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Energy-efficient certificateless signcryption for secure data transfer in wireless sensor networks

10.12928/telkomnika.v23i4.26241
Paruvathavardhini; Avinashilingam Institute for Home Science and Higher Education for Women Jaganthan , Sargunam; Avinashilingam Institute for Home Science and Higher Education for Women Balusamy
Wireless sensor networks (WSN) have gained high popularity in the realm of technological innovation and have a prime responsibility of transferring the data safely to the sink despite the vulnerable situation presiding around the network. There needs to be a compromise made between energy consumption and the intricate network security system because they are inversely correlated. To create a safe and effective data transfer between the communicating nodes, a new censored regressive jaccard indexed certificateless signcryption (CEJICS) technique is suggested. Initially, the Gaussian likelihood censored regression is applied to identify the energy efficient node which supports enhancing the network lifetime. The security is implemented using the rabin cryptographic jaccard indexive certificateless signcryption (RCJICS). To create a signcryption system with the characteristics of digital signature and ciphertext authenticity, the proposed work focuses on certificateless cryptography. The NS-2 simulator is used for the simulation, and performance metrics are used to evaluate it. According to the observed quantitative results, the suggested CEJICS method outperforms the other conventional methods by improving the packet delivery ratio (PDR) by 93%, achieving a minimum drop of 7%, reducing delay by 22%, reducing overhead to 17%, minimizing energy consumption by 15%, and ultimately extending the network lifetime by 10%.
Volume: 23
Issue: 4
Page: 1084-1096
Publish at: 2025-08-01

Graphene-based high-gain MIMO antenna for enhanced 6G wireless communication systems

10.12928/telkomnika.v23i4.26568
Narinderjit Singh; INTI International University Sawaran Singh , Md. Ashraful; Daffodil International University Haque , Jamal; Daffodil International University Hossain Nirob , Kamal; Daffodil International University Hossain Nahin , Md. Kawsar; Daffodil International University Ahmed , Md. Sharif; Daffodil International University Ahammed , Redwan; Daffodil International University A. Ananta , Liton; Pabna University of Science and Technology Chandra Paul
This paper presents a novel design and analysis of a high-performance multiple-input multiple-output (MIMO) terahertz (THz) antenna intended for next-generation sixth-generation (6G) wireless communication systems. The proposed antenna operates over a wide frequency range of 1 THz to 4.9 THz, achieving a broad bandwidth of 3.9 THz with three distinct resonant frequencies at 2.05 THz, 3.9 THz, and 4.52 THz, each exhibiting excellent return loss characteristics. The antenna features a graphene-based patch with a copper ground plane, etched on a polyimide substrate with a dielectric constant (εr) of 3.5 and a thickness of 10 micrometers (μm). Key performance metrics, including a high gain of 15.9 decibels (dB), an efficiency of 95.95%, an envelope correlation coefficient (ECC) of 0.0005, and a diversity gain (DG) of 9.997 dB, indicate outstanding performance. The measured isolation between the two antenna elements is -31.91 dB, signifying excellent isolation. An equivalent resistor-inductor-capacitor (RLC) circuit model is developed using advanced design system (ADS), validated by comparing S11 results from both computer simulation technology (CST) and ADS simulations. The proposed MIMO antenna’s wide operating range and robust performance demonstrates great potential for high-speed THz wireless communication, imaging, spectroscopy, sensing, and offers valuable contributions to industry and innovation.
Volume: 23
Issue: 4
Page: 837-846
Publish at: 2025-08-01

Factors influencing blockchain adoption intention in Philippine small and medium enterprises

10.12928/telkomnika.v23i4.26744
Victor James; Chung Yuan Christian University C. Escolano , Wei-Jung; Chung Yuan Christian University Shiang , Alexander; Lyceum of the Philippines University A. Hernandez , Darrel; Bohol Island State University A. Cardaña
As an emerging technology, blockchain has huge potential for transforming various industries, such as small and medium enterprises (SMEs). Despite its promising impact, its application in the supply chains of SMEs in developing countries is still in its infancy. This study analyzes the key factors of blockchain adoption intention in Philippine SMEs through an integrated technology-organization-environment (TOE) and technology acceptance model (TAM) with external variables. The data were obtained through a survey of 465 SME practitioners in the national capital region (NCR), Philippines, and analyzed using partial least squares and structural equation modeling (PLS-SEM). In terms of technology dimensions, relative advantage (RLA) had a positive influence on perceived usefulness (PUS) while compatibility (COM) had a positive influence on perceived ease of use (PEU), which both subsequently led to blockchain adoption intention. As regards organization, top management support (TMS) had a significant influence on the adoption intention of blockchain among SMEs. In terms of environment, only competitive pressure (CMP) had significant influence on blockchain adoption intention. In general, most of the hypothesized relationships are significant; thus, SMEs have a positive interest in adopting blockchain technology. Finally, the study serves as baseline evidence of blockchain adoption intention among SMEs in the Philippines.
Volume: 23
Issue: 4
Page: 965-975
Publish at: 2025-08-01

Archimides multiband micro ribbon spiral antenna for energy harvesting

10.12928/telkomnika.v23i4.25702
Edison Andrés; Metropolitan Technological Institute Zapata Ochoa , Vanessa; Metropolitan Technological Institute García Pineda , Alejandro; Universidad de Los Lagos Valencia Arias , Francisco Eugenio; Metropolitan Technological Institute López Giraldo
The current need to conserve natural resources and find new ways to advance without damaging the environment has led to the search for different energy sources. In this sense, the availability of radio frequency (RF) energy is found as a favorable option for new energy sources. This work describes the design of a microstrip antenna for the collection of radiofrequency energy in the megahertz band. Using the automatic optimization software computer simulation technology (CST) Studio, a circular spiral antenna is simulated using the low-cost FR4 substrate with a thickness of 1.57 mm and copper as the conductive material with a thickness of 0.035 mm. The proposed design presents resonance frequencies in multiple bands from 550 MHz to 1900 MHz, with bandwidths between 15 MHz and 150 MHz. The antenna design is based on the resonant cavity model and presents circular polarization due to its design coil type, with modified geometry using symmetrical orthogonal slots to generate multiple working bands. The design of this antenna can capture the power emitted by the frequency bands used in mobile telephony, radio communications, broadcasting, and television.
Volume: 23
Issue: 4
Page: 882-895
Publish at: 2025-08-01

Modified electro-optical modulator based bipolar optical code division multiple access for free-space optics

10.12928/telkomnika.v23i4.26727
Eddy; Universitas Kristen Krida Wacana Wijanto , Kevin; Universitas Kristen Krida Wacana Sutanto
This study introduces a modified electro-optical modulator (EOM) to enhance bipolar optical code division multiple access (OCDMA) in free-space optical (FSO) communication. The proposed system improves signal quality, spectral efficiency, and resilience to atmospheric turbulence. Unlike conventional dual EOM techniques, the modified EOM enables simultaneous transmission of ‘0’ and ‘1’ chip values, reducing multiple access interference (MAI) and enhancing system robustness. This approach optimizes bandwidth utilization and ensures stable performance in varying environmental conditions. Simulations were conducted in an additive white Gaussian noise (AWGN) channel using three spectral amplitude coding (SAC) schemes: modified M sequence, Walsh-Hadamard, and random diagonal (RD) codes. Results indicate that the modified EOM scheme significantly improves FSO system performance, achieving an average 47.1% lower minimum log of bit error rate (BER) in normal weather and 43.3% lower in extreme weather, ensuring superior noise suppression and signal integrity across varying environmental conditions. Additionally, the system maintains superior performance over longer distances, demonstrating its suitability for high-speed, long-range FSO applications. These findings highlight the potential of the modified EOM based bipolar OCDMA system in advancing next-generation optical wireless networks, offering a more efficient, interference-resistant, and high-capacity communication solution for future technologies such as 6G and beyond.
Volume: 23
Issue: 4
Page: 857-870
Publish at: 2025-08-01

Deep learning-based image super-resolution using generative adversarial networks with adaptive loss functions

10.12928/telkomnika.v23i4.26105
Hani Q.; Mutah University R. Al-Zoubi
This study investigates deep learning based single image super-resolution (SISR) and highlights its revolutionary potential. It emphasizes the significance of SISR, and the transition from interpolation to deep learning driven reconstruction techniques. Generative adversarial network (GAN)- based models, including super-resolution generative adversarial network (SRGAN), video super-resolution network (VSRResNet), and residual channel attention-generative adversarial network (RCA-GAN) are utilised. The proposed technique describes the loss functions of the SISR models. However, it should be noted that the conventional methods frequently fail to recover lost high-frequency details, which signify their limitations. The current visual inspections indicate that the suggested model can perform better than the others in terms of quantitative metrics and perceptual quality. The quantitative results indicate that the utilised model can achieve an average peak signal-to-noise ratio (PSNR) enhancement of X dB and an average structural similarity index (SSIM) increase of Y. A range of improvements of 7.12-23.21% and 2.75-10.00% are obtained for PSNR and SSIM, respectively. Also, the architecture deploys a total of 2,005,571 parameters, with 2,001,475 of these being trainable. These results highlight the model’s efficacy in maintaining key structures and generating visually appealing outputs, supporting its potential implications in fields demanding high-resolution imagery, such as medical imaging and satellite imagery.
Volume: 23
Issue: 4
Page: 1032-1045
Publish at: 2025-08-01

Outlier detection and clustering of fifth-generation wireless channel model datasets

10.12928/telkomnika.v23i4.26439
Jojo; University of Santo Tomas Blanza , John Bernard; Bank of the Philippine Islands Cipriano
The fifth-generation (5G) wireless communications system offers faster data rates, lower latency, and more interconnecting devices. Various 5G channel models were developed to study its stochastic characteristics before implementation. These channel models generate multipath components that are grouped into clusters. The multipath clusters serve as datasets in multipath clustering. The clustering results are then used to examine the propagation properties of the 5G system. However, datasets are prone to outliers. They tend to affect clustering accuracy. Hence, this study clusters the datasets generated by the channel models using five clustering approaches, removes the outliers using mean-shift outlier detection, and clusters the datasets free of outliers again using the same clustering algorithms. Outlier detection shows that 5G channel model datasets contain noise, and outlier removal improves the modeling characteristics, as demonstrated by enhanced clustering accuracy. Results show that most of the outliers are detected in the 2×SD threshold. The removal of the outliers using the said threshold increased the clustering accuracy of K-means and AC-Single in Semi-Urban B1 LOS multiple links by 78.85% and 55%, respectively, and DBSCAN in Semi-Urban B2 LOS multiple links by 57.14%. Outlier detection and removal also work well with 5G channel model datasets.
Volume: 23
Issue: 4
Page: 871-881
Publish at: 2025-08-01

Consumer behavior switching from human agents to chatbots in the health service industry

10.11591/ijict.v14i2.pp355-365
Dwi Fajar Yulianto , Titik Pratiwi , Fatih Akbarul Irsan , Faranita Mustikasari
Artificial intelligence (AI) technology is used in organizations to replace human services with technology, altering customer service experiences. Only a limited number of studies have explored how consumers change their behavior from human-assisted to technology-assisted services when using AI in frontline and specialty healthcare services. This study examined the elements that impact consumers’ transition from human agents to AI-based conversational agents using the push-pull mooring framework. Data from 147 healthcare users was evaluated using structural equation modeling. The data indicates that push effects, specifically adaptability, have a negative impact on switching behavior, while pull effects, such as responsiveness and accessibility, have a positive impact on the switching behavior of customers.
Volume: 14
Issue: 2
Page: 355-365
Publish at: 2025-08-01

Renewable energy impact integration in Moroccan grid-load flow analysis

10.11591/ijece.v15i4.pp3632-3648
Safaa Essaid , Loubna Lazrak , Mouhsine Ghazaoui
This paper analyzes the behavior of a Moroccan electric transportation system in the presence of an integration of renewable energy sources, which represents a significant challenge due to their intermittent nature. The aim is to evaluate the performance of the transportation system in various situations and possible configurations. The current study enables the calculation of power flow in the network using the Newton-Raphson method under the MATLAB/Simulink software. To achieve this, a series of power flow simulations were conducted on a 5-bus Moroccan electrical network, examining four distinct scenarios. In addition, this article offers an evaluation of the power flow performance of the same electric transportation system with varying percentages of renewable energy penetration. In order to provide a complete critical analysis, many simulations were conducted to obtain the voltage and active power profile generated at different bus locations, as well as an evaluation of the losses in the studied network.
Volume: 15
Issue: 4
Page: 3632-3648
Publish at: 2025-08-01

Perceived ease of use, usefulness, and task interdependence: impacts on employee performance in higher education

10.12928/telkomnika.v23i4.26699
Inayati; Universitas Sriwijaya Mandayuni , Kiagus Muhammad; Universitas Sriwijaya Sobri , Alfitri; Universitas Sriwijaya Alfitri , Andries; Universitas Sriwijaya Lionardo
This research aimed to investigate the relationship between perceived ease of use (PEU), perceived usefulness (PU), and task interdependence (TASKINT) on employees’ work performance. Technology acceptance model (TAM) was used as a theoretical perspective to explore technology adoption in the context of employees in higher education using electronic asset management (EAM). Moreover, a quantitative method was used to explain the causality of the relationship between the variables, and a total of 380 respondents were determined as the sample. The results showed that PEU and usefulness had a significant effect on TASKINT. Even though PEU and TASKINT had a significant effect on employees’ work performance, PU did not have a significant effect. In addition, the results showed TASKINT significantly mediated the relationship between perceived ease of use, usefulness, and employees’ work performance. These findings imply that enhancing the ease of use and fostering TASKINT can lead to improved employee performance when adopting new technologies. For higher education institutions (HEI), focusing on user-friendly systems and promoting collaborative tasks can maximize the benefits of technology implementation on work performance.
Volume: 23
Issue: 4
Page: 1108-1119
Publish at: 2025-08-01

A new resonant-based sensor for non-invasive measurement of blood glucose levels

10.12928/telkomnika.v23i4.26280
Abolfazl; University of Birjand Bijari , Faeze; University of Birjand Foolad , Mohammadreza; University of Birjand Khorshidi
This paper presents a rapidly developed non-invasive microstrip sensor for measuring blood glucose levels (BGLs). The sensor features a microstrip closed-loop square resonator integrated with an interdigital capacitor (IDC), creating a sensitive area for glucose detection when a patient’s finger is placed on it. Using odd and even mode analytical methods and transmission line theory, we analyzed the sensor’s performance. Results indicate that the second even mode demonstrates significant changes across a standard glucose concentration range. The sensor was designed and simulated in ANSYS high frequency structure simulator (HFSS), showing a resonance frequency shift of up to 24.9 MHz at 1.94 GHz and a sensitivity of 110 kHz per mg/dL over a detection range of 0 to 216 mg/dL. Additionally, the frequency shift exhibits a high linear correlation (0.9485). In summary, the proposed sensor shows significant promise for achieving precise measurements of BGLs.
Volume: 23
Issue: 4
Page: 1046-1057
Publish at: 2025-08-01

An extensive framework for assessing the quality of websites

10.12928/telkomnika.v23i4.26374
Sasi; CMR College of Engineering and Technology Bhanu Jammalamadaka , Bala Krishna; MLR Institute of Technology Kamesh Duvvuri , Sastry Kodanda; Koneru Laksmaiah Education Foundation University Rama Jammalamadaka , Vishnu; Koneru Laksmaiah Education Foundation University Priya Biyyapu
The quality of the website is quite important in generating customer satisfaction and loyalty. A website’s quality depends on several factors, features, and characteristics. Several computational methods are necessary to evaluate the quality of each factor and subsequently determine the overall quality of the entire website. Each factor does not contribute to the same level of quality required by the end users and thus requires a weighting system. Expert systems, which are either manually defined or learnt using artificial intelligence (AI), are to be modelled for assessing the quality of a factor/sub-factor or characteristics of a sub-factor. The quality of a website varies depending on the context. Context-based quality assessment of the websites is required. There is a need to generate example sets to assess the quality of websites and to establish relationships between web-related quality factors, subfactors, and characteristics. In this paper, a comprehensive framework is presented that caters to parametric structure building and mapping, parsers for computing characteristic values, context assessment, building expert systems, and learning models for assessing the quality of websites and weighing the factors that have specific significance on the quality of the website.
Volume: 23
Issue: 4
Page: 986-999
Publish at: 2025-08-01

EdgeShield: a robust and agile cybersecurity architecture for the internet of medical things

10.12928/telkomnika.v23i4.26989
Anass; Sultan Moulay Slimane University Misbah , Anass; International University of Rabat Sebba , Imad; Sultan Moulay Slimane University Hafidi
We present EdgeShield, a lightweight pipeline that streamlines internet of medical things (IoMT) traffic analysis by pairing aggressive dimensionality-reduction with federated model aggregation. It employs systematic preprocessing, advanced feature selection, and robust sampling to reduce computational overhead while enhancing performance. Through feature engineering techniques such as principal component analysis (PCA), targeted feature selection, and embedding methods, EdgeShield reduces dataset dimensionality by 96%, enabling near real-time detection and prevention of cyber attacks on resource-constrained edge devices. To harden the IoMT perimeter, EdgeShield trains ten lightweight edge models in just 54s and merges their parameters into a single global clas sifier with negligible extra delay. This method requires no additional training or predictions, thus accelerating deployment. Additionally, by using a compact dataset with five top-performing features and PCA with two components, EdgeShield consistently achieves accuracy levels exceeding 99.2% for individual edge models and the consolidated global model. With a built-in continuous improvement loop, EdgeShield dynamically adapts to emerging data patterns and operational conditions, driving substantial advancements in IoMT ecosystem management. This approach delivers both rapid machine learning model deployment and robust cyber attack detection, illustrating its potential to revolutionize IoMT security and elevate healthcare data integrity
Volume: 23
Issue: 4
Page: 1128-1136
Publish at: 2025-08-01

Work-integrated curriculum transformation and autopedagogical competence in prospective teachers

10.11591/ijere.v14i4.33746
Aiym Tynyskhanova , Kalbike Yessenova , Irodakhon Xoldarova , Fariza Ismailova , Aziza Zhunusbekova
Despite substantial investments in higher education, many Kazakhstani graduates face challenges in securing employment due to a lack of essential job-related competencies. The predominant discipline-based learning approach in Kazakhstani universities often fails to cultivate autopedagogical competence—a critical skill for applying knowledge in real-world contexts and solving practical problems. This study aims to examine the impact of work-integrated curriculum (WIC) transformation on the development of student autopedagogical competence (SAC). A non-experimental quantitative research design was employed, utilizing survey techniques to collect data from a sample of 180 students in Kazakhstan. The sample was determined through random stratified sampling to ensure representativeness. Data analysis was conducted using structural equation modeling (SEM) to identify relationships between dependent (autopedagogical competence), independent (WIC transformation), and moderating variables. The findings reveal that curriculum transformation has a significant and positive impact on the development of autopedagogical competencies. Students exposed to work-integrated learning (WIL) demonstrate stronger self-directed learning capabilities, enhancing their readiness for workforce integration and lifelong professional development. These insights highlight the necessity of innovative curriculum strategies to bridge the gap between academic learning and practical skill acquisition, offering valuable guidance for educators seeking to improve vocational education outcomes.
Volume: 14
Issue: 4
Page: 3273-3282
Publish at: 2025-08-01

Ensemble of convolutional neural network and DeepResNet for multimodal biometric authentication system

10.11591/ijece.v15i4.pp4279-4295
Ashwini Kailas , Madhusudan Girimallaih , Mallegowda Madigahalli , Vasantha Kumara Mahadevachar , Pranothi Kadirehally Somashekarappa
Multimodal biometrics technology has garnered attention recently for its ability to address inherent limitations found in single biometric modalities and to enhance overall recognition rates. A typical biometric recognition system comprises sensing, feature extraction, and matching modules. The system’s robustness heavily relies on its capability to effectively extract pertinent information from individual biometric traits. This study introduces a novel feature extraction technique tailored for a multimodal biometric system utilizing electrocardiogram (ECG) and iris traits. The ECG helps to incorporate the liveliness related information and Iris helps to produce the unique pattern for each individual. Therefore, this work presents a multimodal authentication system where data pre-processing is performed on image and ECG data where noise removal and quality enhancement tasks are performed. Later, feature extraction is carried out for ECG signals by estimating the Heart rate variability feature analysis in time and frequency domain. Finally, the ensemble of convolution neural network (CNN) and DeepResNet models are used to perform the classification. The overall accuracy is reported as 0.8900, 0.8400, 0.7900, 0.8932, 0.87, and 0.97 by using convolutional neural network-long short-term memory (CNN-LSTM), support vector machine (SVM), random forest (RF), CNN, decision tree (DT), and proposed MBANet approach respectively.
Volume: 15
Issue: 4
Page: 4279-4295
Publish at: 2025-08-01
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