cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Nizirwan Anwar
Contact Email
nizirwan.anwar@esaunggul.ac.id
Phone
-
Journal Mail Official
telkomnika@ee.uad.ac.id
Editorial Address
Ahmad Yani st. (Southern Ring Road), Tamanan, Banguntapan, Bantul, Yogyakarta 55191, Indonesia
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 27 Documents
 1   2   3 
Search results for , issue "vol 23, no 5: october 2025" : 27 Documents clear
Cost-effective long-range secure speech communication system for internet of things-enabled applications Samer Alabed; Mohammad Al-Rabayah; Bahaa Al-Sheikh; Lama Bou Farah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26884

Abstract

A new communication framework has been developed that allows voice transmission over long distances for internet of things (IoT) applications such as healthcare, smart cities, and remote monitoring in the least costly way and most secure manner. The system is based on long range (LoRa) technology and takes advantage of its spread spectrum technique, to provide long range transmission without the high-power requirements. The main limitation is LoRa’s bandwidth with a maximum throughput of 22 kbps for data. This presents a challenge for voice transmission communications. To address this shortened bandwidth issue, researchers developed an innovative compression solution that compresses voice data to less than 8 kbps to fit into LoRa’s capabilities. The compression allows for real practical voice communications and possibly can provide even greater distance than an uncompressed voice transmission update. The voice communications transmissions have cryptographic protection in place to protect the transmitted voice messages from unauthorized access.
Hybrid Kolmogorov-Arnold and convolutional neural network model for single-lead electrocardiogram classification Marlin Ramadhan Baidillah; Pratondo Busono; I Made Astawa; Syaeful Karim; Ronny Febryarto; I Putu Ananta Yogiswara; Chaerul Achmad; Nashrullah Taufik
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26735

Abstract

This study proposes a hybrid Kolmogorov-Arnold networks (KANs) and convolutional neural networks (CNN) to classify electrocardiogram (ECG) signal abnormalities in one lead ECG data of wearable telemedicine. The hybrid model combines CNN to extract hierarchical features from sequential data and KANs to model non-linear relationships with fewer parameters as an efficient classification. The study explores the model’s capacity to balance accuracy, computational efficiency, and memory usage as critical factors for real-time health monitoring in resource-constrained environments on the single-lead MIT-Beth Israel hospital (MIT-BIH) Supraventricular Arrhythmia database with five different class labels. For comparison, standalone CNN and KAN models were also trained on the same balanced dataset. The CNN model achieved an accuracy of 96.62%, precision of 96.81%, and recall of 96.53%. The KAN model, while computationally efficient, performed less effectively, with an accuracy of 94.15%, precision of 95.01%, and recall of 92.57%. In contrast, our hybrid KAN-CNN model outperformed both, attaining an accuracy of 97.53%, precision of 97.66%, recall of 97.40%, and a low loss of 0.0840. The study also explores the impact of quantization and compression on model performance, revealing that both CNN and Hybrid KAN-CNN models retained high accuracy post-quantization, whereas the KAN model exhibited a more significant drop in performance.
Water quality monitoring using soft computing techniques in Udupi Region, Karnataka, India Krishnamurthy Nayak; Sumukha K. Nayak; Supreetha Balavalikar Shivaram
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26228

Abstract

A monitoring of water quality index parameters using soft computing technology is the current research focus as the main challenge of which is to design a soft computing algorithm with the highest accuracy and less computation time. For the secondary dataset obtained by the government database, this research proposes a water quality prediction and classification method based on decision tree algorithm. The comparative analysis is made for the different highest accuracy algorithms like decision tree algorithm with support vector machine (SVM), k-nearest neighbour (KNN) classifier, linear discriminant analysis, Naïve Bayes classifier and logistic regression. Decision tree algorithm had the highest accuracy compared to other algorithms. The KNN algorithm used as clustering algorithm to plot the two classes good and bad. The trend analysis of the water quality is performed with various water quality parameters like pH, fluoride and total dissolved solids (TDS) test results are plotted and observed for the variations of the values with respect to increase in time. The performance is measured with statistical indices and the prediction accuracy of 0.99 and mean squared error of 0.05. The results prove that the KNN algorithm found to be better for clustering purposes.
A dual-band modified-rectangular patch with parasitic antenna for 2.4/5 GHz wireless local area network applications Suthasinee Lamultree; Sakolkorn Ungprasutr; Charinsak Saetiaw; Chuwong Phongcharoenpanich
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.27055

Abstract

This research presents the design and implementation of a dual-band patch antenna (DBPA) optimized for 2.4 GHz and 5 GHz wireless local area network (WLAN) applications. The antenna features a modified rectangular patch with a cut corner and two parasitic rectangular patches, enabling dual-band operation with enhanced gain. The DBPA is fed by a 50-Ohm coplanar waveguide and fabricated on a single-layer copper circuit board using a flame-retardant 4 substrate with a relative permittivity of 4.3 and a thickness of 1.6 mm. A prototype with compact dimensions of 0.040×0.040×0.0009 λ³ was constructed and experimentally evaluated. Measurements reveal a nearly omnidirectional radiation pattern, achieving peak gains of 2.92 dBi at 2.4 GHz and 4.25 dBi at 5 GHz. The antenna demonstrates a wide 10 dB return loss bandwidth of 67.7% (1.7–3.44 GHz) for the lower band and 56% (4.59–8.16 GHz) for the upper band. The strong agreement between simulated and measured results validates the design’s potential for practical and scalable implementation. This DBPA design offers a simpler, more compact, and wider-bandwidth alternative to conventional antennas, making it ideal for modern WLAN systems.
Enhanced torque control for horizontal-axis wind turbines via disturbance observer assistance Edwin Villarreal-Lopez; Horacio Coral-Enriquez; Sergio Tamayo-Leon
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26805

Abstract

This paper presents an enhanced control strategy for optimizing energy capture in horizontal axis wind turbines operating in the partial-load region (region 2). The proposed approach builds upon conventional standard torque control (STC) by incorporating a generalized extended state observer (GESO) that follows the active-disturbance-rejection paradigm. Although traditional torque control methods have proven effective under steady wind conditions, they often lack robustness against disturbances, system faults, and model uncertainties inherent in wind energy systems. The proposed observer-assisted control scheme addresses these limitations by estimating and compensating for total disturbance signals, including non-modeled dynamics, parameter uncertainties, and actuator faults. The effectiveness of the proposed control strategy is validated through comprehensive simulations using a 5 MW wind turbine model subjected to realistic operational conditions. Simulation scenarios include turbulent wind speed profiles and actuator degradation to assess controller performance. The results demonstrate improved robustness and energy capture efficiency compared to the conventional control approach, while maintaining the simplicity of the implementation. This work contributes to the development of more reliable wind energy conversion systems (WECSs) by offering a practical solution that improves both performance and fault tolerance in partial load operation.
Design and implementation of a power supply unit for a smart airport lighting control system Amine Derraa; Najat Ouaaline; Boujemaa Nassiri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26690

Abstract

In this paper, a buck-boost converter is used to design and implement a power supply for intelligent airport lighting system applications. Innovative approaches to power supply design are required to meet the increasing demand for fault detection solutions for lighting systems in vital infrastructure such as airports. The buck-boost converter’s ability to step up or down input voltage levels makes it particularly well suited to this application, ensuring stable operation over a range of load conditions. With a fast-settling time of 26 ms at 6.1 V input and dropping to 6 ms at 22.4 V input, the power supply offers exceptional output stability. The output stabilizes steadily at 5 V with low ripple over a wide input voltage range (5 V to 23 V). The physical prototype, simulations, component selection and circuit design are all carefully tested and supported by experimental results. According to these results, the proposed converter-based power unit operates with stability and reliability, making it ideal for demanding lighting applications. By improving power stability in dynamic environments, this work improves the reliability of aviation infrastructure power systems and lays the groundwork for future advances in intelligent airport technologies.
Deep transfer learning based disease detection and classification of tomato leaves - a comparative analysis Munira Akter Lata; Marjia Sultana; Iffat Ara Badhan; Mastura Jahan Maria; Fariha Tasnim Nuha
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26887

Abstract

A wide variety of diseases have a significant impact on tomato plants. To avoid crop quality issues, a prompt and precise diagnosis is crucial. Classifying plant diseases is one of the numerous applications where deep transfer learning models have recently produced remarkable results. This study dealt with fine-tuning by contrasting the most advanced architectures, including Inception V3, ResNet-18, ResNet-50, VGG-16, VGG-19, GoogLeNet, and AlexNet. In the end, a comparison evaluation is conducted. Nine distinct tomato disease classes and one healthy class from PlantVillage make up the dataset used in this study. Precision, recall, F1-score, and accuracy were the basis for a multiclass statistical analysis that assessed the models. The ResNet-50 approach yielded significant results with precision: 82%, recall: 81%, F1-score: 81%, and accuracy: 85%. With this high success rate, it is reasonable to say that mobile applications or IoT-compatible gadgets implemented with the ResNet-50 model can assist farmers in identifying and safeguarding tomatoes against the aforementioned diseases.
Optimized human detection in NLOS scenarios using hybrid dimensionality reduction and SVM with UWB signals Enoch Adama Jiya; Ilesanmi Banjo Oluwafemi; Emmanuel Sunday Akin Ajisegiri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26738

Abstract

Trapped victim localization in search and rescue (SAR) operations is especially difficult in non-line-of-sight (NLOS) conditions, where traditional techniques fail due to debris and signal distortion. Ultra-wideband (UWB) NLOS signal datasets offer a promising alternative but are often high-dimensional and noisy. This study proposes an optimized dimensionality reduction framework combining an adaptive human presence detector (AHPD) with genetic algorithms (GA) and independent component analysis (ICA), followed by support vector machine (SVM) classification. The approach is tested on a public NLOS dataset comprising 23,522 dynamic instances, each with 256 signal samples per attribute, simulating complex SAR scenarios including rubble and dynamic obstacles. The results indicate that the AHPD+GA+SVM model reached an accuracy of 85.78%, sensitivity of 80.00%, and specificity of 96.46%, which is better than the AHPD+ICA +SVM model that had an accuracy of 79.20%, sensitivity of 73.07%, and specificity of 81.05%. These findings demonstrate the framework’s robustness and scalability, making it a strong candidate for real-time human detection in disaster recovery missions.
Elevating cultural understanding: interactive museum exploration using 3D AR and MDLC framework Edy Jogatama Purhita; Eko Sediyono; Ade Iriani
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26343

Abstract

Limited access to information and interaction with artifacts in museums often hinders visitors from gaining a deeper understanding of the culture and historical context presented. This study addresses this challenge by developing a three-dimensional (3D) augmented reality (AR)-based interactive museum that enhances the museum visitor experience through an intuitive user interface (UI) and enriched content related to the exhibited artifacts. This study explores the potential of 3D AR technology in enhancing visitor engagement and interaction with museum exhibits, providing a more immersive and informative experience. This study uses the multimedia development life cycle (MDLC) as a framework to develop a 3D AR-based interactive museum. By applying the MDLC approach, this study integrates advanced AR technology with comprehensive and detailed content, resulting in a structured and user-centered interactive platform. Key benefits of this approach include enhanced interactivity, enriched artifact information, and an intuitive interface that facilitates easier access to museum content. The findings indicate that the developed interactive museum successfully overcomes the barriers of limited accessibility of information and interaction with artifacts. Through the application of advanced AR technology, the museum visitor experience is significantly enhanced, making the museum more inclusive, interactive, and educative for visitors.
The comparison of underwater source localization between Riemannian MFP and blind channel equalizer Tran Cao Quyen; Tran Linh Huong Giang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.27115

Abstract

Blind channel equalization (BCE) has been widely used in underwater communications due to its strong robustness against multipath propagation and its suitability for rapidly varying environments. However, there has been little research on the application of BCE for underwater source localization. On the other hand, conventional matched field processing (MFP), and particularly Riemannian MFP (RMFP), have been regarded as highly effective for this problem. In this paper, based on the statistical characterization of the signal-to-noise ratio (SNR) in underwater acoustic channels, we propose a method for estimating the channel transfer function, which is then used to construct a blind channel equalizer. A source localization approach using the proposed BCE is also presented. The localization performance using BCE is comparable to that of RMFP, achieving a depth error of 10 meters and a range error of 100 meters, while requiring significantly lower computational complexity.

Page 1 of 3 | Total Record : 27

 1   2   3 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 24, No 5: October 2026 Vol 24, No 4: August 2026 Vol 24, No 3: June 2026 Vol 24, No 2: April 2026 Vol 24, No 1: February 2026 Vol 23, No 6: December 2025 Vol 23, No 5: October 2025 Vol 23, No 4: August 2025 Vol 23, No 3: June 2025 Vol 23, No 2: April 2025 Vol 23, No 1: February 2025 Vol 22, No 6: December 2024 Vol 22, No 5: October 2024 Vol 22, No 4: August 2024 Vol 22, No 3: June 2024 Vol 22, No 2: April 2024 Vol 22, No 1: February 2024 Vol 21, No 6: December 2023 Vol 21, No 5: October 2023 Vol 21, No 4: August 2023 Vol 21, No 3: June 2023 Vol 21, No 2: April 2023 Vol 21, No 1: February 2023 Vol 20, No 6: December 2022 Vol 20, No 5: October 2022 Vol 20, No 4: August 2022 Vol 20, No 3: June 2022 Vol 20, No 2: April 2022 Vol 20, No 1: February 2022 Vol 19, No 6: December 2021 Vol 19, No 5: October 2021 Vol 19, No 4: August 2021 Vol 19, No 3: June 2021 Vol 19, No 2: April 2021 Vol 19, No 1: February 2021 Vol 18, No 6: December 2020 Vol 18, No 5: October 2020 Vol 18, No 4: August 2020 Vol 18, No 3: June 2020 Vol 18, No 2: April 2020 Vol 18, No 1: February 2020 Vol 17, No 6: December 2019 Vol 17, No 5: October 2019 Vol 17, No 4: August 2019 Vol 17, No 3: June 2019 Vol 17, No 2: April 2019 Vol 17, No 1: February 2019 Vol 16, No 6: December 2018 Vol 16, No 5: October 2018 Vol 16, No 4: August 2018 Vol 16, No 3: June 2018 Vol 16, No 2: April 2018 Vol 16, No 1: February 2018 Vol 15, No 4: December 2017 Vol 15, No 3: September 2017 Vol 15, No 2: June 2017 Vol 15, No 1: March 2017 Vol 14, No 4: December 2016 Vol 14, No 3: September 2016 Vol 14, No 2: June 2016 Vol 14, No 1: March 2016 Vol 13, No 4: December 2015 Vol 13, No 3: September 2015 Vol 13, No 2: June 2015 Vol 13, No 1: March 2015 Vol 12, No 4: December 2014 Vol 12, No 3: September 2014 Vol 12, No 2: June 2014 Vol 12, No 1: March 2014 Vol 11, No 4: December 2013 Vol 11, No 3: September 2013 Vol 11, No 2: June 2013 Vol 11, No 1: March 2013 Vol 10, No 4: December 2012 Vol 10, No 3: September 2012 Vol 10, No 2: June 2012 Vol 10, No 1: March 2012 Vol 9, No 3: December 2011 Vol 9, No 2: August 2011 Vol 9, No 1: April 2011 Vol 8, No 3: December 2010 Vol 8, No 2: August 2010 Vol 8, No 1: April 2010 Vol 7, No 3: December 2009 Vol 7, No 2: August 2009 Vol 7, No 1: April 2009 Vol 6, No 3: December 2008 Vol 6, No 2: August 2008 Vol 6, No 1: April 2008 Vol 5, No 3: December 2007 Vol 5, No 2: August 2007 Vol 5, No 1: April 2007 Vol 4, No 3: December 2006 Vol 4, No 2: August 2006 Vol 4, No 1: April 2006 Vol 3, No 3: December 2005 Vol 3, No 2: August 2005 Vol 3, No 1: April 2005 Vol 2, No 1: April 2004 More Issue