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29,734 Article Results

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

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

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

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

Smart hydroponic greenhouse with solar energy for urban agriculture

10.12928/telkomnika.v24i2.27630
Zeluyvenca; Takumi Polytechnic Avista , Muhammad Asep; Takumi Polytechnic Rizkiawan , Yudha; Takumi Polytechnic Witanto
Increased industrial activity in South Cikarang has limited the availability of agricultural land, encouraging the adoption of controlled environment agriculture systems. This study describes the design and implementation of a smart hydroponic greenhouse that is fully supported by a 600 Wp solar photovoltaic (PV) system and controlled using an industrial-grade programmable logic controller (PLC). This system automatically regulates temperature and humidity through exhaust fans and sprayers based on real-time sensor feedback. Experimental results show that when the internal temperature exceeds 31 °C, the control system recovers to 29.7 °C within 15 minutes and maintains a temperature range of 24–30 °C. Relative humidity is maintained within the optimal range of 75–90%. The PV system produces an average daily energy output of approximately 2.0 kWh, resulting in an energy self-sufficiency ratio (ESR) of 138%, which indicates excess energy production compared to system demand. These results prove that the integration of industrial automation with renewable energy provides reliable environmental control, high energy efficiency, and operational stability for hydroponic greenhouse applications in urban industrial areas.
Volume: 24
Issue: 2
Page: 727-736
Publish at: 2026-04-01

Impact of electric vehicle demand forecasting on charging station infrastructure development

10.11591/ijece.v16i2.pp1010-1019
Chartrin Kronghinlad , Yuenyong Nilsiam , Nalinpat Bhumpenpein , Siranee Nuchitprasitchai , Sakchai Tangprasert
This research addresses the challenge of forecasting electric vehicle (EV) demand in Thailand and its influence on the development of charging infrastructure. To improve predictive capability in environments with restricted historical data, we employed the grey model (GM) and genetic algorithms (GA) both independently and in combination. Using EV registration records from 2019 to 2023 obtained from the Automotive Information Center of Thailand, the optimized GM-GA hybrid model achieved markedly superior accuracy, with a mean absolute error (MAE) of 0.0016 and root mean squared error (RMSE) of 0.0031. These results demonstrate the model’s capacity to deliver precise forecasts despite data limitations, making it a valuable decision-making tool for charging station planning and deployment. The outcomes underscore the importance of forward-looking infrastructure strategies to support the growth of Thailand’s EV market and its transition toward sustainable mobility.
Volume: 16
Issue: 2
Page: 1010-1019
Publish at: 2026-04-01

Cross-lingual semantic alignment and transfer learning using multilingual language models

10.11591/ijece.v16i2.pp973-980
Niranjan G C , Ramakanth Kumar P , Pavithra H , Minal Moharir
Multilingual language models (MLMs) are widely used for cross-lingual tasks, yet their ability to achieve consistent semantic alignment and transfer to low-resource languages remains limited. This work examines cross-lingual semantic alignment and transfer learning through a comparative evaluation of MLMs at both the word and sentence levels. We analyze general-purpose models such as BLOOM and task-specialized models including LaBSE and XLM-R across English, French, Hindi, and Kannada. Word-level experiments show that LaBSE achieves substantially higher cosine similarity scores of above 0.80 across languages. In sentence-level natural language inference, XLM-R outperforms other models, achieving an F1 score of 68.62% on Kannada and 74.81% on French. These results indicate that model specialization and training objectives play a crucial role in cross-lingual performance, particularly for low-resource languages, and should be carefully considered when deploying multilingual natural language processing (NLP) systems.
Volume: 16
Issue: 2
Page: 973-980
Publish at: 2026-04-01

Multi-objective optimization of distributed generation placement and sizing in active distribution networks considering harmonic distortion

10.11591/ijece.v16i2.pp598-607
Trieu Ngoc Ton , Phong Minh Le , Tan Minh Le
This paper presents a multi-objective optimization model for optimal placement and sizing of inverter-based distributed generation (DG) units in active distribution power systems (DPS), considering their impact on harmonic distortion. The model simultaneously minimizes total power losses and total harmonic distortion (THD), ensuring compliance with IEEE 519 standards. To solve this problem, the reptile search algorithm (RUN) is applied and compared with three metaheuristic algorithms: multi-objective particle swarm optimization (MOPSO), multi-objective grey wolf optimizer (MOGWO), and multi-objective whale optimization algorithm (MOWOA). Simulation results on IEEE 33-bus and 69-bus systems show that reptile search algorithm (RUN) reduces power losses by up to 6.1% and THD by 21.7% compared to MOPSO. Moreover, the results confirm a strong correlation between DG output power and harmonic amplitudes, highlighting the importance of power quality aware DG planning.
Volume: 16
Issue: 2
Page: 598-607
Publish at: 2026-04-01

An extensive review of islanding detection approaches in microgrids for distribution generations

10.11591/ijece.v16i2.pp608-618
Resna S. R. , Devi Vighneshwari B.
Microgrids integrated with distributed systems provide several benefits to the power grid, including faster detection times, superior power quality, and energy savings. Microgrids are managed using various methodologies in both grid-connected and island states. Microgrids must detect inadvertent islanding to protect individuals and prevent device damage. Monitoring and identifying magnitude anomalies are the foundation of the majority of islanding detection approaches (IDAs). This study summarizes the IDAs used in microgrids. An islanding fault is a microgrid that inadvertently disconnects from itself owing to a problem in the utility grid. A through categorization of IDAs is provided, with a focus on both local and remote approaches. Local IDAs can be further classified using passive, active, and hybrid methods. Furthermore, the power-quality effect, nondetection zone (NDZ), detection time (DT), and error detection rate (EDR) statistical comparison of the IDAs is examined. The benefits, drawbacks, and research gaps in the current work are evaluated. Lastly, challenges and recommendations for future research are highlighted.
Volume: 16
Issue: 2
Page: 608-618
Publish at: 2026-04-01

An energy-optimized A* algorithm for path planning of autonomous underwater vehicles in dynamic flow fields

10.11591/ijece.v16i2.pp753-765
Do Khac Tiep , Nguyen Van Tien , Cao Duc Thanh
This paper presents the development and implementation of an energy-optimized A* algorithm for autonomous underwater vehicle (AUV) path planning in these complex environments. The core of the approach is the integration of a computationally efficient flow field model and a detailed AUV energy consumption model directly into the A* search heuristic. The energy model considers factors such as drag forces, relative velocity between the AUV and the flow, and AUV maneuvering. The A* cost function is modified to prioritize paths that minimize the predicted total energy expenditure, while simultaneously ensuring obstacle avoidance and path feasibility. The algorithm was implemented and validated using a simulated environment with varying flow conditions. Results demonstrate that the proposed energy-optimized A* algorithm achieves a significant reduction in energy consumption – up to 50% in tested scenarios – compared to a standard A* implementation, while successfully generating collision-free and dynamically feasible paths. This work contributes a practical and effective solution for energy-aware AUV navigation in dynamic underwater environments, enabling longer mission durations and improved operational efficiency.
Volume: 16
Issue: 2
Page: 753-765
Publish at: 2026-04-01

Simultaneous faults diagnosis and prognostic in induction motor drives under nonstationary conditions

10.12928/telkomnika.v24i2.27624
Ameur Fethi; University Tahar Moulay of Saida Aimer , Ahmed Hamida; University of Sciences and Technology of Oran Boudinar , Mohamed El-Amine; University of Sciences and Technology of Oran Khodja , Azeddine; University of Sciences and Technology of Oran Bendiabdellah
In this paper, an auto regressive (AR) model-based approach is applied in the stator current analysis under non-stationary conditions (case of frequency variation due to variable speed operation). Under these conditions, the identification of fault signatures is almost impossible due the variation of the fundamental frequency using conventional analysis methods. Moreover, this approach is used in the diagnosis of multiple faults occurring simultaneously in induction motor drives. In this aim, the stator current signal is decomposed into short segments then the AR modeling approach is applied on each segment. This approach called short-time ROOT-AR is then applied to solve the problem of the non-stationarity of the stator current signal under variable speed operation. The efficiency of the short-time ROOT-AR approach is evaluated through experimental tests in the diagnosis of multiple faults occurring simultaneously in induction motor drive. Finally, the superiority of the proposed approach is highlighted in comparison with conventional techniques in terms of accuracy, computational time and robustness against the noise.
Volume: 24
Issue: 2
Page: 717-726
Publish at: 2026-04-01

Study of performance the 3-phase induction motor that drives by using scalar and vector control with variable speed loading

10.11591/ijeecs.v41.i3.pp894-911
Omran Alabedalkhamis , Baran Karahan , İbrahim İdiz , Hüseyin Alptekin , Enver Ediz Erol
Induction motor performance and efficiency greatly depend on the applied control technique, particularly in variable- and fixed-speed industrial applications. This paper aims to comparatively assess scalar control and vector control strategies for three-phase squirrel-cage induction motors. Using a simulation-based approach in MATLAB/Simulink, scalar control with sinusoidal pulse width modulation (SPWM) and vector control with space vector modulation (SVM) are built and analyzed under constant, variable, and bidirectional speed loading situations characteristic of a drive system. The results demonstrate that vector control provides greater speed regulation (about 93% compared to scalar control), reduced torque ripple (about 97% compared to scalar control), lower current stress (about 94% compared to scalar control), and improved dynamic responsiveness compared to scalar control, especially during transient operation. The paper is limited to numerical simulations. This paper’s biggest contribution is a clear, practical comparison which provides performance- and cost-oriented guidelines for selecting appropriate induction motor control strategies in severel applications.
Volume: 41
Issue: 3
Page: 894-911
Publish at: 2026-03-10

FGMPSO: a hybrid firefly-gradient-MOPSO framework for high-dimensional feature selection

10.11591/ijeecs.v41.i3.pp1082-1094
Alwatben Batoul Rashed
When working with high-dimensional datasets, selecting the most relevant features is essential for improving both model clarity and processing efficiency, all while keeping predictive accuracy intact. In response to this challenge, the study introduces firefly-gradient-multi-objective particle swarm optimization (FGMPSO), an advanced hybrid technique that blends the firefly algorithm, gradient descent (GD), and multi-objective particle swarm optimization (MOPSO). This approach is specifically designed to identify an optimal subset of features that balances dimensionality reduction with strong classification performance. The method was evaluated on eight benchmark datasets and compared against multiple PSO-based feature selection techniques. The empirical results demonstrated that FGMPSO consistently achieved superior or competitive classification accuracy while selecting significantly fewer features. Notably, in several datasets, FGMPSO not only reduced dimensionality but also outperformed other methods in terms of classification accuracy. This efficiency is attributed to the intelligent exploration of the search space by the firefly algorithm, refinement via GD, and effective trade-off optimization enabled by MOPSO. The findings suggest that FGMPSO is a robust and scalable solution for feature selection, particularly suitable for complex and high-dimensional datasets. Its adaptability, convergence speed, and balance between dimensionality reduction and accuracy position it as a valuable tool in modern machine learning pipelines.
Volume: 41
Issue: 3
Page: 1082-1094
Publish at: 2026-03-10
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