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 321 Documents
 29   30   31   32   33 
LSTM-Based Daily Power Forecasting for a 1 MWp PV System in Tropical Indonesia: Toward Operational Optimization Ali Muhtar; Syamsyarief Baqaruzi; Putty Yunesti
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.777

Abstract

Variations in solar irradiance and module temperature significantly affect the performance and operational efficiency of large-scale photovoltaic (PV) power systems, especially in tropical regions. This study investigates the application of a Long Short-Term Memory (LSTM) network for accurate real-time power prediction in a 1 MWp PV power plant at Institut Teknologi Sumatera (ITERA), Indonesia. Unlike traditional approaches and conventional artificial neural networks (ANN), LSTM networks can effectively capture long-term temporal dependencies and highly nonlinear patterns in PV output data. A five-minute resolution dataset, including actual power output, solar irradiance, and module temperature, was collected throughout March 2025 for model training, with validation performed using independent data from April. The developed LSTM model achieved a mean absolute error (MAE) of 42.8 kW (approximately 4–6% of maximum plant capacity) and a coefficient of determination (R²) of 0.84 during active hours (05:00–19:00) on the validation dataset. These findings indicate that the model performs well not only on the training data, but also maintains strong generalization to unfamiliar data. The proposed approach enables reliable real-time power prediction, supporting applications such as energy forecasting, inverter control, dispatch planning, and anomaly detection in PV systems. This work provides a practical and scalable solution for improving the adaptability and integration of solar power plants in dynamic tropical environments, contributing to the advancement of AI-driven sustainable energy systems.
Epilepsy Classification Using Support Vector Machine with Frequency Domain Feature Extraction Hindarto Hindarto; Ade Eviyanti; Ahmad Ahfas; Egha Arya Affandi
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.797

Abstract

Epilepsy is a brain-related condition characterized by abnormal electrical activity in the brain. This condition can be identified by observing EEG signals, which record the brain's electrical activity. Automatically detecting seizures using EEG signals helps doctors diagnose the condition more quickly and accurately. In this study, a method is proposed that uses a Support Vector Machine (SVM) to classify EEG signals. The features used for classification are extracted from the frequency domain using a technique called Fast Fourier Transform (FFT). The dataset used is called the UCI Epileptic Seizure Recognition Dataset, which includes 11,500 EEG samples divided into five classes. These samples are then categorized into two main types: seizures and non-seizures. The research process includes data preprocessing with MinMaxScaler normalization, feature extraction using FFT, and data classification using SVM with varying numbers of features. Model performance is measured using several metrics, including accuracy, precision, recall, F1 score, and ROC-AUC. The results showed that the use of 21 features with the SVM model provided the best performance, with an accuracy of 97.7%, a precision of 93.2%, a recall of 95.4%, an F1 score of 94.3%, and an AUC value of 0.9930. These results are better than previous studies using similar methods, indicating that the combination of FFT and SVM is effective for detecting epilepsy using EEG signals. These findings help build a more reliable system for medical diagnosis and highlight the importance of using balanced evaluation measures in healthcare.
IoT-Based Soil Condition Monitoring System for Supporting Precision and Sustainable Agriculture in West Java Ayu Latifah; Dendi Ardimansah
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.807

Abstract

West Java Province is one of the largest horticultural producers in Indonesia. The horticulture subsector in West Java covers tens of thousands of hectares of farmland and contributes approximately 1.8% to the province’s total Gross Regional Domestic Product (GRDP). However, this sector faces serious challenges. Climate change has increased the frequency of droughts and floods, disrupting horticultural production. In addition, soil fertility has declined due to unsustainable farming practices; many agricultural lands in West Java contain less than 2% organic matter as a result of long-term chemical fertilizer use. These challenges have led to decreased productivity and threaten the sustainability of horticultural farming. Precision agriculture emerges as a solution to these issues. This concept leverages IoT technologies such as the ESP32 microcontroller with SPIFFS, soil sensors, and a web-based dashboard to monitor land conditions in real time and manage inputs more efficiently. The IoT-based soil monitoring system developed in this study was tested in a farmer’s yard in Pameungpeuk Subdistrict, Garut. The results demonstrated practical benefits, including improved irrigation efficiency (up to ~30% water savings) and reduced risk of crop losses through early detection of drought conditions. Moreover, the application of IoT in potato cultivation in West Java has been shown to double seed tuber yields from 15,000 to 30,000 tubers. These findings are consistent with regional policy directions, as reflected in the West Java Regional Medium-Term Development Plan (RPJMD) and the “Petani Milenial” (Millennial Farmers) program, which promote sustainable agricultural modernization through IoT-based technologies to enhance the productivity of young farmers.
Simulation-Based Design of LTE Indoor Networks in High-Density Building Scenarios Ryan Prasetya Utama; Regina Leonnie; Faiz Husnayain
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.816

Abstract

The implementation of 5G mobile communication technology in Indonesia faces significant challenges due to limited infrastructure and coverage in some areas, that’s why 4G technology is still the favorite among the public. However, not all areas are covered by a good quality 4G LTE (Long Term Evolution) network, especially inside buildings, because the signal from the eNodeB weakens due to the building construction. This study aims to determine the optimal antenna configuration to improve network quality inside the building, in this case in the XYZ building by implementing an IBS (Indoor Building Solution) on the 4G LTE network using Radiowave Propagation Simulator software. The IBS design uses two approaches, namely coverage dimensioning and capacity dimensioning. The simulation comparing the location of antenna placement in the center or edge building and the results show that placing the antenna in the center of the building is more optimal than placing it at the edge of the building. The average of RSSI (Received Signal Strength Indicator) values obtained from the ground floor to the 2nd floor are -46.67 dBm, -50.09 dBm, and -49.41 dBm, respectively. Meanwhile, the average of SINR (Signal to Interference plus Noise Ratio) values obtained from each ground floor to the 2nd floor are 18.77 dB, 23.20 dB, and 27.77 dB, respectively. Both parameter values are included with an Excellent rating based on the RKX operator's KPI (Key Performance Indicator) standards. It is hoped that the results of this research can be used as a reference, model, and implemented in other indoor locations so that 4G network coverage in Indonesia can be further expanded, especially indoors.
Explainable IoT Intrusion Detection Using Random Forest, SMOTE, and SHAP Julfikar Mawansyah; Anik Nur Handayani; Aji Prasetya Wibawa; Triyanna Widiyaningtyas; Mokh. Sholihul Hadi; Fidyah Ajeng Wulandari
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.823

Abstract

Class imbalance in Internet of Things (IoT) Intrusion Detection System (IDS) datasets is a major challenge that degrades the detection performance on minority attacks and complicates model interpretability. This study investigates the performance of an IoT IDS based on Random Forest (RF) combined with the Synthetic Minority Over-sampling Technique (SMOTE) and explainable AI analysis using SHapley Additive exPlanations (SHAP) on the public IoTID20 dataset. The research pipeline consists of data preprocessing (cleaning, numerical and categorical feature encoding, normalization), stratified train–test split into 7,000 training and 3,000 test samples, training an RF baseline on the imbalanced training set, applying SMOTE to balance the DoS, Scan, Normal, and MITM ARP Spoofing classes, and training an RF–SMOTE model. The models are compared using accuracy, precision, recall, and F1-score per class as well as macro averages. Afterwards, SHAP is employed to analyse global feature importance for both models. Experimental results show that the RF baseline already achieves very high performance with an accuracy of about 0.99 and a macro F1-score of approximately 0.984, while the RF–SMOTE model maintains the same accuracy with a macro F1-score of around 0.983. SMOTE substantially improves the class distribution in the training set but yields only minor differences in aggregate performance, RF–SMOTE slightly enhances sensitivity for some minority classes, whereas the F1-score for the MITM ARP Spoofing class decreases marginally compared to the baseline. SHAP analysis indicates that flow-related traffic features such as connection duration, packet counts, byte volume, and packet direction ratios are consistently the most influential features in both models. Changes in SHAP values for the RF–SMOTE model highlight an increased relative contribution of features representing rare attack patterns, making explanations for minority classes more prominent. Overall, the proposed RF–SMOTE–SHAP framework delivers a high-performing IoT IDS while providing improved transparency in explaining detection decisions, thereby supporting the development of trustworthy and interpretable IDS solutions for IoT environments.
Numerical Performance Analysis of Novel-Generation Materials for Mach–Zehnder Interferometer Applications Riski Ramadani; Afiyah Nikmah; Arum Vonie Rachmawati; Hanan Zaki Alhusni; Rohim Aminullah Firdaus; Dzulkiflih Dzulkiflih
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.837

Abstract

Advances in optical technology have driven the need for new-generation materials that are more efficient in Mach–Zehnder interferometer (MZI) structures, particularly for high-precision optical sensors and communication applications. This study aims to evaluate the optical performance of ten material configurations, namely As₂S₃, a-GST, BTO (TE), BTO (TM), c-Si, LNOI (TE), LNOI (TM), PMMA, Si₃N₄, and SU-8, in an MZI configuration using a numerical simulation approach based on the Beam Propagation Method (BPM), the Crank–Nicolson scheme, and the Padé approximation method. Simulations were conducted using MATLAB software to analyze the transverse Ey field, refractive index profiles, and optical transmission efficiency. The results indicate that materials such as BTO (TE/TM), LNOI (TE/TM), and As₂S₃ exhibit superior performance with Ey loss values ≤ 0.01, indicating excellent optical field confinement and high transmission efficiency. Meanwhile, polymer materials, such as PMMA and SU-8, exhibit poor performance with significant loss values, making them unsuitable for active waveguide functions. The novelty of this study lies in the systematic comparison of ten novel-generation material configurations under a uniform MZI simulation framework, enabling the direct evaluation of material-dependent optical confinement, Ey field loss, and transmission efficiency. These findings provide practical guidance for selecting suitable materials for next-generation MZI-based optical sensors, modulators, and integrated photonic devices.
Bio-Based Surface Engineering of AISI 316L for Durable Electromedical Devices Muhammad Akbar Hariyono; Galih Persadha; Ahmad Robittah; A'yan Sabitah
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.853

Abstract

This study investigates a bio-based pack carburizing treatment for AISI 316L austenitic stainless steel using an environmentally friendly carburizing mixture composed of 70% Alaban wood charcoal and 30% eggshell powder. The treatment was conducted to improve the surface properties of AISI 316L for potential use in non-implant metallic components of electromedical devices. Solid-state pack carburizing was performed at 600 °C, 700 °C, and 800 °C for 3 h. The treated specimens were evaluated in terms of surface carbon content, microstructural changes, diffusion layer thickness, surface hardness, and hardness distribution. The results showed that increasing carburizing temperature enhanced carbon absorption and surface modification. The highest surface carbon content of 0.80% was obtained at 800 °C. At the same temperature, the surface hardness increased to 346 HV, and the maximum diffusion layer thickness reached 16.7 µm. Microstructural observations revealed the gradual formation of a darker and more continuous carbon-enriched modified layer as the carburizing temperature increased. These improvements indicate that the treated surface became more resistant to localized deformation, repeated contact, and light friction, which are important factors for maintaining the durability and functional reliability of metallic parts in electromedical devices. These findings indicate that the Alaban wood charcoal–eggshell powder mixture can act as an effective bio-based carburizing medium for improving the surface durability of AISI 316L stainless steel. Therefore, the proposed treatment not only enhances the surface performance of AISI 316L but also offers a sustainable and low-cost surface engineering approach for non-implant electromedical components exposed to repeated handling, cleaning, and maintenance activities.
Front Cover Vol. 26 No. 1
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.865

Abstract

Preface Vol. 26 No. 1
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.866

Abstract

Back Cover Vol. 26 No. 1
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.867

Abstract

Page 32 of 33 | Total Record : 321

 29   30   31   32   33 

Filter by Year

2010 2026


Reset
Filter By Issues
All Issue Vol. 26 No. 1 (2026) 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