces greater field strength than the ITU-R.P 1546-6 model. In addition, this study also found that the farther the receiver is from the transmitter, the higher the free space loss value. This is because due to the hilly geographical conditions in the Kebumen area, it causes high loss. And the farther the receiver is from the transmitter and the greater the frequency value, the greater the field strength value. This research not only provides relevant empirical data for the Kebumen region but also offers insights that can be used in the optimization of digital broadcasting in other urban environments in the future.
Predictive Modeling of Cassava Bioethanol Production Using Random Forest Sandi; Bernolpus Tingge; Suhendri; Jonson Manurung; Amirul Mahmud; Kenny Stevanus Pangemanan; Ghozi Falah Santoso; Ichsan
Jurnal Teknik Elektro Vol. 18 No. 1 (2026)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v18i1.35593

Abstract

Indonesia is accelerating its energy transition to reduce reliance on fossil fuels and achieve a 23% renewable energy mix by 2025. Bioethanol has emerged as a promising renewable alternative, particularly under the current E5 (5% ethanol blend) program and the roadmap toward higher integration. This study presents a Machine Learning (ML) framework for predicting cassava-based bioethanol production in West Java, applying Random Forest (RF) modeling to estimate cassava yields and a conversion process to calculate ethanol output. The dataset comprises 3,000 monthly observations from 27 districts (2013–2024), integrating agronomic, climatic, and economic variables such as harvested area, rainfall, temperature, and farm-gate price. Model validation using blocked time-series Cross-Validation (CV) demonstrated high predictive accuracy ( ; Root Mean Square Error (RMSE) = 191 kL; Mean Absolute Error (MAE) ≈ 40 kL), confirming the effectiveness of ensemble learning in capturing nonlinear agricultural dynamics. Feature importance analysis identified harvested area and rainfall as the most influential predictors, underscoring the relevance of climate-adaptive farming and sustainable land-use strategies. Spatial evaluation highlighted Garut, Sukabumi, and Tasikmalaya as priority districts. Aggregated projections, recalculated with verified conversion factors (=5.86 megawatt-hour per kiloliter, MWh/kL), indicate an energy potential of 820,000 MWh/year and substantial Carbon Dioxide (CO₂) reduction under E5 blending. Despite limitations related to data inconsistencies in productivity units and fluctuating price records, the findings demonstrate that cassava-based bioethanol can serve as a viable pathway for renewable energy diversification, provided that agricultural productivity, infrastructure, and policy alignment are strengthened.
Leakage-Aware Fault Detection in Three-Phase Power Systems Using an Artificial Neural Network and Scenario-Based Data Splitting Verdi Yasin; Sri Mardiyati; Ifan Junaedi; Rumadi Hartawan; Ito Riris Immasari; Ruben Cornelius Siagian
Jurnal Teknik Elektro Vol. 18 No. 1 (2026)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v18i1.40675

Abstract

The reliability of three-phase power systems depends heavily on the ability to detect faults quickly and accurately. However, much previous research has faced challenges such as data leakage, biased evaluations due to random data splitting, and limitations in the utilisation of system dynamics-based features. This study aims to develop a machine learning-based fault detection model that is accurate, robust, and possesses high generalisation capabilities. The method employed is a quantitative approach using an Artificial Neural Network (ANN) model as a binary classifier (normal and fault). The research process includes data preprocessing, elimination of features that could potentially cause data leakage, feature engineering based on current, voltage, energy, and system imbalance, as well as scenario-based data splitting to ensure the validity of the evaluation. The model was trained using repeated cross-validation and optimised based on the Area Under the Curve (AUC) metric. The results show that the ANN model achieved high performance with an AUC of 0.987 and an accuracy of approximately 98.5% and remained stable under varying conditions such as noise and load changes. Compared to other models, Random Forest delivered the best performance, followed by Support Vector Machine (SVM) and ANN. This study addresses the gap regarding more realistic evaluation methods that are free from data leakage. Its novelty lies in the use of scenario-based splitting, comprehensive evaluation, and the finding that raw signal features make a dominant contribution to fault detection.
Automatically Retrained Machine Learning System for Rice Yield Prediction Using Open-Meteo and BPS Data Ulfatun Nadifa; Ikhsan Hidayat; Wildan
Jurnal Teknik Elektro Vol. 18 No. 1 (2026)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v18i1.41368

Abstract

This study proposes a machine learning–based rice yield prediction system with a self-updating mechanism, using Gorontalo Province, Indonesia, as a case study. The system integrates daily climate data from Open-Meteo with agricultural statistics from the Central Bureau of Statistics (BPS) to support data-driven decision-making in agriculture. A key challenge addressed in this study is the limited availability of yield data, which are provided only at an annual scale for the period 2018–2024, without seasonal labels. To overcome this limitation, a temporal disaggregation approach is adopted to construct initial seasonal yield labels (M1, M2, M3). These constructed labels serve as approximations, enabling the development of a seasonal prediction model under data-constrained conditions. Several machine learning algorithms, namely Gradient Boosting, Random Forest, XGBoost, Ridge Regression, and Linear Regression, are evaluated using Leave-One-Out Cross-Validation (LOO-CV). Model performance is assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results indicate that ensemble-based models outperform linear baselines, with Gradient Boosting providing the best balance between prediction accuracy and model stability. The main contribution of this study is the design of an adaptive prediction system with a self-updating mechanism that supports periodic retraining and dynamic model evaluation. At its current stage, this mechanism is positioned as an initial framework rather than a fully validated continuous learning system. The proposed system is implemented as a web-based platform that supports yield prediction and planting season recommendations, providing a scalable foundation for intelligent agricultural systems in data-limited environments.
Arduino-Based ECU Simulator and OBD-II Scanner for Electric Vehicle Diagnostics with CAN BUS Tole Sutikno; Mulyadi; Son Ali Akbar; Tri Wahono
Jurnal Teknik Elektro Vol. 18 No. 1 (2026)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v18i1.41900

Abstract

Electric vehicles (EVs) require reliable and accurate diagnostic systems to ensure the performance, safety, and efficiency of their electrical and electronic components. This study presents the design and development of an Arduino-based Electronic Control Unit (ECU) simulator and an On-Board Diagnostics II (OBD-II) scanner for electric vehicle diagnostics, both using the Controller Area Network (CAN) communication protocol. The ECU simulator is built using an Arduino Mega 2560 and an MCP2515 CAN controller, equipped with potentiometers and push buttons to simulate various EV operating parameters such as motor speed, vehicle speed, battery voltage, temperature, state of charge, and regenerative braking conditions, as well as to generate Diagnostic Trouble Codes (DTCs). The OBD-II scanner is developed using an Arduino Uno, MCP2515 module, and a Nextion HMI LCD to support live data monitoring, DTC reading, and DTC clearing in accordance with SAE J1979 standards. System performance was evaluated using a CAN analyzer to verify frame structure, arbitration behaviors, data integrity, and communication reliability. The experimental results show that all transmitted CAN frames conform to the ISO 11898 standard and are correctly interpreted by the OBD-II scanner. The system achieved data accuracy of approximately 97% with an average response time of ±100 ms, indicating stable, reliable communication. The developed platform provides a low-cost, flexible, and effective solution for electric vehicle diagnostics, education, and laboratory testing without requiring real vehicles. It can serve as a foundation for future research involving multi-ECU communication and additional OBD protocols.
Bandwidth Enhancement of Proximity-Coupled Patch Antenna Using Dual Open-Ended Ring-Shaped Slots for Sub-6 GHz Applications Rheyuniarto Sahlendar Asthan; Mia Maria Ulfah
Jurnal Teknik Elektro Vol. 18 No. 1 (2026)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v18i1.47475

Abstract

This paper presents a bandwidth enhancement of patch antenna intended for sub-6 GHz applications. A square-shaped radiating patch is configured on a double-layer FR-4 dielectric substrates with a dielectric constant of 4.3 and a total thickness of 3.2 mm. To improve the impedance bandwidth, the antenna is fed using proximity-coupling method, in which the microstrip feeding line and the ground plane are placed on the top and bottom layers of the second substrate. Dual open-ended ring-shaped slots are subsequently introduced and etched on the radiating patch element to further enhance the antenna bandwidth. Various slot structures are evaluated, and their physical parameters are varied in a parametric study to analyze their effect on antenna performance, particularly impedance bandwidth. The proposed antenna has a compact dimension of 0.38λ0×0.38λ0×0.06λ0 and exhibits wideband characteristics with a fractional bandwidth of 22.3%.
A Two-Stage High-Gain Quadratic Boost Converter Using Inverse-Coupled-Inductors for Low Input-Current Ripple Alwy Muhammad Ravi; Herlambang Setiadi; Muhammad Zakiyullah Romdlony
Jurnal Teknik Elektro Vol. 18 No. 1 (2026)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v18i1.48133

Abstract

High step-up DC–DC converters are required to interface low-voltage renewable energy sources with higher-voltage dc buses in dc microgrid applications. This paper presents a simulation-based two-stage high-gain DC–DC converter integrating an inverse-coupled-inductor front end with a quadratic boost rear stage. The inverse-coupled front end is employed to reduce source-side input-current ripple and mitigate dc-flux accumulation, thereby lowering the risk of magnetic-core saturation in the front-stage inductors under high-power operation. Compared with uncoupled and direct-coupled configurations, the inverse-coupled arrangement enables ripple-current cancellation between the two input phases, which improves its feasibility for higher-power implementation. The quadratic boost rear stage provides high voltage gain and supports a more stable dc-bus voltage response. Analytical derivations of the operating principle, voltage gain, and input-current ripple ratio are presented, followed by comparative simulation of the three inductor configurations. The results show that the inverse-coupled configuration achieves the lowest average input-current ripple ratio of , with a minimum value of  at  = 0,5. The converter also produces an output voltage close to 400 V from a 20 V input, corresponding to a voltage gain of approximately 20. These results indicate that the proposed topology offers a promising trade-off between high voltage gain, reduced front-stage magnetic stress, source-side ripple suppression, and output-voltage stability for renewable-energy-fed dc-bus applications.

Page 3 of 3 | Total Record : 27

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