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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 3,452 Documents
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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.
Physical security with power beacon assisted in half-duplex relaying networks over Rayleigh fading channel: performance analysis Phu Tran Tin; Duy-Hung Ha; Luu Gia Thien; Tran Thanh Trang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.15857

Abstract

In this research, we proposed and investigated physical security with power beacon assisted in half-duplex relaying networks over a Rayleigh fading channel. In this model, the source (S) node communicates with the destination (D) node via the helping of the intermediate relay (R) node. The D and R nodes harvest energy from the power beacon (PB) node in the presence of a passive eavesdropper (E) node. Then we derived the integral form of the system outage probability (OP) and closed form of the intercept probability (IP). The correctness of the analytical of the OP and IP is verified by the Monte Carlo simulation. The influence of the main system parameters on the OP and IP also is investigated. The research results indicated that the analytical results are the same as the simulation ones.
Quantum binary particle swarm optimization for optimal on-load tap changing and power loss reduction Aji Akbar Firdaus; Irrine Budi Sulistiawati; Vicky Andria Kusuma; Dimas Fajar Uman Putra; Hamzah Arof; Novian Patria Uman Putra; Sena Sukmananda Suprapto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.25744

Abstract

Over time, there has been a continuous surge in the demand for electrical energy, necessitating the development of larger and more intricate electrical power networks. These extensive networks pose a significant challenge, primarily in the form of considerable loss of electrical energy, which, if not effectively addressed, may lead to persistent and imperceptible losses. In response to this challenge, this research proposes the application of quantum binary particle swarm optimization (QBPSO) for the coordinated management of on-load tap changers (OLTC) in loaded transformers within a distribution network, with a specific emphasis on reducing power losses. The experimental results demonstrate that the implementation of QBPSO results in a reduction of power loss from 21.756107 kW to 19.157321 kW and an increase in the average voltage from 19.00467941 kV to 19.93068 kV in a 20 kV 34-bus distribution network. This has the potential to significantly enhance overall system efficiency.
Comparative performance analysis of convolutional neural network-architectures on coffee-bean roast classification Irfan Asfy Fakhry Anto; Jony Winaryo Wibowo; Aris Munandar; Taufik Ibnu Salim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.27090

Abstract

The classification of coffee bean roast levels using Agtron standards has evolved from traditional subjective methods to technology-driven approaches employing advanced artificial intelligence. Recent advancements in computer vision have demonstrated the capability of convolutional neural networks (CNNs) in providing objective and consistent roast level classification compared to human visual assessment, which is prone to variability and subjectivity. This research presents a performance analysis of five CNN architectures (AlexNet, ResNet, MobileNet, VGGNet, and DenseNet) for classifying coffee beans into eight distinct Agtron roast levels. The comprehensive methodology encompasses four phases: i) data acquisition, ii) image preprocessing, iii) model training and validation, and iv) evaluation metric. During training-validation, DenseNet outperformed other models, achieving 99.702% training accuracy and 77.68% validation accuracy. In the testing evaluation, DenseNet also led with an average testing accuracy of 93.8%, followed by ResNet at 92.6%, VGGNet and AlexNet both at 92.4%, and MobileNet at 89.7%. The results show that the DenseNet shows promise in classifying Agtron coffee-bean roast classification.
A comparative study of mango fruit pest and disease recognition Kusrini Kusrini; Suputa Suputa; Arief Setyanto; I Made Artha Agastya; Herlambang Priantoro; Sofyan Pariyasto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 6: December 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i6.21783

Abstract

Mango is a popular fruit for local consumption and export commodity. Currently, Indonesian mango export at 37.8 M accounted for 0.115% of world consumption. Pest and disease are the common enemies of mango that degrade the quality of mango yield. Specialized treatment in export destinations such as gamma-ray in Australia, or hot water treatment in Korea, demands pest-free and high-quality products. Artificial intelligence helps to improve mango pest and disease control. This paper compares the deep learning model on mango fruit pests and disease recognition. This research compares Visual Geometry Group 16 (VGG16), residual neural network 50 (ResNet50), InceptionResNet-V2, Inception-V3, and DenseNet architectures to identify pests and diseases on mango fruit. We implement transfer learning, adopt all pre-trained weight parameters from all those architectures, and replace the final layer to adjust the output. All the architectures are re-train and validated using our dataset. The tropical mango dataset is collected and labeled by a subject matter expert. The VGG16 model achieves the top validation and testing accuracy at 89% and 90%, respectively. VGG16 is the shallowest model, with 16 layers; therefore, the model was the smallest size. The testing time is superior to the rest of the experiment at 2 seconds for 130 testing images.
Design and simulation of a compact patch antenna for breast cancer tumors detection in the Wi-Fi band Salima Azzaz-Rahmani; Hadj Zerrouki
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27781

Abstract

The escalating need for safe, low-cost, and reliable medical diagnostic tools has propelled research into microwave imaging (MWI) for breast cancer detection. This study aims to design and evaluate, via simulation, a compact microstrip patch antenna operating in the 2.4 GHz wireless fidelity (Wi-Fi) band as a preliminary sensing element. The antenna, with overall dimensions of (14×14×3.1) mm³, is designed on a Rogers RT/duroid 5880 substrate and superstrate (ε_r= 2.2). To evaluate its detection capabilities, the antenna is placed over a simplified three-layer human breast phantom (skin, fat, and fibro-glandular tissue). Using Ansys high frequency structure simulator (HFSS) software, three scenarios were simulated: a healthy breast, a breast with a single tumor, and a breast with two tumors. The simulation results indicate measurable shifts in electromagnetic parameters used as diagnostic indicators. The presence of tumors caused a shift in the resonant frequency (from 2.42 GHz to 2.46 GHz) and a variation in the reflection coefficient (S11) magnitude. Specifically, the antenna demonstrates high sensitivity to dielectric loading changes, identified by a performance framework that correlates frequency shifts to tumor presence, while maintaining specific absorption rate (SAR) values within safety limits. This work demonstrates, through simulation, the feasibility of using a compact Wi-Fi band patch antenna for breast tumor detection, providing a foundation for future experimental development of portable diagnostic systems.
Designing common-source low noise amplifier utilizing GaN HEMT for sub-6 GHz in 5G wireless applications Samia Zarrik; Abdelhak Bendali; Fatehi ALtalqi; Karima Benkhadda; Sanae Habibi; Zahra Sahel; Mouad El Kobbi; Abdelkader Hadjoudja; Mohamed Habibi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.26324

Abstract

In the domain of gallium nitride based high electron mobility transistors (GaN HEMT), this work refines a class A low noise amplifier (LNA) tailored for fifth generation (5G) wireless applications within the sub-6 GHz band. Employing a common-source topology and leveraging GaN HEMT technology, the amplifier seamlessly achieves operation at 3.5 GHz. Simulations were conducted using Advanced Design System (ADS) software. The GaN HEMT transistor manifests noteworthy intrinsic and extrinsic characteristics, with a Vds of 6 V, Vgs of -1.56 V, and Id of 1024 mA. Through meticulous optimization within the [3.3-3.9] GHz frequency band, the GaN HEMT transistor attains an impressive maximum gain of 16.225 dB, coupled with a minimal low noise figure (NF) of 1.232 dB. Additionally, the amplifier showcases noteworthy power added efficiency (PAE) of approximately 60.527%. These exceptional attributes position the amplifier as highly suitable for sub-6 GHz and millimeter-wave applications across the extensive 5G spectrum. The investigation is centered on precisely situating the LNA as a pivotal catalyst for improving 5G network front-end performance. With a dedicated focus on frequencies below 6 GHz, the research not only addresses challenges but also pioneers’ advancements in 5G application LNA design, ultimately elevating the overall system performance.
Adaptive segmentation algorithm based on level set model in medical imaging Boualem Mansouri; Abdelkader Khobzaoui; Mehdi Damou; Mohammed Chetioui; Abdelhakim Boudkhil
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.22365

Abstract

For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Monitoring and resource management taxonomy in interconnected cloud infrastructures: a survey Vingi Patrick Nzanzu; Emmanuel Adetiba; Joke Atinuke Badejo; Mbasa Joaquim Molo; Claude Takenga; Etinosa Noma-Osaghae; Victoria Oguntosin; Sadeeq Suraju
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 2: April 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i2.20503

Abstract

Cloud users have recently expanded dramatically. The cloud service providers (CSPs) have also increased and have therefore made their infrastructure more complex. The complex infrastructure needs to be distributed appropriately to various users. Also, the advances in cloud computing have led to the development of interconnected cloud computing environments (ICCEs). For instance, ICCEs include the cloud hybrid, intercloud, multi-cloud, and federated clouds. However, the sharing of resources is not facilitated by specific proprietary technologies and access interfaces used by CSPs. Several CSPs provide similar services but have different access patterns. Data from various CSPs must be obtained and processed by cloud users. To ensure that all ICCE tenants (users and CSPs) benefit from the best CSPs, efficient resource management was suggested. Besides, it is pertinent that cloud resources be monitored regularly. Cloud monitoring is a service that works as a third-party entity between customers and CSPs. This paper discusses a complete cloud monitoring survey in ICCE, focusing on cloud monitoring and its significance. Several current open-source monitoring solutions are discussed. A taxonomy is presented and analyzed for cloud resource management. This taxonomy includes resource pricing, assignment of resources, exploration of resources, collection of resources, and disaster management.
Distorted born iterative method reconstruction in high-noise environments using KNN-based machine learning denoising Nguyen Quang Huy; Nguyen Truong Thang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 1: February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i1.27401

Abstract

Ultrasound tomography reconstruction using the distorted born iterative method (DBIM) is sensitive to measurement noise, which degrades image fidelity and slows convergence. We propose integrating a k-nearest neighbors (KNN) denoising step within each DBIM iteration to suppress noise adaptively while preserving structural edges. Simulations with a circular cylindrical target and transmit/receive geometry (12×12) were conducted at signal-to-noise ratio (SNR) levels of 6 dB, 3 dB, and 1 dB. Compared with conventional DBIM employing Tikhonov regularization, the KNN-filtered DBIM reduces normalized reconstruction error by up to 57.2% at 1 dB and shows faster error decay over successive iterations. The method is training-free, computationally lightweight, and preserves fine structural details. These properties make KNN-filtered DBIM attractive for noisy or resource-constrained imaging environments. Future work will validate the approach on experimental data and explore adaptive K selection.

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