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TELKOMNIKA (Telecommunication Computing Electronics and Control)
Published by Universitas Ahmad Dahlan
ISSN : 16936930     EISSN : 23029293     DOI : 10.12928
Core Subject : Science,
Computer Science & IT
Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of submissions that TELKOMNIKA has received during the last few months the duration of the review process can be up to 14 weeks. Communication Engineering, Computer Network and System Engineering, Computer Science and Information System, Machine Learning, AI and Soft Computing, Signal, Image and Video Processing, Electronics Engineering, Electrical Power Engineering, Power Electronics and Drives, Instrumentation and Control Engineering, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 3,484 Documents
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Beamforming design for interference cancellation in fifth generation new radio system Heba Shafeik Kashour; Abdelrazak Badawieh; Ali Ahmad; Mohammad Rizk Assaf
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27664

Abstract

This study proposes a hybrid beamforming (HBF) architecture for interference mitigation design aimed at improving interference cancellation in fifth-generation new radio (5G-NR) systems. The proposed architecture employs interference suppression at two processing level: the linear eigen zero forcing (EZF) method is applied at the base station (BS) to mitigate inter-user interference (IUI), while the nonlinear successive interference cancellation (SIC) is implemented at each user terminal to cancel intra-user interference. Although EZF is linear, meaning it doesn’t require iterative or cumulative operations, its operations are complex and rely on matrix inversion. On the other hand, SIC is a non-linear algorithm, meaning it requires many sequential iterative operations, which increases the probability of cumulative errors. Therefore, using EZF as the first stage makes the signals to be processed in SIC less overlapping, thus reducing cumulative errors, especially under varying channel conditions. The single to interference and noise ratio (SINR) reached a value of 11.9 dB. This is based on the simulation scenarios that were tested. and a channel quality indicator (CQI) value of 10. Moreover, the proposed scheme provides a spectral efficiency of 36 bit/s/Hz at 100 MHz bandwidth and -20 dB signal-to-noise ratio (SNR), achieving performance approaches the performance of a fully digital interference-free system under the considered simulation conditions. while maintaining practical computational complexity.
MRI segmentation and classification using 1-D and 2-D empirical mode decomposition Giang Hong Nguyen; Yen Thi Hoang Hua; Ngan Vuong Thuy Nguyen; Liet Dang Van
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27976

Abstract

This study proposes a new framework for brain magnetic resonance imaging (MRI) tumor detection and classification using 1-D and 2-D empirical mode decomposition (EMD) / bidimensional empirical mode decomposition (BEMD). These adaptive algorithms optimize image denoising, segmentation and feature extraction. Tumors are detected using a global threshold derived from EMD-generated bimodal curves. A visual geometry group 19 (VGG19) model extracts features from the regions of interest (ROI) across both denoised images and BEMD components. The method was validated on the public Figshare brain tumor dataset. For segmentation, the proposed method performed similarly to multilevel Otsu, Kapur-Harmonic, and K-means algorithms. It achieved an average precision of 96.70%, slightly trailing K-means of 97.07%, and a sensitivity of 91.07%, outperforming K-means but falling behind Kapur and Otsu. Notably, it achieved the highest F1-score of 93.70%, tying the Otsu technique. These results highlight enhanced tumor localization, reflecting segmentation that closely aligns with the ground truth. For tumor classification, ensemble learning was used to develop nine models, all of which surpassed 93% accuracy; one achieved 100%, and three reached 96.7%. The proposed method outperformed seven state-of-the-art techniques with a peak accuracy of 99.02%, demonstrating its clinical viability. Overall, integrating EMD and BEMD algorithms provides a superior framework for brain tumor segmentation and classification compared traditional methods.
Solid oxide fuel cells as next‑generation high‑efficiency alternatives to combined cycle gas turbines Ignatius Riyadi Mardiyanto; Handrea Bernando Tambunan; Haryadi Haryadi; Lina Troskialina; Sri Paryanto Mursid; Yusuf Sofyan; Lidya Elizabeth
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27887

Abstract

This study provides a direct thermodynamic comparison between a combined cycle gas turbine (CCGT) and a solid oxide fuel cell (SOFC) system, each designed for approximately 450 MW gross output. The benchmark CCGT, based on Siemens STG5‑4000F and SST5‑5000 units, achieved 61% gross efficiency under baseline conditions, with moderate sensitivity to fuel composition. In contrast, the SOFC system, modeled with an active cell area of 250,000 m², demonstrated superior performance, reaching nearly 70% gross efficiency and exceeding 63% net efficiency, while requiring lower fuel input (18.5 kg/s versus 19.5 kg/s for CCGT). Efficiency trends highlight fundamental differences: CCGT shows incremental gains with increased fuel flow but remains below 57% net efficiency, whereas SOFC sustains higher values despite greater sensitivity to input variations. These findings emphasize SOFC’s potential as a next‑generation utility‑scale power technology, offering higher efficiency and reduced fuel demand, while acknowledging challenges in scalability, durability, and cost that must be addressed for practical deployment.
Robustness analysis of distributed CFAR detection with K-out-of-L fusion in heterogeneous environments Hung Van Pham; Tinh Dinh Nguyen
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27784

Abstract

To operate effectively in complex environments, distributed radar systems re quire a robust constant false alarm rate (CFAR) processor. Conventional distributed cell-averaging (D-CA) CFAR detectors are optimal in homoge neous noise. However, they suffer severe performance degradation in non homogeneous scenarios, specifically under multiple interfering targets and abrupt clutter edges. This study presents a comprehensive performance analysis of distributed order statistics (D-OS) CFAR systems employing the K-out-of-L fusion rule. We derive the exact global detection and false alarm probabilities using the Poisson binomial distribution. These models are implemented via vari able precision arithmetic to ensure numerical stability under extreme masking conditions. Detection thresholds are rigorously calibrated to maintain a fixed global false alarm rate of 10−4 in homogeneous backgrounds. Quantitative re sults demonstrate that D-OS-CFAR significantly outperforms the D-CA-CFAR baseline. It maintains stable detection performance (PD > 0.9) under clutter power variation and suppresses false alarm surges at clutter edges to approxi mately 4.5 × 10−4. The analysis concludes that lower-K rules (e.g., K = 1,2) offer the optimal trade-off, limiting the homogeneous CFAR loss to below 0.5 dB while ensuring stable operation in highly heterogeneous environments.
Power optimization in 5G network bands using modified genetic algorithm Anand Gachhadar; Nanda Bikram Adhikari; Surendra Shrestha; Ram Krishna Maharjan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27024

Abstract

This study investigates power optimization techniques in fifth-generation (5G) networks to enhance network performance across multiple metrics. Slow con vergence and often getting stuck in local optima is an issue in traditional ge netic algorithm (GA). Whereas, with machine learning (ML) algorithms large datasets are required and constraints cannot be enforced. To avoid local min ima and achieve global optimal solution, the average fitness value is checked in modified GA after completion of the total count Nc, so that the change in fitness should be greater than 1%. In this research work, modified GA algorithm is used to optimize power allocation, minimize interference, and improve resource man agement across different frequency bands, specifically 700 and 3500 MHz. The results show that the modified GA achieves enhancement in spectral efficiency by 4% and 18%, success probability by 16% and 13% and power efficiency by 30% and 28% at 700 MHz and 3500 MHz frequencies respectively over ML at signal-to-interference-plus-noise ratio (SINR) = 30 dB. The analysis of power consumption with respect to threshold SINR reveals that modified GA approach reduces power usage by 18% and 14% over ML at 700 and 3500 MHz respec tively, while maintaining acceptable SINR levels, especially at higher thresholds.
Review on badminton players hand-grip analysis and tactical dynamics prediction using egocentric vision Sriramachandra Murthy K.; Shekhar R.; Athira B. Kaimal
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27915

Abstract

Many works have already been carried out on computer vision-based sports analytics, activities in egocentric vision to improve player performance. But less is explored in terms of vision based kinematic analysis in badminton where shuttle trajectory is explored, angles are derived from features and analyzing biomechanics of the player first person vision cameras. This paper reviews existing research on badminton hand-grip analysis and tactical prediction using egocentric vision. It highlights key methods, datasets, and challenges in applying computer vision techniques to sports analytics. The review focuses on computer vision approaches for analyzing hand-grip patterns and predicting tactical dynamics in badminton using egocentric video data. The study summarizes current advancements, identifies research gaps, and outlines future directions for improving egocentric-vision-based sports analytics.
High-performance 28 GHz antenna array design for millimeter wave 5G systems Moussab Chbeine; Abderrahman Chbeine; Mohamed Bayjja; Lahcen Sellak
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27857

Abstract

This paper presents the design and performance evaluation of compact 28 GHz microstrip antenna arrays for millimeter-wave (mmWave) fifth generation (5G) wireless communication applications. The proposed antennas are implemented on a Rogers RT/duroid 5880 substrate with a relative permittivity of 2.2 and a thickness of 0.508 mm and were evaluated using full-wave electromagnetic simulations in Computer Simulation Technology (CST) Microwave Studio. Starting from a single antenna element, several scalable array configurations, including 1×2, 1×4, 1×8, and 2×8 structures, were designed to improve antenna gain and radiation directivity while maintaining good impedance matching characteristics. The novelty of this work lies in the development of compact scalable antenna arrays using a simple feeding network configuration to achieve progressive gain enhancement with reduced structural complexity. The simulation results show that the antenna performance improves as the number of array elements increases, achieving a maximum gain of 16.2 dB at 28 GHz with satisfactory return loss, voltage standing wave ratio (VSWR), and radiation behavior. In addition, the proposed arrays provide an appropriate trade-off between gain, bandwidth, and compact size, making them suitable candidates for future high-data-rate mmWave 5G communication systems.
Exploration of thesis topic trends of students majoring in informatics and computer engineering with LDA method Ruslan Ruslan; Rezki Nurul Jariah S. Intam; Sasmita Sasmita; Dewi Fatmarani Surianto; Andi Akram Nur Risal; Nur Azizah Eka Budiarti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27674

Abstract

The selection of a thesis topic is very important because it determines the focus of the research and its contribution to knowledge. However, many students find it difficult to choose a topic that suits their interests and expertise. This study models the thesis topics of students in the Department of Informatics and Computer Engineering (JTIK) using the latent Dirichlet allocation (LDA) method, with term frequency–inverse document frequency (TF–IDF) as the model input. The data set includes 969 thesis titles from 2009 to 2024. The optimized LDA model identifies 17 main topics by adjusting parameters such as the number of topics, alpha, and beta. The best coherence value (0.7431) is achieved with alpha = 0.81, beta = 0.01, and 17 topics. The dominant themes included information system development, computer networks, and multimedia, reflecting the main research areas of JTIK. In addition, a web-based system was developed and integrated with the best model to help students identify relevant topics and find thesis references. This study demonstrates the effectiveness of topic modeling in higher education and provides insights into academic research trends.
A hybrid CAVI-ML algorithm reducing blood pressure dependence in arterial stiffness assessment Lilia Radjef; Tahar Omari; Karim Baiche
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.28045

Abstract

This article introduces a hybrid cardio-ankle vascular index–machine learning method (CAVI-ML) that combines the classical non-invasive CAVI with a random forest (RF) model to reduce CAVI’s residual dependence on systolic blood pressure (SBP) and the pressure gradient. The key change is replacing the standard coefficients a and b with RF-predicted apred and bpred. Using a synthetic pulse wave database (PWDB) of 4,374 virtual pulse waves (ages 25–75), classical CAVI was computed via the Bramwell–Hill equation, and 11 non-invasive hemodynamic features per subject were used to train an RF regressor (100 trees; 63.2% bootstrap samples; 3 variables per split). We then used tree-averaged apred and bpred to calculate normalised CAVIDyn. We compared performance with classical CAVI and the gold standard carotid-femoral pulse wave velocity (cfPWV) using the metrics: R², root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). CAVI-ML reduced correlation with SBP by 59% (r = 0.613 → 0.251) and with diastolic pressure by 73% (r = 0.735 → 0.201), and was 62% less blood-pressure-dependent than cfPWV (r = 0.662). CAVI-ML improves independence from instantaneous blood pressure and shows promise for non-invasive arterial stiffness estimation and cardiovascular risk prediction, but requires validation on clinical data.
Internet of things-based smart irrigation system using soil moisture and weather data Zakarie Abdi Mohamud; Rozeha Binti A. Rashid; Yazid Abubakar Sufyan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27889

Abstract

Water wastage in agriculture remains a significant challenge due to irrigation practices that often rely on fixed schedules rather than actual field conditions. This study presents an internet of things (IoT)-based smart irrigation system designed to improve water-use efficiency through real-time monitoring and automated irrigation control. The system integrates a capacitive soil moisture sensor with weather information obtained from an online application programming interface (API), while all data processing is performed locally on a Raspberry Pi edge device. A rule-based decision mechanism is used to classify soil conditions into dry, optimal, and wet categories and to determine appropriate irrigation actions based on soil moisture levels and rainfall forecasts. The system was implemented using low-cost and readily available components and tested under controlled conditions with soil moisture levels ranging from approximately 0% to above 85%. Experimental results showed consistent classification of critical dry, optimal, and critical wet conditions, enabling appropriate irrigation responses under different scenarios. In addition, email notifications were generated only during critical conditions, while no alerts were triggered under optimal moisture levels, demonstrating stable and reliable operation. The proposed system provides a practical and cost-effective solution for supporting efficient irrigation management and sustainable agricultural practices.

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