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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

The developing a smart grid control system based on Konnex electrical equipment and internet of things technology

10.11591/ijece.v16i2.pp1020-1029
Tran Duc Chuyen , Mai Van Tao , Hoang Dinh Co
In this research, the authors present a method for developing a smart grid control system based on Konnex (KNX) electrical equipment and internet of things (IoT) technology to control and monitoring electrical energy processes such as: voltage, current, frequency, and power for independent or grid-connected power systems in industry and civil use. The system includes: KNX electrical equipment (KNX-connectivity), IoT control board, Solar panels that produce electricity to supply the system, battery storage devices, converters and controllers, power consumption loads, and many measuring, switching and protection devices for the system. With computer control programming devices, software, and control algorithms, access is possible via website, computer, smartphone, iPhone, and iPad. The goal is to monitor electricity and automatically control the smart building system, which is being used for high-end apartment buildings (luxury housing estate); offices, hotels, and garden villas. The system was researched and tested at the practice workshop for industrial factories and enterprises, bringing high results. The system aims to save energy in the context of increasingly depleted fossil energy, both in Vietnam and around the world.
Volume: 16
Issue: 2
Page: 1020-1029
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

Comparison of adaptive tuning fuzzy PID and Ziegler-Nichols PID for photovoltaic cooling system

10.11591/ijece.v16i2.pp1063-1074
Yusnan Badruzzaman , Aggie Brenda Vernandez , Septiantar Tebe Nursaputro , Pangestuningtyas Diah Larasati
Renewable energy, particularly solar power, is widely recognized as a clean and sustainable resource, with rooftop photovoltaic (PV) systems playing a vital role in electricity generation. However, high temperatures can significantly reduce their efficiency, making effective cooling systems essential. This study proposes a proportional-integral-derivative (PID) based cooling control system for rooftop PV panels, integrating an adaptive Mamdani fuzzy logic controller to optimize PID parameters dynamically. The methodology includes system modeling, hardware and software implementation, and comparative testing between the Mamdani fuzzy-PID controller and the Ziegler-Nichols PID method. Experimental results show that both controllers effectively regulate PV panel temperature at 36 °C. The Ziegler-Nichols PID achieves faster settling time of 6.45 minutes with a steady-state error of 1.345%, whereas the Mamdani fuzzy-PID reduces the steady-state error to 0.93% but with a longer settling time of 9.15 minutes. These results indicate that the fuzzy-PID controller offers better accuracy and system stability, making it a promising solution for maintaining PV performance under varying environmental conditions. The key novelty of this study lies in its adaptive approach, where the Mamdany fuzzy-PID controller continuously adjust control parameters (Kp,Ki,Kd) in real time, resulting in more consistent and precise temperature regulation than conventional PID tuning methods.
Volume: 16
Issue: 2
Page: 1063-1074
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

Adaptive Lyapunov-based control for underactuated nonlinear system using deep neural network

10.11591/ijece.v16i2.pp717-728
Triya Haiyunnisa , Jony Winaryo Wibowo
This paper proposes an adaptive Lyapunov-based control approach using deep neural networks (DNN) for underactuated nonlinear systems, with case studies on the Furuta pendulum and a wheeled path-following system. This approach combines simultaneous learning of the Lyapunov function V(x) to satisfy the positive-definite condition and the control law u(x) to satisfy negative definiteness of V ̇(x) thus ensuring the asymptotic stability of the system. The proposed model is validated using Python-based simulation. Results show that the proposed method significantly expands the region of attraction (RoA) compared to the linear quadratic regulator (LQR) method. In the Furuta pendulum, the RoA area in the [θ−θ˙] plane increased from 89.04% to 101.14% and in the [α−α˙] plane from 80.28% to 83.79%. Meanwhile, in the wheeled path-following system, the RoA within safety domain increased from 85.28% to 101.69%. Furthermore, robustness tests showed that the controller can maintain tracking performance on a sinusoidal path and reject short disturbances without excessive safety boundary violations. The resulting control signal remained smooth, non-oscillatory, and within the actuator saturation limits, ensuring safe and energy-efficient control. This approach offers a significant contribution by integrating Lyapunov stability theory, deep learning, and online adaptation, resulting a robust and practical for nonlinear underactuated systems.
Volume: 16
Issue: 2
Page: 717-728
Publish at: 2026-04-01

A comprehensive analysis of feature selection and XAI for machine learning classifiers to recognize guava disease

10.12928/telkomnika.v24i2.27599
Sujon Chandra; University of Frontier Technology, Bangladesh (UFTB) Sutradhar , Md. Mehedi; University of Frontier Technology Hasan
Recognizing and classifying diseases in guava is crucial for managing farms to keep crops healthy and increase harvest quality. Cultivators face the most severe challenges when it comes to recognizing and diagnosing guava fruit and leaf illnesses, a task that is nearly impossible to perform manually. This research focuses on developing a robust disease identification model using image data collected locally from guava trees. After data collection, various image processing techniques, including scaling and contrast enhancement, are utilized to make the data more suitable for use. K-means clustering is employed to quickly divide the images into groups, followed by the extraction of important characteristics. Two separate feature ranking approaches, analysis of variance (ANOVA) and least absolute shrinkage selection operator (LASSO), are used to select the best characteristics, identifying the 10 most important attributes. The adaptive boosting (AdaBoost) classifier achieves the highest accuracy among six classifiers for the top seven characteristics indicated by LASSO among the specified features. To enhance the model’s interpretability, two explanation methods, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP), are employed to illustrate how the classifier reaches its conclusions. This approach not only simplifies disease identification but also clarifies the reasoning behind predictions, opening the door to real-world applications in detecting and preventing dangerous diseases.
Volume: 24
Issue: 2
Page: 574-587
Publish at: 2026-04-01

Time encoded signal processing and recognition with vector quantization: applied to Arabic numerals

10.12928/telkomnika.v24i2.27443
Abdelmajid; Sidi Mohamed Ben Abdellah University Lamkadam , Mohammed; Sidi Mohamed Ben Abdellah University Karim
This article presents our contribution to speaker recognition using Arabic numerals. This recognition is based on hybridization between the time encoded signal processing and recognition (TESPAR) technique and vector quantization (VQ), in order to consolidate the classification step thanks to this combination. To set up an effective and efficient recognition system, we used a corpus recorded under ideal conditions, minimizing the differences between the reference corpus and the test corpus. We also applied the linear discriminant analysis (LDA) technique in order to discriminate the acoustic vectors and minimize the representative space. This hybridization indicated a quantifiable increase in the speaker recognition rate with the ten Arabic numerals (0–9).
Volume: 24
Issue: 2
Page: 481-489
Publish at: 2026-04-01

Hybrid PSO-WOA approach for an efficient task offloading in mobile edge computing

10.12928/telkomnika.v24i2.27293
Fatima Zohra; Mustafa Benboulaid University (Batna 2) Cherhabil , Sonia Sabrina; Mustafa Benboulaid University (Batna 2) Bendib , Maamar; Mustafa Benboulaid University (Batna 2) Sedrati , Chahrazad; Mustafa Benboulaid University (Batna 2) Adouane , Sifeddine; Mustafa Benboulaid University (Batna 2) Benflis
Offering a promising solution for latency-sensitive and resource-constrained internet of things (IoT) applications, mobile edge computing (MEC) extends cloud capabilities to the network edge. However, the decentralized nature of edge resources, coupled with stringent latency requirements and IoT energy constraints, presents significant challenges for efficient task offloading. Integrating IoT with MEC and software-defined networking (SDN) can meet the growing demands for low latency and energy-aware resource management. This paper proposes a hybrid evolutionary algorithm combining whale optimization algorithm (WOA) and particle swarm optimization (PSO) with crossover, mutation, and Lévy flight operators (CML) to balance exploration and exploitation. The algorithm minimizes a weighted sum function (energy 35%, delay 35%, and monetary cost 30%) for joint task offloading and resource allocation in SDN-enabled MEC environments. The proposed approach is evaluated against six well-known metaheuristics, analyzing performance across various metrics including scalability with up to 100 users. Experimental results, validated by non-parametric statistical tests, demonstrate that the proposed algorithm achieves statistically significant improvements in convergence speed, solution quality, and scalability, making it a robust and promising candidate for real-time MEC task scheduling.
Volume: 24
Issue: 2
Page: 514-526
Publish at: 2026-04-01

Transforming e-government projects by developing a RAF using Scrum integrated with CASE tool in Botswana

10.12928/telkomnika.v24i2.27431
Thapelo; North-West University Monageng , Bukohwo Michael; North-West University Esiefarienrhe
The digital transformation in Botswana has placed strong emphasis on e-government initiatives aimed at improving public service delivery. However, these projects continue to face low success rates due to challenges such as inadequate and reactive risk management practices, limited technical expertise, and fragmented implementation. This study proposes an integrated risk assessment framework (RAF) that combines Scrum methodology with computer-aided software engineering (CASE) tools that allows for the development of an automated, proactive, and iterative approach to risk management that is specific to the socioeconomic circumstance of Botswana. A quantitative survey was conducted with 32 project management specialists involved in e-government projects to assess their familiarity with agile methods and CASE tools, perceptions of traditional risk management approaches, and acceptance of the proposed model. The results revealed that 90.6% of respondents were familiar with Scrum, 78.1% had used CASE tools, and 81.25% supported the new framework, highlighting the urgent need for real-time risk tracking and continuous stakeholder engagement. The proposed e-government risk assessment framework (e-GRAF) model offers a flexible and adaptive solution to strengthen risk management processes, increase the success rate of e-government projects, and improve the quality and resilience of digital governance systems in Botswana.
Volume: 24
Issue: 2
Page: 466-480
Publish at: 2026-04-01

Smartphone data privacy and security awareness among university students in Malaysia

10.11591/ijece.v16i2.pp850-862
Ahmed Al-Rassas , Zaheera Zainal Abidin
This study examines the level of data privacy and security awareness (DPSA) among Malaysian university students who depend on smartphones for academic activities. An enhanced cybersecurity education (CE) technological proficiency–perceived control (CTP) model is proposed, incorporating technological innovation and cultural norms (TICN) as a mediating factor between technological proficiency (TP) and awareness. A total of 356 students from public and private institutions in Melaka participated. The Krejcie and Morgan table was used to determine the sample size. Descriptive analysis was conducted using IBM SPSS 27, and SmartPLS-SEM was used to evaluate both measurement and structural models. Reliability and validity were confirmed through a pilot study with 50 respondents. Findings show that TICN significantly strengthens the translation of technical skills into protective behavior, outperforming the original model that used frequency of smartphone usage (FSU) as a mediator. The enhanced model provides a deeper understanding of the socio-technical determinants of smartphone privacy awareness. Implications, limitations, and directions for future research are also discussed.
Volume: 16
Issue: 2
Page: 850-862
Publish at: 2026-04-01

Virtual decomposition with time delay control for underactuated robot manipulator

10.11591/ijece.v16i2.pp791-805
Imane Cheikh , Khaoula Oulidi Omali , Hachmia Faqihi , Mohammed Benbrahim , Mohammed Nabil Kabbaj
The importance of controlling robot manipulators is undeniable. However, faults in these systems can significantly impact the workspace environment and personal safety. To address these challenges, a new adaptive approach has been proposed that easily adapts to a faulty actuator while precisely tracking its desired position. The virtual decomposition control (VDC) method decomposes the robot into subsystems, each with its sub-controller, while ensuring the overall system remains stable. Meanwhile, time delay estimation (TDE) is used to estimate unknown and uncertain parameters. A co-simulation was conducted to test the TD-VDC method on a 6 DoFs robot, which becomes underactuated during its running. The results of the root main square error of the proposed controller were lower of 6% than those of sliding mode control based on partial feedback linearization control (SMC-PFLC), which proves the proposal's effectiveness and efficiency.
Volume: 16
Issue: 2
Page: 791-805
Publish at: 2026-04-01

Taxonomy of cooperative adaptation level for cooperative adaptive mobile applications

10.12928/telkomnika.v24i2.27542
Berhanyikun Amanuel; Addis Ababa University Gebreselassie , Nuno M.; University of Lisbon Garcia , Dida; Addis Ababa University Midekso
Adaptive mobile applications (AMAs) are software systems designed to dynamically adjust their behavior in response to contextual changes. When multiple AMAs coexist on the same device, they create an ecosystem of heterogeneous applications with distinct functionalities, interaction models, and sensor requirements. This diversity enables opportunities for cooperative adaptation, where applications synchronize their behavior for collective benefit. Building on prior work that identified cooperation as a key dimension of adaptive mobile systems, this study proposes a refined taxonomy of cooperation levels for AMAs. The taxonomy is validated through case studies and formal specification methods, demonstrating its theoretical soundness and practical applicability. The findings advance the understanding of cooperative adaptation mechanisms and provide structured guidance for designing and classifying cooperative AMAs.
Volume: 24
Issue: 2
Page: 500-513
Publish at: 2026-04-01

Hybrid classical–quantum ensemble learning for real-time flight delay prediction at Tribhuvan International Airport

10.12928/telkomnika.v24i2.27240
Pavan; Civil Aviation Authority of Nepal Khanal , Nanda Bikram; Tribhuvan University Adhikari
This study investigates ensemble learning using classical and quantum-inspired models to predict flight delays at Tribhuvan International Airport (TIA), Nepal. It combines traditional machine learning algorithms with quantum-based approaches, quantum boosting (QBoost) and the hybrid QBoostPlus, leveraging quantum properties for faster computation. The dataset includes flight records from 2020 to 2024 and Meteorological Aerodrome Reports (METAR), analyzed across four sea- sons to capture delay patterns in domestic and international flights. A combined seasonal dataset assesses model generalization. Six models; VotingClassifier, adaptive boosting (AdaBoost), xtreme gradient boosting (XGBoost), categorical boosting (CatBoost), QBoost, and QBoostPlus are evaluated based on accuracy, precision, recall, F1 score, area under the curve(AUC), and execution time. CatBoost achieved high accuracy (up to 0.97) but slower execution (up to 10,570.63 ms). QBoostPlus provides competitive AUC scores (0.83–0.95) with faster execution, improving speed by up to 99.94% and generating predictions in as little as 6.46 ms. Although quantum-inspired models have slightly lower accuracy, their computational efficiency and stability show strong potential for real-time flight delay prediction. This is the first study applying quantum-inspired ensemble learning to Nepalese aviation data, showing promise for regional airports with limited infrastructure.
Volume: 24
Issue: 2
Page: 527-535
Publish at: 2026-04-01

Design and evaluation of a low‑cost real‑time fluid-level monitoring system for fuel stations

10.12928/telkomnika.v24i2.27548
Jovianne; Université Catholique de Bukavu (UCB) Birindwa , Stéphane Birindwa; Université Catholique de Bukavu (UCB) Birhashwirwa
Accurate fluid level management in fuel stations is hampered by inventory errors, delayed shortage detection and costly proprietary sensors. We designed and built a low‑cost, open‑source monitoring system using an Arduino Uno, an HC‑SR04 ultrasonic sensor, a NodeMCU ESP8266 and a DHT11 temperature sensor. Validation was restricted to static short-term conditions, with a prototype tested in a 200 cm tank over 62 hours and 32 paired measurements collected at two-hour intervals. Prototype readings were compared with dipstick measurements after temperature compensation. The system achieved a mean error of 0.03 cm, a mean absolute error of 0.91 cm, a standard deviation of 1.06 cm and a root‑mean‑square error of 1.05 cm, with a 95 % confidence interval of ±0.37 cm. These results demonstrate that a calibrated and temperature‑compensated ultrasonic sensor can deliver centimetre‑level accuracy suitable for inventory management in resource‑constrained fuel stations. Future work will extend validation to dynamic transfers, sloshing/vibration, humidity effects, and long-term drift in operational tanks.
Volume: 24
Issue: 2
Page: 608-619
Publish at: 2026-04-01
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