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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
 311   312   313   314   315 
Insights on the internet of things: past, present, and future directions Tole Sutikno; Daniel Thalmann
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.v20i6.22028

Abstract

The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process’s ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.
Extended-state observer control with online payload identification for HRI in series elastic actuators Edwin Villarreal-López; Horacio Coral-Enriquez; Luini L. Hurtado-Cortés
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.v24i4.27787

Abstract

Safe human–robot interaction requires actuators that combine compliance with accurate force inference. Series elastic actuators (SEAs) are particularly suitable for this purpose; however, separating payload-induced torques from voluntary human interaction forces without dedicated sensors remains a critical challenge. This paper proposes a unified observer-based control framework for SEAs that integrates three key components: a linear parameter-varying extended state ob server (LPV-ESO), an online algebraic payload estimator, and a disturbance driven motion intention identification (MII) mechanism. The payload estimator continuously updates the observer gains to adapt to load variations, while the LPV-ESO decouples payload dynamics from human interaction forces. The dis turbance estimate is then processed through the MII block to infer operator intent in real time without additional sensing hardware. Numerical simulations under nominal conditions and ±10% parametric uncertainty confirm bounded trajec tory tracking error, rapid convergence of payload estimation, and effective sepa ration of payload-induced torques from voluntary interaction forces. The results demonstrate that the proposed framework achieves sensorless force decompo sition and reliable motion intention inference, offering a practical solution for SEA-based collaborative robots in rehabilitation, assistance, and industrial ap plications.
Hybrid optimization algorithm for resource-efficient and data-driven performance in agricultural IoT Depa Ramachandraiah Kumar Raja; Zuraida Abal Abas; Goshtu Hemanth Kumar; Chakana Ravindra Murthy; Venappagari Eswari
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.v23i1.26361

Abstract

The agricultural sector is undergoing a significant transformation with the adoption of the agricultural internet of things (IoT), yet it faces persistent challenges in optimizing resource efficiency and data-driven performance due to limitations in current optimization algorithms. This research assesses the effectiveness of four prominent algorithms such as ant colony optimization (ACO), genetic algorithms (GA), particle swarm optimization (PSO), and artificial bee colony (ABC) in addressing these challenges within agricultural IoT (AIoT). Introducing a novel hybrid optimization algorithm (HOA), we aim to overcome these limitations by prioritizing both resource efficiency and data-driven performance. Through a thorough evaluation, HOA demonstrates its superiority in enhancing both aspects, thereby establishing itself as a compelling solution for AIoT applications. The introduction of HOA sets the stage for sustainable, cost-effective, and data-driven precision agriculture, significantly enhancing resource efficiency and data accuracy within the IoT network.
Automatic channel selection using shuffled frog leaping algorithm for EEG based addiction detection Grace Mary Kanaga Edward; Angela Esther Rajakumar; Kumudha Raimond; Anitha Jeevanayagam
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.v21i5.23172

Abstract

Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
An adaptive neural control methodology design for dynamics mobile robot Khulood Eskander Dagher; Rabab Alaa Hameed; Ibrahim Amer Ibrahim; Muntaha Razak
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.v20i2.20923

Abstract

The paper demonstrates an enhancement in the mobile robot’s performance during trajectory tracking with static obstacles. An adaptive artificial neural network (ANN) control methodology with online tuning evolutionary slice genetic algorithm is used for the motion control of the nonlinear dynamics mobile robot system. This paper aims at locating the optimal path from the starting point to the target point and designing an ANN trajectory tracking control methodology. The algorithm is simulated with fixed-global environment obstacles to demonstate the effectiveness of the ANN controller and the evolutionary optimization algorithm in terms of the shortest path length generated and the minimum number of the evaluation cost function calculated. The simulation results illustrate that the ANN controller’s parameters are obtained quickly, generating smooth wheels’ torque actions for the mobile robot platform with a minimum cost function evolution that lead to minimize the tracking error to approximately zero with no oscillation in the responses.
RWT-Net: A hybrid ResNet-wavelet-transformer for early detection of left ventricular hypertrophy Hoang Huu To Nguyen; Phuong Huu Nghia Le; Lam Mai; Nguyen Pham Ho Trong
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.v24i1.27445

Abstract

Early detection of left ventricular hypertrophy (LVH), a key predictor of heart failure and stroke, is critical. However, standard 12-lead electrocardiogram (ECG) criteria suffer from low sensitivity. While deep learning shows promise, a research gap exists for models that robustly integrate diverse signal fea tures to improve detection, especially sensitivity. We propose ResNet-wavelet transformer net (RWT-Net), a hybrid architecture that fuses deep morpholog ical features from a ResNet1D with statistical time-frequency features from a wavelet packet transform (WPT) using a transformer encoder. The model was evaluated on the PTB-XL dataset (11,201 recordings) using a stringent, patient level 5-fold cross-validation. RWT-Net achieved a mean area under the curve (AUC)of0.9868andF1-scoreof0.8725. Critically, its wavelet-enhanced stream yielded significantly higher sensitivity compared to a ResNet-transformer base line (0.8964 vs. 0.8716, p=0.0039), better addressing the clinical need to mini mize false negatives. A key limitation is the reliance on ECG-based labels, not an echocardiography gold standard. RWT-Net demonstrates potential as a re liable, automated screening tool to prioritize at-risk patients for further clinical assessment.
Migration aspects from monolith to distributed systems using software code build and deployment time and latency perspective Alok Aggarwal; Vinay Singh
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25655

Abstract

The transition from an error-prone, slower, and extremely high-volume legacy system like monolithic system to a faster, lighter, and error-free microservices based system is not always so simple. Microservices are independently deployable and allow for a better team autonomy. In this work, several migration efforts to migrate from a legacy based monolithic system to a pure distributed microservices based system has been tested and deployed in keeping two DevOps principles, the software code build and deployment time and latency in monolithic and microservices. Some real-time projects are considered to measure the performance and the time taken to execute the experiments. To measure the total build and deployment time and latency, Jenkins, Prometheus, and JMeter are installed which are industry-recommended softwares. It is observed that there is a total of 7 seconds taken to build and deploy at containers for 10 microservices whereas 10 monolith applications took almost 260 seconds to be built and deployed to the application server. While increasing more requests per second it is observed that upto 3000 requests per second, it impacted the response time of monolith applications but microservices stays the same. The main conclusion is that microservices are rarely impacted in response time with respect to requests per second.
Comparison of the speedy estimate methods of the induction motors Thinh Cong Tran; Pavel Brandstetter; Hau Huu Vo; Chau Si Thien Dong; Martin Kuchar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 1: February 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i1.24089

Abstract

This paper deals with a novel method to achieve the effective performance of the extended Kalman filter (EKF) for the speedy estimate of an induction motor. The real coding genetic algorithm (GA) is used to optimize the components of the covariance matrix in the EKF, thus ensuring the stability and accuracy of the filter in the speed estimation. The advantage of the proposed method is less dependent on the parameters of the induction motor. The content includes the vector control model for induction motor, the speed estimation by modeling the reference frame-model reference adaptive system (RF-MRAS), the current based-model reference adaptive system (CB-MRAS), and the speed estimation with the EKF optimized by genetic algorithm. Simulative studies on the field-oriented controller (FOC) with different operating conditions are performed in Matlab Simulink when the rotor resistance changes in the current speed estimation methods. The simulation results demonstrate the efficiency of the proposed GA-EKF filter compared with other speed estimation methods of induction motors. 
Enhancing spam detection using Harris Hawks optimization algorithm Mosleh M. Abualhaj; Sumaya Nabil Alkhatib; Ahmad Adel Abu-Shareha; Adeeb M. Alsaaidah; Mohammed Anbar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26615

Abstract

This paper employs machine learning (ML) algorithms to identify and classify spam emails. The Harris Hawks optimization (HHO) algorithm can detect the crucial features that distinguish spam from ham emails. The HHO algorithm decreased the number of features in the ISCX-URL2016 spam dataset from 72 to 10. Implementing this will enhance the efficiency and cognitive acquisition of the ML algorithms. The decision tree (DT), Naive Bayes (NB), and AdaBoost algorithms are evaluated and contrasted to identify spam emails. The random search algorithm is used to optimize the significant hyperparameters of each algorithm for the specific task of spam identification. All three ML algorithms showed exceptional accuracy in detecting spam emails during the conducted testing. The DT algorithm attained a remarkable accuracy rate of 99.75%. The AdaBoost algorithm ranks second with an incredible accuracy of 99.67%. Finally, the NB algorithm attained an accuracy of 96.30%. The results demonstrate that the HHO algorithm shows promise in recognizing the crucial features of spam emails.
Outage and throughput performance of cognitive radio based power domain based multiple access Dinh-Thuan Do; Chi-Bao Le
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.v18i2.12731

Abstract

This paper considers power domain based multiple access (PDMA) in cognitive radio network to serve numerous users who intend to multiple access to core network. In particular, we investigate the effect of signal combination scheme equipped at PDMA end-users as existence of direct link and relay link. This system model using relay scheme provides performance improvement on the outage probability of two PDMA end-users. We first propose a simple scheme of fixed power allocation to PDMA users who exhibit performance gap and fairness. Inspired by PDMA strategy, we then find signal to noise ratio (SNR) to detect separated signal for each user. In addition, the exact expressions of outage probability are derived in assumption that receiver can cancel out the interference completely with successive interference cancellation (SIC). By exploiting theoretical and simulation results, both considered combination schemes (Maximal Ratio Combining (MRC) and Selection Combining (SC) can achieve improved performance of two PDMA users significantly.

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