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Hybrid intrusion detection in IoT devices: a deep learning approach using Kitsune and quantized autoencoder

10.12928/telkomnika.v24i2.27316
Md. Rifat E; Comilla University Noor , Md. Tofael; Comilla university Ahmed , Dulal; Comilla University Chakraborty , Pintu Chandra; Comilla University Paul , Sohana; Comilla University Nowar , Rejwan; Comilla University Ahmed , Tanjina; Comilla University Akter
Internet of things (IoT) has been transforming the way to connect and communicate in smart homes, healthcare, and businesses so fast and rapidly around the world. But this growth has complicated security, because IoT devices are more likely to be hacked as they’re smaller, without even regular security practices, and under attack by more sophisticated threats. Traditional intrusion detection systems (IDS) are not functioning well in IoT environments as they are computationally expensive and struggle to accommodate the heterogeneous nature of IoT networks. This paper introduces a cross-domain intrusion detection based on adaptive adversarial training using Kitsune and quantized autoencoders (QAE) for anomaly detection and classification. The model is capable of capturing different attacking techniques, such as distributed denial of service (DDoS), Mirai botnet attacks, address resolution protocol (ARP) spoofing, and data exfiltration, by leveraging the reconstruction error generated by Kitsune autoencoders. The degree-based classification enables the system to dynamically categorize anomalies according to their severity, rendering the model exceptionally adaptive to various attacks. The anomalies are also classified into different types of attacks (normal, suspicious, and malicious) based on binarized error values. The approach achieves a high accuracy with an F1 score of 85.9% and supports real-time characterization to increase security in IoT scenarios.
Volume: 24
Issue: 2
Page: 452-465
Publish at: 2026-04-01

Wearable and implantable antennas for healthcare applications: advancements, challenges, and future directions

10.11591/ijece.v16i2.pp827-841
Sameera P. , Priyadarshini K. Desai , Keerthi Kulkarni
The rise of personalized and remote healthcare solutions has accelerated the demand for reliable wireless communication systems integrated into medical devices. Among these, wearable and implantable antennas play a crucial role by enabling the seamless exchange of data between in-body or on-body sensors and external monitoring equipment. These antennas are key components in systems designed for continuous health monitoring, early diagnosis, and patient rehabilitation. Unlike conventional antennas, those used in medical applications must function efficiently in close contact with or inside the human body, often under challenging conditions such as body movement, varying tissue properties, and limited space. As a result, the design and development of these antennas require careful consideration of factors like flexibility, biocompatibility, low power operation, and electromagnetic safety. This study reviews recent publications from 2017 onwards on wearable and implantable antennas. The material type, operating frequency band, and operational environment are considered for the design of the wearable and implantable antenna. To minimize loss, the research employed a high-thickness substrate, gold, and graphene material for the radiating patch in most of the design. This review presents a detailed overview of recent advancements in wearable and implantable antennas tailored for healthcare applications, highlights current design challenges, and outlines future research opportunities in this rapidly evolving field.
Volume: 16
Issue: 2
Page: 827-841
Publish at: 2026-04-01

Development of BAPOLAIC: AI chatbot for optical character recognition based-document extraction and voice assistant

10.11591/ijece.v16i2.pp1002-1009
Rival Fahreji , Ryan Satria Wijaya
Conventional chatbots often lack integrated functionalities for complex academic tasks, such as multi-format document handling and multimodal interaction. This paper presents the design, implementation, and performance evaluation of BAPOLAIC, a web-based, multimodal AI assistant developed to address this gap. The system architecture integrates optical character recognition (OCR), a dual-strategy natural language processing (NLP) module, and voice assistance, all orchestrated by the Gemini API. Quantitative evaluation confirmed high performance: the OCR module achieved a 98.69% average accuracy, and the retrieval-based NLP path correctly handled 90% of test queries. Furthermore, the API integration demonstrated exceptional efficiency with a median latency as low as 0.06 ms. Task-based evaluations validated BAPOLAIC's effectiveness in performing intelligent functions like summarization and content-based Q&A, with a superior capacity for handling up to 10 consecutive documents. The results validate BAPOLAIC as a successful proof-of-concept for a specialized academic tool, providing a framework for integrating multiple AI technologies to enhance educational productivity.
Volume: 16
Issue: 2
Page: 1002-1009
Publish at: 2026-04-01

Design and simulation of an electric vehicle charger with integrated interleaved boost converter and phase-shifted full-bridge converter using MATLAB/Simulink

10.11591/ijece.v16i2.pp687-698
Ahmad Saudi Samosir , Tole Sutikno , Alfin Fitrohul Huda , Luthfiyyatun Mardiyah
This paper presents the design and simulation of a high-efficiency electric vehicle (EV) charger that integrates a two-phase interleaved boost converter (IBC) with a phase-shifted full-bridge (PSFB) converter using MATLAB/Simulink. In contrast to existing studies that treat these converter stages independently, this work introduces a unified AC–DC–DC architecture that simultaneously minimizes input current ripple, improves DC-bus stability, and enables soft-switching operation for reduced switching losses. The values of the inductors and capacitors are derived analytically based on ripple constraints and switching frequency considerations, and example calculations are explicitly provided. Simulation results demonstrate that the proposed charger maintains a stable 600-V DC bus with less than 2% voltage ripple, delivers a controlled charging current of 100 A with 3 A ripple, and achieves an overall efficiency of 95%. These findings indicate that the integrated interleaved–PSFB topology provides superior conversion efficiency and power quality, making it a strong candidate for future EV fast-charging infrastructure.
Volume: 16
Issue: 2
Page: 687-698
Publish at: 2026-04-01

Comparative analysis for different passive filter topologies in grid-tied PV systems

10.11591/ijeecs.v42.i1.pp1-12
Shorouk Elsayed Ibrahim Mehrez , Asmaa Sobhy Sabik , Fady Wadie , Ibrahim A. Nassar
The enhancement of power quality in grid-connected photovoltaic (PV) systems requires the development of effective harmonic mitigation techniques. This paper addresses the design and evaluation of specific passive filters (RC, LC, and LCL filters) for a three-phase grid-tied PV system, aiming to mitigate harmonics in the power system. The paper also systematically calculates and optimally solves for the components required for the given system. The design of the parameters for all filter topologies within the 100-kW grid-connected PV array is thoroughly elaborated. Each topology is evaluated based on the total harmonic distortion (THD) content, which is obtained using fast fourier transform (FFT), as well as DC voltage and system efficiency. The results are presented to identify the best solutions for harmonic mitigation. The modified filter model demonstrated in this study effectively limits harmonic distortion at the output. It is shown that the proposed design addresses the issue of harmonic distortion in grid-connected inverters for PV systems. The goal of this paper is to identify the most reliable filter for extending the system’s lifespan. The results suggest that the LCL filter is superior, as the system’s DC voltage remained within the rated value and the system efficiency was higher compared to the RC filter. The performance and functionality of these filters were tested using MATLAB/Simulink.
Volume: 42
Issue: 1
Page: 1-12
Publish at: 2026-04-01

FADTESE: A framework for automated deployment and effectiveness evaluation for big data tools

10.11591/ijece.v16i2.pp1051-1062
Mony Ho , Sokroeurn Ang , Sopheaktra Huy , Midhunchakkaravarthy Janarthanan
Manual deployment of big data tools such as Hadoop, Sqoop, and Python is often slow, complex, and error prone because of extensive configuration steps, dependency conflicts, and inconsistent command-line execution. These challenges lead to unreliable installations and variations across systems. This study introduces framework for automated deployment and time, error, satisfaction evaluation (FADTESE), a unified framework that automates the installation of big data tools and evaluates its performance. The framework consists of two integrated components. The first is the automated deployment model, which validates environment readiness using the automation deployment readiness index (ADRI) and achieved a readiness value of 1.0 in this study. The second is the time, error, and satisfaction evaluation model, which quantifies improvements gained from automation and produced a score of 0.5941 through bootstrap resampling with ten thousand samples, indicating moderate effectiveness. The FADTESE script was technically validated across multiple Linux environments, including Ubuntu, Linux Mint, and AWS Ubuntu server systems. The performance evaluation involving eighty IT practitioners was conducted on Ubuntu systems to ensure consistent testing conditions and confirmed substantial gains in installation time, error reduction, and user satisfaction. Combining readiness and effectiveness yields a composite score of 0.5941 or 59.41%. FADTESE provides a reproducible and data driven method that standardizes big data deployment and improves reliability across local and cloud-based Linux environments.
Volume: 16
Issue: 2
Page: 1051-1062
Publish at: 2026-04-01

Design and implementation of smart meter for optimizing and managing electrical energy in Morocco

10.11591/ijece.v16i2.pp663-674
Alhussein Bagayogo , Omar Kabouri , Aboubakr El Makrini , Mohamed Azeroual , Hassane El Markhi
The growth of renewable energy sources necessitates the use of accurate and fast smart meter solutions. This article presents a low-cost internet of things (IoT) based smart meter adapted to the Moroccan electricity grid, supporting bidirectional energy measurement, DLMS/COSEM-based communication and control relays for automated energy flow management. The experimental validation shows a maximum measurement error of less than ±0.5%, satisfying the IEC-oriented accuracy requirements. The measured end-to-end latency is approximately 700 ms, including data acquisition (≈450 ms), signal processing (≈60 ms), data serialization (≈75 ms), network transmission (≈90 ms), and server-side processing (≈25 ms). These results demonstrate that the proposed system allows an almost real-time monitoring and control of imported and exported energy, which makes it suitable for the integration of residential renewable energies and the application of smart grids.
Volume: 16
Issue: 2
Page: 663-674
Publish at: 2026-04-01

Towards decision-making and task planning modules for autonomous mini-UAV mission planning in civil applications

10.11591/ijeecs.v42.i1.pp48-61
Asmaa Idalene , Sophia Faris , Hicham Medromi , Khalifa Mansouri
Autonomous mini unmanned aerial vehicles (UAVs) for civilian applications face a critical challenge: during flight, their mission planning cannot break down complex goals into real-time actions. It’s like having a brilliant strategy with no way to execute it in the moment conditions change. While current solutions can handle basic navigation, they often fail when conditions change. This lack of adaptability seriously limits autonomy in real-world applications, like infras tructure inspection or emergency response. The core problem? Nobody has yet built a system that can think in both layers, combining hierarchical goal decom positions with dynamic tasks without overloading the onboard computer. Our work addresses this gap by introducing an integrated mission planning system with two complementary modules. First: the decision-making module employs recursive goal tree construction to transform high-level mission goals into hier archical sub-goal structures in a systematic manner. Second: the task planning module converts these structured goals into concrete MAVLink command se quences. Together, these modules bridge the gap between abstract mission spec ifications and low-level flight operations while enabling dynamic replanning. To verify if our system actually works, we validated the framework through simulation-based experiments using a Python UAV mission simulator across 50 test runs. The results showed a 94% mission completion rate, with an average planning time of 1.8 seconds for missions with 5 to 8 waypoints. It adapted well to surprises: new targets (100% success), no-fly zones (92% success), and priority changes (96% success). Compared to traditional reactive baseline ap proaches, the framework reduced replanning time by 67%. This tells us that the modular approach is not just theoretically sound but it’s also practically viable for real-world civilian operations.
Volume: 42
Issue: 1
Page: 48-61
Publish at: 2026-04-01

Evaluating learning rate effects on long short-term memory for Indonesian sentiment classification

10.12928/telkomnika.v24i2.27398
Serly; Universitas Maritim Raja Ali Haji Eldina , Tekad; Universitas Maritim Raja Ali Haji Matulatan , Novrizal Fattah; Universitas Maritim Raja Ali Haji Fahmitra
Hyperparameter optimization is a crucial process for enhancing the performance of deep learning models, particularly in the context of Indonesian sentiment classification. This study examines the impact of varying learning rates on a long short-term memory (LSTM) architecture trained with the adaptive moment estimation (Adam) optimizer. The dataset comprises 9,295 Indonesian comments automatically labeled by the Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) model. Stratified k-fold cross-validation was employed to maintain class balance during training. Learning curves were analyzed to evaluate convergence and identify potential overfitting, while early stopping was applied when performance improvements became insignificant. The one-way analysis of variance (ANOVA) test (p-adj = 0.000575 < 0.05) revealed significant differences among the learning rate variations. Post-hoc analysis indicated the learning rates of 0.0001, 0.001, and 0.002 differ significantly from 0.02. Descriptive statistics showed that a learning rate of 0.001 was the most optimal, achieving the highest validation accuracy while maintaining a relatively low variance. Evaluation across two data categories demonstrated that lower learning rates (0.0001 and 0.002) achieved the best accuracy, 78.71% on in-domain data, whereas higher learning rates (0.01 and 0.02) performed better on cross-domain data with 36% accuracy. These findings highlight the crucial role of learning rate selection in determining model stability and generalization capability.
Volume: 24
Issue: 2
Page: 564-573
Publish at: 2026-04-01

The impact of EPS on procurement performance: the mediating role of supplier relationship quality in Ghana

10.12928/telkomnika.v24i2.27514
Isaac; University of Professional Studies, Accra - Ghana Asampana , Felix Acquah; Public Procurement Authority Baiden
This study examines the effect of e-procurement systems on procurement performance (PP) in Ghana, highlighting the mediating role of supplier relationship quality (SRQ). A quantitative, cross-sectional survey of 370 procurement professionals from public and private organisations was conducted to assess four dimensions of e-procurement: system integration, data transparency, user-friendliness, and automation. Results indicate that all four dimensions significantly enhance PP, with system integration and user-friendliness emerging as the strongest predictors. Mediation analysis further reveals that SRQ, characterised by trust, communication, and collaboration, partially strengthens the relationship between e-procurement and procurement outcomes. Nonetheless, challenges such as inadequate staff training, limited supplier digital skills, weak infrastructure, and insufficient managerial support hinder optimal system effectiveness. Grounded in the resource-based view (RBV) and transaction cost economics (TCE), the study demonstrates the importance of combining technological and relational capabilities. Recommendations include enhancing digital skills training, strengthening supplier engagement, improving system design, and fostering institutional support.
Volume: 24
Issue: 2
Page: 490-499
Publish at: 2026-04-01

Performance assessment of an adaptive model predictive control with torque braking for lane changes

10.12928/telkomnika.v24i2.27167
Zulkarnain; Universitas Sriwijaya Zulkarnain , Irwin; Universitas Sriwijaya Bizzy , Armin; Universitas Sriwijaya Sofijan , Mohd Hatta Mohammed; Universiti Teknologi Malaysia Ariff
The growing demand for autonomous vehicles requires robust control systems that can maintain safety during complex maneuvers like lane changes. However, a significant research gap exists in developing controllers that effectively manage the combined challenges of steering and braking across diverse and unpredictable driving conditions, such as varying speeds and low-friction road surfaces. This research addresses this gap by proposing and evaluating an adaptive model predictive control (MPC) system integrated with a torque braking distribution strategy. The key advantage of our adaptive method is its ability to continuously update its internal model in real-time, allowing it to anticipate and respond to changing road friction and vehicle dynamics more effectively than a static controller. In simulations of a lane change maneuver across speeds of 10-25 m/s and road friction levels from 0.3 (icy) to 1 (dry asphalt), the proposed system demonstrated a substantial performance improvement. The proposed framework demonstrated a 52.8% average reduction in lateral tracking error and enhanced stability by reducing the yaw rate by up to 41.8% on low-friction surfaces, compared to a non-adaptive MPC baseline. These results quantitatively confirm that our framework’s synergistic coordination of steering and braking significantly enhances the safety, precision, and reliability of autonomous lane change maneuvers.
Volume: 24
Issue: 2
Page: 696-706
Publish at: 2026-04-01

Attributes conducive to anthropomorphism in artificial intelligence

10.12928/telkomnika.v24i2.27483
Rizwan; Murray State University Syed , Hassan; Murray State University Mistareehi
The rapid development of artificial intelligence (AI), particularly large language models (LLMs), has generated both enthusiasm and concern regarding its role in society. While these systems demonstrate impressive technical capabilities, public acceptance is often hindered by perceptions of unpredictability, mistrust, and fears amplified by media narratives. One potential strategy to improve user acceptance is anthropomorphism, the attribution of human-like qualities to AI systems which can make interactions feel more natural and trustworthy. This paper investigates the attributes most conducive to anthropomorphism by conducting a structured review across psychology, human-robot interaction, communication studies, and business applications. The analysis identifies key traits such as emotional expressiveness, conversational coherence, adaptive social behavior, and role-based framing that enhance perceptions of AI as relatable and dependable. By synthesizing these insights, we propose a conceptual framework that highlights the psychological, social, and technical dimensions of anthropomorphism in AI. The findings provide guidance for designing AI systems that balance efficiency with user trust, thereby supporting more effective integration of AI into business, research, and everyday life.
Volume: 24
Issue: 2
Page: 588-598
Publish at: 2026-04-01

Secure two-way relaying with successive interference cancellation and fountain codes: performance analysis

10.12928/telkomnika.v24i2.27314
Nguyen Thi; Industrial University of Ho Chi Minh City Hau , Tran Trung; Posts and Telecommunications Institute of Technology Duy
This paper proposes a secure two-way relaying (TWR) scheme using fountain codes (FCs), successive interference cancellation (SIC), and digital network coding (DNC). Using FCs, two sources exchange their data by first encoding the data into a series of packets (called encoded packets). These encoded packets are then exchanged between the sources via the help of a common relay, and they are also overheard by an eavesdropper. The packet exchange is carried out over two time slots: i) in the first time slot, both sources send their encoded packets to the rela y; and ii) the relay applies SIC to decode two received packets, and then broadcasts the exclusive OR (XORed) packet to both sources in the second time slot. The sources and the eavesdropper try to collect a sufficient number of encoded packets to successfully recover the original data. This paper derives and validates exact closed-form expressions for system throughput (TP), system outage probability (SOP), and system intercept probability (SIP) over Rayleigh fading channels. Furthermore, our findings reveal a reliability-security trade-off as well as the impact of system parameters on the network performance.
Volume: 24
Issue: 2
Page: 420-430
Publish at: 2026-04-01

Dynamic pooling using average-thresholding to improve image classification performance

10.12928/telkomnika.v24i2.27619
Pajri; President University Aprilio , Tjong Wan; President University Sen
Pooling layers are essential in convolutional neural networks (CNNs) for reducing data size while preserving key features. Traditional methods such as Max and Average pooling have limitations. Max pooling is sensitive to noise, while Average pooling treats all activations equally. Although T-Max-Avg pooling addresses these limitations through adaptive top-k selection, its rigid decision rule requires multiple threshold comparisons and limits efficiency, motivating a simpler decision mechanism. This study introduces average-thresholding pooling (ATP), a simplified adaptive method that replaces multiple threshold comparisons with a single decision based on the average of the top-k activations. This design improves computational efficiency and reduces sensitivity to outliers. Experiments on the STL-10 dataset using a LeNet-5 architecture show that the proposed method achieves accuracy comparable to T-Max-Avg pooling (~55.5%) while consistently improving both training efficiency and inference speed. These results indicate that ATP provides a lightweight and practical alternative for CNN-based image classification, offering an improved balance between classification performance and computational efficiency.
Volume: 24
Issue: 2
Page: 663-675
Publish at: 2026-04-01

Design of vehicle to vehicle communication: accident collision prevention using light fidelity and wireless fidelity technology

10.12928/telkomnika.v24i2.27570
Folashade Olamide; Landmark University Omua-ran Nigeria Ariba , Yusuf Isaac; Landmark University Omu-Aran Onimisi , Adedotun; Landmark University Omu-Aran Ijagbemi , Dickson Ogochukwu; Landmark University Omu-Aran Egbune
Vehicle-to-vehicle (V2V) communication is a key component of intelligent transportation systems (ITS), enabling seamless data exchange between vehicles to limit collision risks. This study presents a hybrid communication framework that integrates light fidelity (LiFi) and wireless fidelity (WiFi) technologies to enhance safety and reliability in accident prevention. Lifi using visible light communication, provides line-of-sight for short-range communication, while WiFi ensures long-range coverage in dynamic traffic environments. The proposed system allows vehicles to share speed, braking, and positional data, enabling timely warnings to drivers in high-risk scenarios. The system fuses data communication protocol design, simulation, prototype development, testing, and evaluation. The prototype model was designed and simulated to evaluate the performance of the system in terms of functionality, timing and reliability. Results indicate that the hybrid LiFi-WiFi system improves data transmission efficiency and reduces delay compared to standalone wireless systems. This approach demonstrates significant potential in developing safer transportation networks by combining complementary wireless technologies for V2V communication.
Volume: 24
Issue: 2
Page: 396-406
Publish at: 2026-04-01
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