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31,207 Article Results

Comparative study of MPPT algorithm on photovoltaic string under partial shading

10.11591/ijape.v15.i3.pp1422-1438
Dikpride Despa , Gigih Forda Nama , Zulmiftahul Huda , Stefanus Debiarto Marudut Sagala
Partial shading significantly degrades the photovoltaic (PV) performance value by introducing multiple peaks in power voltage (P-V) curve, complicating maximum power point tracking (MPPT). This research aims to presents a systematic comparative study of 4 MPPT algorithms, that are i) perturb and observe (P&O), ii) incremental conductance (InC), iii) particle swarm optimization (PSO), and iv) flower pollination algorithm (FPA), under 6 systematically testbeds modeled irradiance scenarios. The evaluation focused on tracking accuracy, convergence speed, and also robustness against local maxima entrapment. The findings indicated that slope-based algorithms (P&O and InC algorithm) achieved rapid convergence (
Volume: 15
Issue: 3
Page: 1422-1438
Publish at: 2026-09-01

Effect of power and voltage variations on transformer core losses and geometry for two distinct core materials

10.11591/ijape.v15.i3.pp995-1008
Kamran Dawood , Furkan Gezer , Güven Kömürgöz Kırış , Semih Tursun
This study presents a comprehensive comparison of transformer core size and no-load losses, focusing on transformers with power ratings ranging from 400 kVA to 3200 kVA. The research evaluates transformer performance at three distinct primary voltage levels: 6 kV, 15 kV, and 33 kV, while maintaining a constant secondary voltage of 0.4 kV. Additionally, the study investigates the impact of two commonly used transformer core materials, M4 and H0, on core design, with a particular focus on their effects on no-load losses. Another central aspect of the analysis is the examination of core geometry, including the cross-sectional area and height of the transformer core, across various power ratings and voltage levels. The results reveal a clear relationship between core size and energy efficiency. M4 cores exhibit higher no-load losses compared to H0 cores; additionally, H0 cores demonstrate better overall efficiency, making them ideal for high-efficiency applications. The findings underscore the importance of selecting the right core materials and geometries to balance performance, efficiency, and size. These insights are intended to guide researchers and transformer designers in making informed decisions when developing energy-efficient solutions for diverse applications across the electrical power industry.
Volume: 15
Issue: 3
Page: 995-1008
Publish at: 2026-09-01

Performance analysis of multi carrier PWM techniques for a 5-phase three level NPC inverter in EV applications

10.11591/ijape.v15.i3.pp1264-1274
Venu Yarlagadda , N. Kavitha , Chava Sunil Kumar , G. Naveen , Swetha Mareddy , S. Venkata Rami Reddy , K. V. Govardhan Rao
Multi-phase multilevel inverters have become more popular in contemporary applications due to their many benefits, which include lower switching losses, decreased common mode voltage, and reduced stress from voltage on switches. This study focuses on enhancing total harmonic distortion (THD) performance in a five-phase multilevel neutral point clamped (NPC) inverter using various multi-carrier pulse width modulation (PWM) techniques, including PD, POD, APOD, IC, PSC, and VFC. These approaches are particularly suitable for electric vehicle and industrial motor applications. Several PWM approaches were used in the SIMULINK environment to model and simulate a 5-phase, 3-level NPC inverter. In this study, the performance of load voltage THD is compared using R and RL loads connected to a multilevel inverter. Additionally, 5-phase induction motor and permanent magnet synchronous motor models are developed as loads for electric vehicle applications, and variations in torque, speed, and stator current are analyzed. The PD modulation technique showed the lowest THD among the various PWM methods, demonstrating its effectiveness in maximizing inverter performance.
Volume: 15
Issue: 3
Page: 1264-1274
Publish at: 2026-09-01

Modeling and implementation of a dual-mode emulator for line distance protection based on minimum line reactance

10.11591/ijape.v15.i3.pp1327-1339
Yassine El Asri , Abdellah Lassioui , Hassan El Fadil , Anwar Hasni , Marouane El Ancary , Hafsa Abbade
Advances in power system protection and the increasing complexity of electrical networks have created a growing need for flexible and cost-effective platforms for testing and validating protection algorithms. However, academic laboratories still face a lack of accessible experimental platforms allowing researchers to implement and evaluate new protection strategies under realistic conditions. This gap is becoming increasingly significant with the emergence of artificial intelligence and data-driven techniques, which require flexible environments for development, testing, and experimental validation. To address this limitation, this work presents the modeling and implementation of a dual-mode emulator for minimum reactance distance protection of overhead transmission lines, operating in both real-time and offline modes. The proposed system acquires and processes voltage and current signals to determine the minimum line reactance used for fault detection and distance estimation. The developed algorithm is evaluated through simulated fault scenarios under different operating conditions. Results demonstrate reliable fault detection and consistent fault-distance estimation. The dual-mode architecture enables both offline analysis of recorded signals and real-time algorithm evaluation. The proposed emulator therefore provides a practical, low-cost academic platform for research, training, and experimental validation of conventional and emerging protection strategies.
Volume: 15
Issue: 3
Page: 1327-1339
Publish at: 2026-09-01

QSUMeMarket: a decision support system framework for smes to process customer orders

10.11591/ijict.v15i3.pp975-985
Winston G. Domingo , Jennifer A. Gamay , Virdi C. Gonzales , Selino S. Malunao
With the progression of big data analytics (BDA), point of sale (PoS) could be amalgamated with an inventory management (IM) system. The problem of SMEs is to generate a financial report only using simple calculations based on income and expenses. To develop a web-based computerized system to solve the problems encountered and ease the marketing office’s operations. The used of a descriptive research design and software development life cycle (SDLC) methodology. The ISO/EIC 25010:2011 software quality standard was chosen as one of the most comprehensive software-quality evaluation models. The software performs exceptionally well in several key areas, including performance efficiency (4.00), usability (3.94), portability (3.89), compatibility (3.83), security (3.80), maintainability (3.70), and reliability (3.67). These ratings highlight its strengths in speed, security, and overall user experience. However, with the lowest score being 3.44, there is still potential for improvement to better align the software with user needs and business objectives. While it meets industry standards and is suitable for practical use, ongoing enhancements are essential to sustain and elevate its quality.
Volume: 15
Issue: 3
Page: 975-985
Publish at: 2026-09-01

Multi-objective planning of distributed resources (PV and SVC) with NSGA-II for radial networks: application to the IEEE 33-bus test system

10.11591/ijape.v15.i3.pp1243-1252
Hassane Ousseyni Ibrahim , Abdoul Malik Maman Issaka , Moussa Gonda , Arouna Oloulade , François-Xavier Fifatin
The quality of electricity supply in distribution networks is critically dependent on minimizing active power losses and ensuring voltage stability. This study proposes a unified multi-objective optimization approach for the simultaneous placement and sizing of a photovoltaic (PV) source and a static var compensator (SVC) in radial networks. The non-dominated sorting genetic algorithm II (NSGA-II) is employed as the robust methodology to generate the Pareto optimal front, effectively exploring the trade-offs between two conflicting objectives: active loss minimization and voltage profile improvement. Unlike sequential or single-unit optimization strategies, this joint optimization framework is the key novelty, leveraging the specific physical interaction between PV active power injection and SVC-based dynamic reactive support to maximize overall network efficiency. Simulations are performed on the standard IEEE 33-bus test system. The results demonstrate that the optimal and coordinated integration of a 0.97 MW PV system at bus 14 and a 1.32 MVAr SVC at bus 30 yields superior electrical performance. Specifically, the system achieves a substantial active power loss reduction of 62.53% and decreases the voltage deviation index from 0.117 p.u. to a minimum of 0.0169 p.u., confirming the effectiveness of the proposed NSGA-II approach for comprehensive distributed resource planning.
Volume: 15
Issue: 3
Page: 1243-1252
Publish at: 2026-09-01

Integrating F-filter and K-Means SMOTE to enhance LGWUM-based turnover prediction

10.11591/ijict.v15i3.pp1078-1086
Risyda Miftahur Rahmah , Sigit Priyanta
High employee turnover poses a significant challenge for organizations. While uplift modeling offers a prescriptive analytics approach by estimating differential treatment effects to optimize retention programs, its performance is often hindered by irrelevant features and imbalanced class distributions. To address these issues, this study proposes an employee turnover model utilizing lai’s generalized weighted uplift method (LGWUM), enhanced with F-Filter feature selection and K-Means SMOTE for a refined feature space and balanced treatment representation. Evaluated across three HR datasets with four engineered uplift classes (CN, CR, TN, TR), the integrated framework significantly improves uplift performance, yielding increased Qini coefficients of 0.0755 and 0.0870 on Datasets 2 and 3, respectively. Furthermore, top-decile probability distribution analysis confirms a clearer separation between positive and negative responders, with XGBoost demonstrating the most robust and reliable uplift discrimination across the models.
Volume: 15
Issue: 3
Page: 1078-1086
Publish at: 2026-09-01

Design a Gaussian mixture-based clustering model for enhancing accuracy and robustness in smart homes

10.11591/ijict.v15i3.pp1047-1057
Kanaka Raju Rajana , Shanmuk Srinivas Amiripalli
Nowadays, smart homes have become quite complicated systems. Thus, an appropriate technique for controlling all those devices is necessary, especially considering that certain nodes are likely to be broken. In that connection, we have proposed two algorithms related to Gaussian mixture models (GMM): GMM equal and GMM unequal. They were compared with graph neural network (GNN) equal, GNN unequal, and the LucasWheel algorithms. The peculiarity of the GMM equal algorithm consists in the fact that all clusters should have similar sizes and shapes, which is quite useful for routing and balancing purposes, while the clusters in the GMM unequal algorithm can have various sizes and shapes depending on the data distribution. All five models were analyzed using 843 nodes, where failure rates ranged from zero to fifty percent. The surprising outcome of this analysis is that GMM equal performed better than the other four models in every aspect. Efficiency was steady and steadily increased in accordance with the rising failure rate. The Wiener index gradually fell from its initial value to nearly zero, suggesting a dense connection among the nodes and an evenly spread-out network. Furthermore, GMM equal attained the highest modularity among the five models at every failure level. In combination, these results indicate that GMM equal is the most balanced topology, with the best balance between reliability, efficient communication, and scalability when applied to the internet of things and smart homes.
Volume: 15
Issue: 3
Page: 1047-1057
Publish at: 2026-09-01

Condition assessment of medium voltage cable insulation using leakage current and phase-resolved partial discharge

10.11591/ijape.v15.i3.pp1340-1350
Kamrai Janprom , Sittadach Morkmechai , Natchanun Prainetr , Supachai Prainetr
Reliable operation of medium-voltage distribution networks critically depends on the integrity of cross-linked polyethylene (XLPE) insulated cables. This paper proposes a diagnostic methodology that integrates leakage current (LC) measurement with phase-resolved partial discharge (PRPD) analysis to assess cable insulation condition. A MATLAB R2025 simulation model is first developed to emulate partial discharge (PD) signals superimposed on leakage current, providing preliminary validation of the proposed approach. The method is then experimentally verified using a 70 mm² XLPE cable rated at 16/20 (24) kV and tested in accordance with IEC 60502. Under applied high-voltage stress, leakage current and PD activity are measured, and insulation degradation is characterized using PRPD patterns. Both simulation and experimental results confirm that the proposed method reliably detects insulation defects and provides accurate condition assessment of XLPE cables. These findings demonstrate the potential of the method as a practical tool for supporting condition-based maintenance in medium-voltage power distribution systems.
Volume: 15
Issue: 3
Page: 1340-1350
Publish at: 2026-09-01

Cross-modal attention fusion using vision transformers for robust student attentiveness estimation

10.11591/ijict.v15i3.pp935-943
Rajasekaran Mariswamy , Praveen Sundar
Automated student attentiveness estimation is a fundamental component of intelligent e-learning systems and adaptive classroom analytics. Traditional convolutional and recurrent architectures often struggle to model long-range temporal dependencies and complex inter-modal relationships inherent in engagement behavior. To address these limitations, this paper proposes a cross-modal attention fusion framework built upon a vision transformer (ViT) backbone for robust student attentiveness estimation. The proposed architecture leverages patch-based visual encoding through a ViT to capture global spatial dependencies, while behavioral cues such as gaze direction, head pose, and blink dynamics are embedded into a shared latent representation space. A cross-modal multi-head attention mechanism is introduced to dynamically learn interactions between visual and behavioral modalities, replacing static weighted fusion strategies. Temporal dynamics are modeled using a Transformer encoder, enabling effective long-range sequence modeling without recurrent dependencies. Experimental evaluation on a benchmark attentiveness dataset demonstrates superior performance compared to CNN–LSTM-based models, achieving improved accuracy, F1 score, and robustness under challenging lighting and occlusion conditions. Ablation studies validate the contribution of cross-modal attention and transformer-based temporal modeling. The proposed framework maintains real-time feasibility while significantly enhancing discriminative capability.
Volume: 15
Issue: 3
Page: 935-943
Publish at: 2026-09-01

Development of highway vehicle detection using background subtraction and Haar cascade methods

10.11591/ijict.v15i3.pp1004-1015
Ni Gusti Ayu Dasriani , Anthony Anggrawan , Khasnur Hidjah , Christofer Satria , I Nyoman Yoga Sumadewa
Vehicle recognition is a critical component of traffic analysis and the progress of advanced transportation systems, underscoring the importance of automated, real-time methods that reduce the need for manual observation. While the field has seen notable innovations in deep learning-centric detection technologies, many of these approaches require considerable computational strength and are not well-suited for real-time application in resource-constrained environments. In response to this limitation, the present study introduces a streamlined vehicle detection framework that combines background subtraction for motion-oriented foreground extraction with a Haar cascade classifier for object identification in traffic video sequences. The system is evaluated using real-world highway traffic recordings under different illumination conditions, including both day and night scenarios. The experiment's findings show that the system achieves an overall accuracy of 82.08%, with a precision of 85.33%, a recall of 66.67%, and an F1-score of 74.86%. The system also demonstrates consistent performance across different lighting conditions. These findings indicate a trade-off between detection accuracy and computational efficiency, where the proposed approach prioritizes practical deployment feasibility. Overall, the results suggest that classical computer vision techniques remain viable alternatives for real-time traffic monitoring in environments with limited computational resources.
Volume: 15
Issue: 3
Page: 1004-1015
Publish at: 2026-09-01

Deep reinforcement learning inspired optimization framework using Optuna for brain tumor detection

10.11591/ijict.v15i3.pp1352-1363
Aashutosh Kharb , Prachi Chaudhary
Accurate brain tumor detection is essential for effective clinical diagnosis; however, the performance of deep learning models is highly sensitive to manually selected architectures and hyperparameters. To address this challenge, this paper presents a reinforcement learning–inspired automated optimization framework for brain tumor detection that eliminates manual trial-and-error tuning of hyperparameters. The proposed approach integrates EfficientNetB0 as a fixed feature extractor (base model) with an Optuna-based reinforcement learning strategy to jointly optimize the classifier architecture and key training hyperparameters, including learning rate, batch size, dropout rate, and network depth. Unlike existing studies that rely on static or heuristically tuned models, the proposed framework dynamically adapts model configurations based on validation feedback. Experiments conducted on the BraTS 2020 MRI dataset demonstrate that the optimized model achieves an accuracy of 92%, an F1-score of 92%, and a ROC–AUC of 0.96. Additional evaluations on imbalanced and cross-dataset settings show stable minority-class performance and good generalization. The results confirm that the proposed automated optimization framework offers a robust, scalable, and clinically relevant solution for brain tumor detection, representing a significant advancement over manually tuned deep learning approaches.
Volume: 15
Issue: 3
Page: 1352-1363
Publish at: 2026-09-01

Digital forensics for cultural preservation: multi-device image classification of the historic Surabaya City Hall

10.11591/ijict.v15i3.pp995-1003
Ulfa Meilinda Putri , Imam Yuadi
Cultural heritage preservation increasingly relies on digital forensics to ensure authenticity and consistency in heritage documentation. This study presents a digital forensic framework based on machine learning for classifying multi-device images of the historic Surabaya City Hall. The dataset was collected from nine smartphone devices and preprocessed through standardization, 360° rotational augmentation, and three filtering methods: gaussian, median, and laplacian. Three supervised algorithms (support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression (LR)) were evaluated using accuracy, macro average, and weighted average of precision, recall, and F1-score. The results indicate that image preprocessing substantially affects model performance, with the gaussian-filtered KNN achieving the best result, reaching 92% accuracy, and balanced macro and weighted F1-scores of 0.92-0.93. Confusion-matrix analysis revealed minor misclassifications among iPhone models with similar sensor characteristics, while other devices were accurately identified. The findings confirm that gaussian filtering improves feature consistency and that KNN’s distance-based classification exhibits robustness across heterogeneous image sources. However, the study is limited to a single heritage object and a restricted number of devices, which may affect generalizability. The proposed framework provides a reproducible and interpretable method that supports digital authenticity verification and aligns with UNESCO’s vision for open, transparent cultural heritage preservation.
Volume: 15
Issue: 3
Page: 995-1003
Publish at: 2026-09-01

Design of a pyramidal slotted wide-band patch antenna for directive wireless communications over Ka-Band

10.11591/ijict.v15i3.pp1135-1142
Safa Nasssr Nafea
A wide Band for directive wireless communications over Ka-Band was presented. The proposed antenna printed on Rogers RT/Duroid 5880 substrate and achieved a moderate gain of 6.80 dB at resonating freqenc of 28 GHz. The pyramidal antenna achieved a wide operating bandwidth of 11.55 GHz. The proposed antenna achieved high size reduction percentage with a compact overall dimension of (4.8×6.9×1.5) mm3. The antenna achieved the wide operating bandwidth based on two factors; the first is selection of low loss dielectric material to ensure etter antennas performance. The other is etching pyramidal with 2×2 squared – shapes slots array etched from patch’s surface to reduce stored reactive energy which decreases overall ualit factor of and improves the -10 dB bandwidth obviously.
Volume: 15
Issue: 3
Page: 1135-1142
Publish at: 2026-09-01

Classroom behavior mining in adolescents: a cognitive and data-driven approach using BEHAVE_Apriori

10.11591/ijict.v15i3.pp1058-1065
Suresh Govindarajalu , Muthukumaran Subramaniyan , Kamatchy Balakrishnan , Kalaichelvi Nagarajan , Nandhini Krishnamoorthy
Adolescence is a critical developmental stage that leads to essential changes in social, emotional, and cognitive domains that affect conduct in the classroom. Students' perceptions, processing, and reactions to their learning environment are better-understood thanks to cognitive psychology. However, contemporary data mining techniques frequently ignore the environmental, emotional, and cognitive elements influencing teenage behavior in learning environments. This research presents a comprehensive approach to analyzing teenage college students' classroom behavior by integrating cognitive psychology with data-driven methods to identify key behavioral traits shaped by both external and internal factors. A brand-new algorithm called the behavioral evaluation via hybrid attributes and valuable extraction using the Apriori (BEHAVE_Apriori) approach is presented. Also, a variety of feature selection (FS) strategies, including information gain (IG), chi-squared (CS), and tree-based approaches, are used for FS. Then, using the Apriori algorithm, association rules are found that relate behavior patterns to elements like family history, academic involvement, and peer influence. The IG-based FS combined with the Apriori algorithm delivered the best performance, generating 95 rules in 0.0241 seconds, outperforming CS (154 rules, 0.0629s) and tree-based FS (251 rules, 0.1394s), while the unfiltered dataset produced 514 rules in 0.2853 seconds.
Volume: 15
Issue: 3
Page: 1058-1065
Publish at: 2026-09-01
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