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30,376 Article Results

Performance enhancement of embedded object detection via neural hardware acceleration

10.12928/telkomnika.v24i1.27448
Alwin; STMIK Indonesia Mandiri Hartono Limaran , Agung; STMIK Indonesia Mandiri Wicaksono , Patah; STMIK Indonesia Mandiri Herwanto
This paper presents the first benchmarking of you only look once version 11 (YOLO11) on the Rockchip RK3566 neural processing unit (NPU) within the Orange Pi 3B platform. Performance was compared between the quad-core ARM Cortex-A55 CPU and the integrated NPU using the COCO2017 dataset, evaluating latency, energy, and accuracy. NPU acceleration achieved >80% latency reduction and ≈ 94% lower per-inference energy consumption, with speedup of up to 16.7× while maintaining accuracy within 0.03 mean average precision (mAP) of the baseline. Average power remained nearly constant (3.60 W central processing unit (CPU) vs. 3.59 W NPU), indicating that the efficiency gains stem from reduced inference time rather than lower wattage. Limitations included unstable INT8 quantization due to unsupported operators and calibration-range mismatch, as well as minor CPU-side overhead in preprocessing and non-maximum suppression. The findings confirm that the RK3566 NPU delivers substantial efficiency gains without accuracy loss, enabling compact and low-cost platforms to sustain modern object-detection workloads. This demonstrates that affordable NPUs can provide reliable, real time artificial intelligence (AI) inference for embedded vision, internet of things (IoT), and robotics applications.
Volume: 24
Issue: 1
Page: 126-141
Publish at: 2026-02-01

Newchaos function from the composition of DTM and Gauss iterated map for digital image encryption

10.12928/telkomnika.v24i1.27551
Adrianus; Universitas Indonesia Yosia , Tokonyai Tawanda Jonathan; Universitas Indonesia Rabvemhiri , Suryadi; Universitas Indonesia MT
This manuscript introduces a novel chaotic discrete function, formulated through the composition of the dyadic transformation map (DTM) and the Gauss iterated map (GIM), and designated as DTGIM. The National Institute of Science and Technology (NIST) randomness test suite, bifurcation diagrams, and Lyapunov exponents are used to examine the chaotic characteristics of DTGIM. With ini tial condition x0 = 0.12345 and parameters α = −15 and β = 0.3, the func tion shows chaotic behavior in the bifurcation diagram and produces a positive Lyapunov exponent. Strong randomness is further confirmed by NIST tests, which achieve 100% for 32-bit binary sequences and 63.75% for 8-bit binary sequences. Additionally, we compare a number other chaotic discrete functions that also employ the composition method. These findings show that DTGIM is a viable option for applications involving chaos-based cryptography.
Volume: 24
Issue: 1
Page: 228-239
Publish at: 2026-02-01

Optimizing blood cell classification: evaluating feature dimensionality and validation strategies

10.12928/telkomnika.v24i1.27269
Ruaa H. Ali; Northern Technical University Al-Mallah , Marwa Mawfaq; Northern Technical University Mohamedsheet Al-Hatab , Maysaloon; Northern Technical University Abed Qasim
Manual blood cell classification is time consuming and may lead to inconsistent results. This study aims to assist pathologists in diagnosing hematological disorders using machine learning (ML) techniques for automated classification of blood cells in multi-color test images, distinguishing red blood cells (RBCs) and white blood cells (WBCs). Features were extracted using the InceptionV3 network, and several ML models were evaluated for classifying blood cells into eight categories. Two validation strategies: a 66%–34% train–test split and 20-fold cross-validation were applied. The effect of dimensionality reduction through principal component analysis (PCA) was also examined, reducing the feature space from 2,048 to 100 components. Among all models, support vector machine (SVM) achieved highest performance, with 93.4% accuracy and an area under the curve (AUC) of 0.996 without PCA, and 90.1% accuracy with an AUC of 0.991 after PCA. Although PCA slightly reduced accuracy, it improved computational efficiency. Overall, SVM provided the most accurate, stable, and generalizable classification results for automated blood cell analysis.
Volume: 24
Issue: 1
Page: 359-370
Publish at: 2026-02-01

Information technology value engineering through partial adjustment valuation theory

10.12928/telkomnika.v24i1.27478
Lukman; Telkom University Abdurrahman , Candiwan; Telkom University Candiwan
The paper proposes a systems management approach that utilizes information technology (IT) treatment as a framework to help firms enhance future performance by optimising key parameters. The method certifies a valuation approach that enables businesses to better manage their IT infrastructure and improve performance. A case study of A case study of PT Telekomunikasi Indonesia (Telkom) and PT XL Axiata (XL) (2004–2018) shows the method’s effectiveness. Once the IT value is identified, specific parameters can be engineered to improve performance without changing other variables. The approach uses a partial adjustment valuation model, enabling performance gains at lower costs. The results show significant improvements in both firms’ performance values and ratios compared to their originals. This supports adopting a cost leadership strategy, making IT based businesses more efficient, cost-effective, and better performing across financial, business, and strategic dimensions.
Volume: 24
Issue: 1
Page: 111-125
Publish at: 2026-02-01

Performance analysis of a multi-level inverter fed permanent magnet synchronous motor for electric vehicles

10.12928/telkomnika.v24i1.27234
Donepudi; Aditya University Tata Rao , Bhimaraju; Aditya University Pemmanaboidi Srihari Datta , Uma Phanendra; Aditya University Kumar Chaturvedula , Kondala; Aditya University Rao Parasa , Mummidi Parvateeswara; Aditya University Subba Raju
Electric vehicle (EV) drive systems utilizing permanent magnet synchronous motors (PMSMs) often encounter performance limitations due to switching losses, voltage stress, and harmonic distortion. To address these challenges, this paper presents a compact 31-level multilevel inverter (MLI) topology designed to enhance drive efficiency and power quality. The proposed inverter minimizes switching devices and driver circuits, resulting in reduced total harmonic distortion (THD), lower voltage stress, and improved waveform fidelity. Advanced control strategies are employed to further optimize performance. field-oriented control (FOC) ensures precise torque and flux regulation, while direct torque control (DTC) delivers rapid transient response. To mitigate torque ripple and variable switching frequency inherent in conventional DTC, adaptive predictive control (APC) is integrated to refine switching behavior and enhance dynamic stability. Simulation studies conducted in MATLAB/Simulink demonstrate the effectiveness of the proposed system, revealing significant improvements in torque smoothness, reduced THD (0.85%) and elevated efficiency under variable load conditions. This integrated solution offers a practical and scalable approach for next-generation EVs, contributing to greater reliability, energy utilization, and overall system performance.
Volume: 24
Issue: 1
Page: 302-312
Publish at: 2026-02-01

Secure hybrid power-frequency multiple access in satellite terrestrial communication systems: a performance study

10.12928/telkomnika.v24i1.26892
Huu; Industrial University of Ho Chi Minh City Q. Tran , Viet-Thanh; Industrial University of Ho Chi Minh City Pham
This paper investigates a secure hybrid power–frequency multiple access (PFMA) framework for satellite–terrestrial communications. By integrating power- and frequency-domain multiplexing, PFMA achieves approximately 4 dB lower transmit signal-to-noise ratio (SNR) than non-orthogonal multiple access (NOMA) for the same connection outage probability (COP) at SNR > 0 dB, and it reduces the COP by up to 30% at low-to-medium SNRs. It further decreases the intercept probability (IP) by 20–25% at PS = 10 dBm. Closed-form COP and IP expressions are derived under shadowed-Rician fad ing with both internal and external eavesdroppers and validated via Monte Carlo simulations. Parameter analysis indicates that PFMA’s SNR gain can either ex tend coverage by 60% or save 37% energy, providing design guidelines for 6G, satellite IoT, and emergency communication systems. The single-cell assump tion points to future work on multi-cell and mobility scenarios.
Volume: 24
Issue: 1
Page: 14-21
Publish at: 2026-02-01

Anchovy-inspired filter algorithm: A bio-inspired optimization approach for high-dimensional benchmark functions

10.12928/telkomnika.v24i1.27594
Azrul; Politeknik Sultan Idris Shah Mahfurdz , Muhammad Muizz; Politeknik Sultan Idris Shah Mohd Nawawi , Sunardi; Universitas Ahmad Dahlan Sunardi , Mohd Azriq; Sapura Industrial Berhad, Bandar Baru Bangi Abd Aziz
This paper presents the anchovy-inspired filter algorithm (AFA), a novel bio-inspired metaheuristic optimization method motivated by the filter feeding behavior of anchovies. Unlike conventional swarm intelligence algorithms, AFA employs a filtering mechanism in which each agent generates multiple candidate solutions within a local sampling radius and selects the best, mimicking how anchovies filter microscopic prey from seawater. To evaluate its performance, AFA was benchmarked against particle swarm optimization (PSO) and genetic algorithm (GA) using six standard test functions: Sphere, Rosenbrock, Schwefel 1.2, Rastrigin, Griewank, and Ackley in 30-dimensional search spaces. Simulation results demonstrate that AFA consistently outperforms PSO and GA across unimodal and multimodal functions. For unimodal problems such as Sphere, Rosenbrock, and Schwefel 1.2, AFA achieved significantly lower best and mean fitness values, reflecting strong exploitation capability. For multimodal functions including Rastrigin, Griewank, and Ackley, AFA effectively avoided local minima, maintained robustness, and achieved stable convergence with lower variance. Convergence analysis further indicates that AFA steadily approaches near-global optima without premature stagnation. Overall, the results highlight the effectiveness of the filter-based exploitation mechanism in balancing exploration and exploitation. Future research will focus on adaptive filtering strategies, hybrid integration with other metaheuristics, and applications to real-world optimization problems.
Volume: 24
Issue: 1
Page: 271-281
Publish at: 2026-02-01

The role of digital technologies in the transformation of ethical norms in the educational process

10.11591/ijere.v15i1.32497
ZuoYuan Liu , Alena Gura , Olga Pavlovskaya , Nataliya Antonova
In contemporary education, which increasingly incorporates digital technologies, the issue of adhering to ethical norms by both educators and students has gained particular relevance. This study aims to examine the impact of digital technologies on the transformation of ethical standards within the educational process. A survey was conducted among 45 educators and 345 students from three universities before and after the transition to remote learning, to assess changes in the adherence to ethical standards. The results revealed that after the implementation of remote learning, there was a significant increase in the level of adherence to ethical norms among educators (up to 98%) and students (up to 91%). Additionally, there was an improvement in academic performance, with 46% of students achieving a high level of success following the transition to remote learning. The evaluation of the impact of digital technologies on ethical transformation was found to be moderate but positive. Thus, digital technologies can serve as an effective tool for enhancing ethical standards and improving educational outcomes, particularly in the context of remote learning. These findings underscore the importance of integrating digital technologies into the educational process as a means of supporting ethical culture.
Volume: 15
Issue: 1
Page: 943-954
Publish at: 2026-02-01

Deep learning-based power amplifier linearization in OFDM systems with unknown channel state information

10.12928/telkomnika.v24i1.27236
Meryem Mamia; University of Tlemcen Benosman , Mohammed Yassine; University of Tlemcen Bendimerad , Fethi Tarik; University of Tlemcen Bendimerad
This paper presents an end-to-end deep learning-based approach for orthogonal frequency-division multiplexing (OFDM) communication systems impaired by nonlinear power amplifiers (PAs) and channel fading. The PA nonlinearity is modeled using the modified Rapp model, and simulations are performed on a 64-subcarrier OFDM system with a cyclic prefix (CP) of 8 and 16-quadrature amplitude modulation (16-QAM). The proposed autoencoder-based OFDM–PA (AE-OFDM-PA) system jointly optimizes the transmitter and receiver through end-to-end learning, enabling simultaneous compensation of both PA nonlinearities and channel distortions without requiring explicit channel state information (CSI) estimation. Instead, the model leverages embedded pilot sequences to learn the implicit CSI representation directly from data, allowing the receiver to correct amplitude and phase distortions adaptively. Simulation results demonstrate that AE-OFDM-PA significantly outperforms conventional OFDM and OFDM-PA systems, achieving over 70× block error rate (BLER) improvement compared with the uncompensated OFDM-PA system at an input back-off (IBO) of 3 dB. Furthermore, the proposed method achieves approximately 11.5 dB adjacent channel leakage ratio (ACLR) improvement over the classical memory polynomial digital predistortion (DPD) technique, while slightly reducing the peak-to-average power ratio (PAPR). Overall, AE-OFDM-PA provides a robust, spectrally efficient, and low-complexity solution for nonlinear and fading environments with unknown or varying CSI.
Volume: 24
Issue: 1
Page: 1-13
Publish at: 2026-02-01

Advancements in physical layer key generation: a review on channel reciprocity and IoT security techniques

10.12928/telkomnika.v24i1.27340
Syed Shafaq; Southeast University Ali Shah , Ajab; University of Science and Technology Bannu Noor , Ruiyue; Changchun University of Science and Technology Liang , Rahmat Ullah; FAST National University of Computer and Emerging Science Zadran
With the burgeoning internet of things (IoT), securing communication becomes paramount. Traditional cryptography does not meet computational needs and brute-force attacks. This review explores the state-of-the-art physical layer secret key generation (PLKG) that takes advantage of the inherent reciprocity and randomness of wireless channels. We investigate cutting-edge techniques such as feature extraction networks, domain adversarial training, and deep learning-based approaches, evaluating their effects on the security and efficiency of key generation. In addition to these methods, the review addresses real-world challenges such as multi-user scenarios, reconciliation overhead, and inconsistent channel measurement. We believe that improved key generation rates and security can be achieved through the use of millimeter wave technology and full-duplex communication. To strengthen the robustness of key generation, the paper concludes by suggesting future directions, such as incorporating more random sources, such as physiological signals and sensor data. This comprehensive overview offers deep insights into the state-of-the-art and paves the way for reliable communication in ever more complicated IoT settings.
Volume: 24
Issue: 1
Page: 196-205
Publish at: 2026-02-01

Optimized edge-aware frequency-guided filtering for robust image denoising

10.12928/telkomnika.v24i1.27338
Iman; Karabuk University Elawady , Ahmet Reşit; Karabuk University Kavsaoğlu , İsmail Rakıp; Karabuk University Karaş
The problem of denoising intrusion is still of great concern in computational imaging because of the trade-off between noise reduction and image structure and details recovery. This paper proposes an optimized edge-aware fast adaptive guided filter (E-FAGF) combining wavelet-domain decomposition, edge-awareness, and lightweight deep learning for efficient and effective denoising. The biorthogonal wavelet transform is employed to decompose noisy images into low- and high-frequency sub bands and an improved edge-attention map for selective high-frequency denoising. Regularization parameters are estimated pixel-wise by a compact convolutional neural network (CNN), allowing spatial-varying filtering to be done with multi-scale processing. The resultant E-FAGF consistently outperforms the state of the art on this dataset: on BSD500 for speckle and Gaussian noise (peak signal-to-noise ratio (PSNR) of 39.63 dB and 33.97 dB, respectively), and competitive performance for Poisson noise (30.84 dB) a large margin compared to the reference bilateral and non-local means. Our method maintains high structural similarity (up to 0.97 in structural similarity index measure (SSIM)), runs at 0.015 seconds per 512×512 image on graphics processing unit (GPU), and can be applied without dataset specific training. These results suggest the possibility of E-FAGF to achieve a balance between classical efficiency and learning-based adaptability, thereby forming a new scenario to combine fast and reliable image restoration for actual scenarios.
Volume: 24
Issue: 1
Page: 219-227
Publish at: 2026-02-01

Determinants of AI adoption for authentic assessment in open university systems

10.11591/ijere.v15i1.36368
Kemmanat Mingsiritham , Gan Chanyawudhiwan
Artificial intelligence (AI) is transforming higher education through personalized learning and innovative assessment methods. This study explores the factors influencing AI adoption for authentic assessment in open and distance learning environments. Using a survey of 185 instructors, an integrated framework based on the theory of planned behavior (TPB) and the technology acceptance model (TAM) was tested via structural equation modeling (SEM). Key constructs included attitude toward the behavior (ATT), subjective norm (SN), perceived behavioral control (PBC), self-efficacy (SE), and barriers to AI adoption (BAA), with intention to use AI (INT) and actual adoption behavior (AAB) as outcomes. Results showed that SE, ATT, PBC, and SN positively influenced INT, which in turn strongly predicted AAB. In addition, BAA had no significant effect on INT but showed a negative impact on AAB. The model demonstrated good fit and explained substantial variance (R²=0.746 for INT; R²=0.649 for AAB). These findings highlight the importance of enhancing instructors’ confidence, control, and institutional support while reducing perceived barriers. Strategic investments in training, infrastructure, and leadership support are crucial to advancing AI-enabled authentic assessment in higher education.
Volume: 15
Issue: 1
Page: 479-488
Publish at: 2026-02-01

Organizing students’ research activities based on STEM elements in the study process

10.11591/ijere.v15i1.35075
Shakhislam Laiskhanov , Seminar Yerkegul
Although science, technology, engineering, and mathematics (STEM) integration in higher education is advancing globally, its adoption in geography programs in Kazakhstan remains limited. This study examines the effectiveness of embedding selected STEM elements into the course “Geography of Aktobe Oblаst” as а means of strengthening students’ research competencies. A mixed-method design was employed, combining analysis of satellite-derived indicаtоrs, wоrk with geоspаtiаl plаtfоrms (аrcGIS Prо, EоSDа Crоp Mоnitоring, and Eо Brоwser), prаcticаl climаte-bаsed cаlculаtiоns, clаssrооm оbservаtiоns аnd cоmpаrаtive аssessment аnаlysis. The 24 students pаrticipаted in the interventiоn, cоmpleting а series оf inquiry-driven tаsks invоlving the Normalized Difference Vegetation Index (NDVI) interpretаtiоn, spectrаl reflectаnce аnаlysis аnd climаtоlоgicаl cоrrelаtiоns. Survey dаtа indicаted thаt mоre thаn 90% оf pаrticipаnts repоrted imprоved understаnding оf envirоnmentаl prоcesses, while mаny nоted gаins in аnаlyticаl reаsоning аnd dаtа-driven interpretаtiоn. Midterm perfоrmаnce results shоwed а mоdest but cоnsistent imprоvement fоllоwing the implementаtiоn оf STEM-оriented аssignments. The findings suggest thаt structured integrаtiоn оf geоspаtiаl аnd аnаlyticаl STEM tооls cаn meаningfully suppоrt the develоpment оf reseаrch skills in university geоgrаphy cоurses. By enаbling students tо wоrk with аuthentic envirоnmentаl dаtаsets, the аpprоаch cultivаtes higher-оrder reаsоning, interdisciplinаry thinking аnd sustained learner engаgement. The results highlight the pоtentiаl fоr brоаder аpplicаtiоn оf STEM-bаsed instructiоnаl mоdels in Kаzаkhstаni higher educаtiоn аnd underscоre the need fоr further lоngitudinаl аnd cоmpаrаtive studies tо evаluаte lоng-term impаcts.
Volume: 15
Issue: 1
Page: 705-713
Publish at: 2026-02-01

Optimization and techno-economic analysis of hybrid renewable systems in Nigeria

10.12928/telkomnika.v24i1.27499
Lambe; Kwara State University Mutalub Adesina , Jamiu; Kwara State University Lawal , Olalekan; Kwara State University Ogunbiyi , Abdulwaheed; Kwara State University Musa , Bilkisu; Kwara State University Jimada Ojuolape , Monsurat; Kwara State University Omolara Balogun , Bashiru; Kwara State University Olalekan Ariyo
Rising electricity demand, fossil fuel depletion, and environmental concerns highlight the need for sustainable rural electrification. The Elenjere community in Kwara State, Nigeria, depends on costly diesel generation and limited grid access, creating an urgent demand for reliable and affordable alternatives. This study designs and optimizes a hybrid renewable energy system (HRES) for the community using hybrid optimization model for electric renewables (HOMER) Pro simulation. The proposed system combines photovoltaic (PV), wind turbines (WT), battery storage (BAT), inverter (INV), and a diesel generator (DG) as backup. Field data on load demand, solar radiation, and wind speed were used for realistic modeling. System performance was evaluated using levelized cost of energy (LCOE), net present cost (NPC), and system capital cost (SCC). Results show the PV/WT/BAT/INV/GEN configuration achieved the lowest LCOE of USD 0.455/kWh, an NPC of USD 2.98 million, and 86.2% renewable penetration, significantly reducing diesel use. Sensitivity analysis revealed that reducing battery costs and increasing PV capacity could lower the LCOE to USD 0.227–0.325/kWh. The study demonstrates how modest wind resources (4.19 m/s at 10 m) complement PV in low-wind regions while addressing inflation realism (25.5% discount rate, foreign exchange (FX) volatility). Future work will include dynamic control simulation and lifecycle analysis to enhance scalability and sustainability.
Volume: 24
Issue: 1
Page: 343-358
Publish at: 2026-02-01

Tolerance on campus: the impact of religious commitment and respect among university students

10.11591/ijere.v15i1.32607
Mohammad Jaber Thalgi , Nader Al-Refai , Kadir Gömbeyaz , Hanan Bdoor , Ayse Zisan Furat
Religious commitment, particularly within Islamic contexts, is often viewed as a guiding framework for promoting values such as tolerance, respect, and social harmony; however, differing interpretations and personal expressions of religiosity can sometimes challenge these ideals, necessitating deeper exploration of how religiosity influences social interactions. The study investigates the relationship between religious commitment and respect for others regarding the levels of tolerance behavior among university students. The study employed a descriptive quantitative cross-sectional survey from June 16 to August 16, 2023, with a sample of 334 enrolled in the College of Sharia at Yarmouk University in Jordan. The survey consists of three main scales: religious commitment, respect for others, and tolerance. Students’ demographic data, including gender, nationality, age group, academic department, and the year of study, were also collected via the questionnaire. The findings highlight significant gender differences in religious commitment, with males demonstrating higher levels than females. While no significant age differences were observed in religious commitment, tolerance varied notably, particularly among the 24-26 age group. The study participants represented a diverse range of countries of origin. A country-wise analysis revealed that students from Thailand have the highest religious commitment, underscoring the influence of cultural contexts. Departmental comparisons showed no significant differences, although the findings highlight that respect for others impacts tolerance, religious commitment and demography have almost no effect as predicted. The findings emphasize the primary role of respect in fostering social harmony, suggesting that future interventions should focus on promoting respect as a fundamental value in Islamic culture to enhance tolerance.
Volume: 15
Issue: 1
Page: 181-194
Publish at: 2026-02-01
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