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30,735 Article Results

Edge-aware coffee aroma classification using multi-representation feature extraction and LightGBM on Jetson Nano

10.11591/ijece.v16i4.pp2096-2105
Denda Dewatama , Erni Yudaningtyas , Muhammad Fauzan Edy Purnomo , Setyawan Purnomo Sakti
Objective coffee aroma evaluation remains challenging outside controlled laboratory settings, and most electronic nose studies neglect embedded deployment constraints. This work proposes an edge-aware coffee aroma classification framework that integrates multi-representation feature extraction with LightGBM and evaluates both predictive performance and computational efficiency. A six-sensor metal-oxide semiconductor (MOS) e-nose was developed, producing a balanced dataset of 1,080 trials from 12 aroma classes. Five feature representations were investigated, including baseline signals, autoencoder embeddings, and convolutional features derived from pseudo-image transformation. Experiments on an NVIDIA Jetson Nano using stratified five-fold cross-validation showed that residual-based representations significantly improved performance. The lightweight residual network achieved an accuracy of 0.9972 with low training time and memory usage. Pareto analysis confirms that optimal performance is achieved by balancing accuracy and resource constraints, thereby enabling reliable deployment in edge and IoT environments.
Volume: 16
Issue: 4
Page: 2096-2105
Publish at: 2026-08-01

Scenario-driven fault injection for realistic bugs in web application testing

10.11591/ijece.v16i4.pp2014-2030
Asri Maspupah , Joe Lian Min , Yadhi Aditya
Conventional mutation-based fault injection techniques generally produce single-line syntactic faults, which often fail to represent realistic errors at the functional requirement level because requirement context, execution paths, and functional dependencies are not considered. To address this limitation, this study proposes scenario-driven fault injection (SDFI), a scenario-based fault insertion approach that derives faults from functional requirements and test cases. SDFI integrates operational fault localization, web fault taxonomy, fault injection patterns, and functional scenario mapping to produce targeted fault injections at relevant code locations, resulting in a realistic bug dataset with multi-line faults. An experimental evaluation on a real web application produced 29 mutants, achieving a fault detection rate of 89.29% based on the RIP model. Further analysis shows that the generated mutants replicate common real-world bug characteristics, including logic errors, validation anomalies, inter-function data propagation, and multi-line faults affecting client–server application behavior. These results demonstrate that SDFI is effective in producing realistic bug datasets for evaluating software testing quality, improving test case effectiveness, and supporting further research on requirement-based fault realism.
Volume: 16
Issue: 4
Page: 2014-2030
Publish at: 2026-08-01

Integrating principal component analysis in spatial-spectral fusion models for hyperspectral image segmentation

10.11591/ijece.v16i4.pp2074-2086
Alexander Calvin , Laksmita Rahadianti
Hyperspectral imaging (HSI) from unmanned aerial vehicles (UAVs) provides rich spatial-spectral data, but its high dimensionality presents significant computational challenges for semantic segmentation. While state-of-the-art models like the transformer-based HSI-TransUnet are often employed, they introduce massive computational overhead. This study adapts a lightweight, dual-tunnel deep convolutional neural network (DCNN) framework for land-use segmentation on hyperspectral images by integrating PCA-based spatial reduction in the spatial branch, and benchmarks it on the UAV-HSI-Crop dataset against HSI- TransUnet. For further analysis, an ablation study compares principal component analysis (PCA) and local similarity projection (LSP) as spatial feature ex- tractors. The results demonstrate a significant performance and efficiency advantage. Our proposed PCA-based model (271.1K parameters) obtained a Kappa (κ) of 0.8582, overall accuracy (OA) of 0.8800, and average accuracy (AA) of 0.4918, outperforming the LSP-based model by 0.65% in κ, 0.51% in OA, and 2.16% in AA and the HSI-TransUnet baseline by 2.35% in κ, 1.95% in OA, and 8.10% in AA. On our experimental setup, this result was achieved with a 152.7-fold reduction in model size, a 14.2-fold decrease in training time, and a 4.6-fold speedup in inference relative to the reported HSI-TransUnet baseline. These findings show that the PCA-based dual-tunnel DCNN provides a favor- able trade-off between class-balanced accuracy and computational efficiency for this HSI segmentation task.
Volume: 16
Issue: 4
Page: 2074-2086
Publish at: 2026-08-01

Multiobjective framework for congestion management through coordinated scheduling of generation and demand

10.11591/ijece.v16i4.pp1688-1703
Jayesh Priolkar , Govind Kunkolienkar
The safe and secure operation of power system networks remains a significant challenge due to the ever-increasing demand for electrical energy. In deregulated environments, there is a strong emphasis on the optimal and efficient utilization of existing resources. This work aims to address line congestion by optimally re-dispatching generation resources and proactively managing demand through advanced demand response (DR) programs. An elasticity based, multi-period load model is employed to enhance the realism and effectiveness of DR strategies. The novelty of the proposed work is the holistic approach that simultaneously addresses economic, environmental, and technical objectives, incorporating realistic DR behavior and the advanced modified elephant herding optimization (MEHO) technique. This work proposes a MEHO algorithm for multi-objective congestion management with coordinated generation and DR programs, with comparative analysis against MPSO on both IEEE 30-bus and IEEE 118-bus systems. The MEHO algorithm generates seven unique Pareto-optimal solutions that represent various trade-offs between the conflicting objectives, demonstrating the implementation's remarkable performance on the IEEE 30-bus and IEEE 118-bus test system. MEHO achieves 3.5 to 6.2% better cost solutions, 2.3 to 5.2% lower emissions, and 60 to 62.5% better congestion indices across both test systems.
Volume: 16
Issue: 4
Page: 1688-1703
Publish at: 2026-08-01

Performance analysis of 5G NR network planning at 2300 MHz in Indonesia: A comparative study of LoS and NLoS scenarios

10.11591/ijece.v16i4.pp1944-1954
Putri Rahmawati , Lia Hafiza , Muhammad Adam Nugraha , Syifa Maliah Rachmawati
This study presents a systematic comparative analysis of 5G new radio (NR) network planning at 2300 MHz for Bandung City, Indonesia, evaluating line-of-sight (LoS) and non-line-of-sight (NLoS) propagation scenarios to determine infrastructure requirements and performance characteristics for urban deployment. Employing the 3GPP TR 38.901 Urban Macro propagation model, link-budget analysis, and Atoll-based simulation for a 167.31 km² urban area, the study evaluates coverage performance under projected 2025 deployment conditions. The results reveal significant differences between propagation scenarios. LoS conditions require 45 gNodeBs for uplink coverage, achieving a synchronization signal reference signal received power (SS-RSRP) of -94.63 dBm and a synchronization signal signal-to-interference-plus-noise ratio (SS-SINR) of 10.87 dB, both categorized as “Good.” In contrast, the NLoS scenario requires substantially denser deployment with 635 gNodeBs, resulting in improved SS-RSRP performance of -71.98 dBm (“Excellent”) and SS-SINR of 12.32 dB (“Good”), along with more uniform coverage distribution. The findings indicate that improved KPI performance and coverage uniformity in NLoS environments can be achieved through substantially increased infrastructure density, highlighting the trade-off between network quality, deployment complexity, and infrastructure cost in urban 5G NR planning.
Volume: 16
Issue: 4
Page: 1944-1954
Publish at: 2026-08-01

Lightweight face recognition based on PCA coupled with PSO-based feature optimization in resource-constrained environments

10.11591/ijece.v16i4.pp2134-2157
Chaimaa Khoudda , Zineb Gotti , Salma Azzouzi , Moulay El Hassan Charaf
We present a streamlined PCA-PSO structure of lightweight face recognition in this paper, which combines principal component analysis (PCA) to reduce dimensions with particle swarm optimization (PSO) to adaptively select features. In contrast to traditional PCA-based models, our model dynamically chooses the most informative subset of the 44 features and results in a small but informative representation. The approach has a high recognition accuracy of 99.99 on ORL and 98.45 on LFW, with low latency (approximately 3 seconds per epoch) and low memory footprint, and without the need to use a graphic card to run it. Extensive computational studies have shown that the proposed pipeline is much faster and consumes less memory than PCA-ACO and lightweight convolutional neural network (CNN) models like MobileNetV2 and Squeeze Net and is best suited to run in real-time in CPU-limited environments. The statistical tests prove the betterment of our method compared to the past methods, where the changes are statistically significant. This research offers an efficient, simple, yet scalable alternative to deep learning-based recognition systems, especially when embedded integration is a key requirement, by offering rapid convergence, adaptive feature selection, and compact representation.
Volume: 16
Issue: 4
Page: 2134-2157
Publish at: 2026-08-01

A new diagnostic method based on support vector machine for short circuit winding faults in induction motors

10.11591/ijece.v16i4.pp1735-1754
Hicham Zaimen , Tawfik Thelaidjia , Makhlouf Chouki , Abdurrahman Ünsal
Inter-turn short circuits (ITSCs) in induction motor (IM) windings are among the most critical and frequent faults in industrial environments, as they can rapidly evolve into severe damage, leading to unplanned downtime and costly maintenance. To enhance the reliability of IMs, this paper proposes a machine learning–based diagnosis method dedicated to ITSC failures. The developed diagnostic tool combines a support vector machine (SVM) classifier with Fisher’s ratio (FR)-based feature selection. The proposed framework uses experimentally acquired current signals under healthy conditions and five ITSC fault severity levels (1%–5%), evaluated across four load conditions (25%, 50%, 75%, and 100%). Each signal is segmented into 200 non-overlapping segments (500 samples each), from which nine time-domain features are extracted to capture fault-related characteristics. These features are then used for training and testing a SVM classifier capable of distinguishing between healthy states and levels of severity of ITSC faults. To optimize the classification process, the Fisher’s ratio (FR) algorithm is employed to select the most informative features while discarding those with low relevance. Our findings unveiled that the proposed hybrid FR-SVM-based diagnosis achieves high diagnostic accuracy ranging from 99.54% to 100%. Furthermore, the outcomes prove that the integrated technical framework (time-domain features + FR + SVM) provides zero false alarms and a balanced diagnostic system that combines computational speed with high precision.
Volume: 16
Issue: 4
Page: 1735-1754
Publish at: 2026-08-01

Remaining useful life estimation for predictive battery maintenance with improved recurrent singular spectrum analysis algorithm

10.11591/ijece.v16i4.pp1817-1831
Chutipongse Boonyakitmaitree , Suchada Sitjongsataporn
As the global electric vehicle (EV) battery market is projected to reach a valuation of over USD 100 billion by the end of 2026, the demand for sophisticated battery management systems (BMS) has become more critical than ever. Accurate remaining useful life (RUL) prediction is essential for ensuring vehicle safety, optimizing maintenance, and evaluating retired batteries for second-life applications. However, existing prognostic methods often struggle to balance computational efficiency with predictive accuracy, especially during the early stages of battery usage. This research proposes combined weighted similarity-based and recurrent singular spectrum analysis (CWS-RSSA), a hybrid forecasting framework that integrates RSSA with a similarity-based approach through a weighted logistic switching mechanism. The algorithm is designed to be computationally lightweight, making it suitable for resource-constrained BMS hardware. The proposed method was validated using NASA and a large-scale dataset from MIT-Stanford consisting of 124 lithium-ion cells. Experimental results demonstrate that CWS-RSSA is capable of early-stage prediction with a relative error of 19.8%, whereas existing methods are unable to provide predictions. In later stages, once sufficient data becomes available, the algorithm achieves near-perfect accuracy with a negligible relative error on the NASA dataset and an average relative error of only 0.14% across the 124 MIT-Stanford batteries. Furthermore, the algorithm demonstrates robust performance in handling capacity regeneration phenomena. These findings suggest that CWS-RSSA represents a scalable and practical advancement for battery health management, supporting the transition toward a sustainable circular energy economy and providing a reliable foundation for second-life battery certification.
Volume: 16
Issue: 4
Page: 1817-1831
Publish at: 2026-08-01

ChatGPT as scaffold: quiz performance across session complexity

10.11591/ijere.v15i4.39915
Fatima Ezzahra Kabba , Zouhair Ejbari
Many studies examine the use of ChatGPT in education, but most measure student perceptions, not performance, and few track performance across multiple sessions of different complexity. In particular, no study has tracked whether this association varies across sessions of different cognitive complexity. Using a quasi-experimental design, first-year undergraduates (N=193) at the Higher International Institute of Tourism (ISITT) in Tangier, Morocco were followed across seven introductory statistics sessions. One group had access to ChatGPT during learning activities, while the other followed the same instruction without artificial intelligence (AI) access. Performance was measured through end-of-session quizzes (1,136 observations) and analyzed using a linear mixed-effects model. No consistent overall advantage was associated with either condition. However, a significant interaction between condition and session was identified (χ²(6)=61.50, p
Volume: 15
Issue: 4
Page: 3172-3181
Publish at: 2026-08-01

Online counseling competences among Thai secondary school advisory teachers: a confirmatory factor analysis

10.11591/ijere.v15i4.34738
Bovornpot Choompunuch , Dussadee Lebkhao , Wipanee Suk-Erb , Phamornpun Yurayat
The increasing reliance on digital technology in education, accelerated by the COVID-19 pandemic, highlights the urgent need to understand online counseling competencies among secondary school advisory teachers. However, limited empirical evidence exists regarding the structure of these competencies in the Thai educational context. This study aimed to examine the components and validate a model of online counseling competences among secondary school advisory-teachers in Thailand. A total of 400 teachers from the Northeastern region were selected using multistage stratified random sampling. Data were collected through self-reported questionnaires and analyzed using second-order confirmatory factor analysis (CFA). The findings indicate that online counseling competences comprises three components: adaptive online counseling proficiency (AOC), school online counseling ethics (SCE), and secure comprehensive technology-integrated online counseling systems (SCS). The model demonstrated strong fit indices (χ2=76.240, df=70, p-value=0.284, χ2/df=1.089; root mean square error of approximation (RMSEA)=0.015; standardized root mean square residual (SRMR)=0.029; normed fit index (NFI)=0.990; comparative fit index (CFI)=1.000). This model provides a framework to guide training and policy development aimed at enhancing the effectiveness of online counseling services in Thai secondary schools.
Volume: 15
Issue: 4
Page: 2729-2740
Publish at: 2026-08-01

Motivational and normative drivers of generative AI substitution in academic work: a mixed-methods study from Saudi higher education

10.11591/ijere.v15i4.38745
Mazin Mansory , Zilal Meccawy
The rapid integration of generative artificial intelligence (GenAI) in higher education has intensified tensions between legitimate learning support and unauthorized task substitution, particularly where institutional guidance remains ambiguous. This mixed-methods study investigates how attitudes toward AI, moral rationalization strategies, and perceived institutional clarity interact to shape AI-based substitution behavior among 249 undergraduates at a Saudi university. Partial least squares structural equation modeling (PLS-SEM) revealed that positive attitudes and rationalization together explained 46% of the variance in substitution behavior, with perceived clarity of institutional guidance significantly moderating the rationalization–substitution link. Complementary interviews with seven students and ten instructors revealed that linguistic burden, peer norms, and fragmented faculty guidance facilitated boundary crossing from scaffolding to shortcutting. By exploring the relationship between moral neutralization and environmental clarity, this research offers a new approach to evaluating the effectiveness of institutional AI guidance beyond common technology acceptance models. The findings can be used to inform the design of multi-tiered, inclusive AI usage policies, assessments that value process over product, and culturally responsive academic integrity education within a multilingual higher education context.
Volume: 15
Issue: 4
Page: 2946-2958
Publish at: 2026-08-01

SS-ANFIS: a semi-supervised neuro-fuzzy model for offline signature verification

10.11591/ijece.v16i4.pp1985-1997
Sadly Syamsuddin , Jufri Jufri , Suci Rahma Dani Rachman , Suryani Suryani , Wilem Musu , Salmiati Salmiati , Yesycha Arun Mangopo
Signature verification remains a critical authentication mechanism in academic and administrative environments, yet manual verification is vulnerable to forgery and subjective judgment. This study proposes SS- ANFIS, a semi-supervised neuro-fuzzy model for offline signature verification under limited labeled data conditions. The proposed model integrates pseudo-label-based self-training into a Takagi-Sugeno-Kang adaptive neuro-fuzzy inference system (ANFIS). Static image-based features were extracted from offline signature images and transformed using principal component analysis (PCA) before classification. Experiments were conducted on 800 offline signature samples collected from Dipa University Makassar, consisting of 400 genuine and 400 forged signatures. The proposed model achieved an accuracy of 90.5%, precision of 98.8%, recall of 82.0%, and F1-score of 90.0%. The high precision indicates that SS- ANFIS is effective in minimizing false positive predictions, which is important for academic document verification. The results show that the proposed model provides a practical, interpretable, and computationally efficient approach for offline signature verification, particularly in institutional settings with limited labeled data.
Volume: 16
Issue: 4
Page: 1985-1997
Publish at: 2026-08-01

ACLiMA: an IoT-based autonomous flood monitoring and mitigation system with database-driven threshold control

10.11591/ijece.v16i4.pp1867-1875
Hendi Santoso , Rizqan Khairan Munandar , Apriansyah Apriansyah , Andi Ihwan , Putri Yuli Utami
Urban flooding remains a critical challenge in densely populated and low-lying areas, where delayed response and limited monitoring infrastructure significantly increase flood risks. Existing flood monitoring systems are typically limited to passive observation or fixed-threshold alerting without integrated autonomous mitigation and flexible configuration. This study proposes autonomous control logic for IoT-based monitoring and actuation (ACLiMA), an IoT-based autonomous flood monitoring and mitigation system using a database-driven threshold control approach to enable real-time monitoring and immediate response. The system integrates ultrasonic water-level sensing, centralized database management, web-based visualization, and autonomous pump actuation within a unified architecture. Flood conditions are classified into four operational states—SAFE, CAUTION, DANGEROUS, and FLOOD—based on configurable threshold values stored in the database, allowing dynamic adjustment without firmware modification. Experimental results demonstrate stable system integration with deterministic control behaviour and low response latency between sensing and actuation, enabling timely pump activation during critical conditions. The system also provides multi-temporal visualization for monitoring and analysis, while the database-driven configuration enhances flexibility, scalability, and ease of deployment across different environments. Overall, the proposed system offers a low-cost, modular, and autonomous solution for real-time flood mitigation, contributing to the transition from passive monitoring toward active mitigation in smart city and resource-constrained urban applications.
Volume: 16
Issue: 4
Page: 1867-1875
Publish at: 2026-08-01

A non-interactive lattice-based scheme supporting multiple blindness modes

10.11591/ijece.v16i4.pp1976-1984
Dinh Hai Le , Luong Vu Ngoc Bui
This paper proposes a lattice based multi-mode blind signature framework that uniformly supports three operating modes: full blindness, partial blindness, and non-blindness. The design employs public matrices with trapdoors, combined with hash functions mapping into ℤ𝑞𝑛, short preimage sampling mechanisms, and an identity encryption component to ensure fairness and enable traceability in case of misuse. Based on this construction, the user generates a blinded request, the signer produces a short response bound to the encrypted identity, and the user performs an unblinding step to obtain the final signature. The paper also presents the system model, research methodology, security claims, and parameter discussions in the post-quantum setting. The main properties analyzed include correctness, blindness, partial blindness, existential unforgeability, one more unforgeability, fairness, and traceability under the SIS and one more SIS assumptions. The results indicate that the proposed approach provides a flexible solution for applications requiring a balance between anonymity, accountability, and post-quantum security.
Volume: 16
Issue: 4
Page: 1976-1984
Publish at: 2026-08-01

Methods of finding the maximum common transitive subgraph: experimental comparison

10.12928/telkomnika.v24i4.27683
Oleg; Volgograd State Technical University Sychev , Anton; Volgograd State Technical University Chupinin
The problem of finding a maximum common subgraph (MCS) in a graph has broad applications in practical domains. However, certain scenarios require subgraphs with special properties, such as transitivity, that must be kept during building the subgraph. We formally define the concept of a transitive subgraph, investigate its properties. We study four different algorithms for finding the max imum common transitive subgraph (MCTS), compiled a list of tests aim at com paring graphs after making various changes and evaluated their accuracy and efficiency on a set of test cases. Benchmarking on 64 tests ranks the algorithms by scalability and accuracy: branch matching is the most scalable (> 1000 ver tices) and accurate (F1: 0.9907). MCS tree search is viable for graphs of up to ∼ 250 vertices (F1: 0.9752). Backtracking is limited to < 30 vertices (ac curacy: 0.5625), and brute-force is only feasible for graphs with ≤ 10 vertices, despite its high accuracy (0.9375). We discuss the advantages and disadvantages of each method, the test cases where each method demonstrates a non-optimal MCTS,identify the classes on which the methods work correctly and found that the branch matching method based on the longest common subsequence (LCS) algorithm performed the best.
Volume: 24
Issue: 4
Page: 1187-1196
Publish at: 2026-08-01
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