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30,735 Article Results

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

Beyond adoption: measuring the success of mandatory information systems through an integrated ECM and ISSM

10.11591/ijece.v16i4.pp2031-2041
Muhammad Rosyid Ridlo , Muhammad Fachri Shandika Iman , Reny Yuliati
The successful implementation of mandatory organizational information systems depends not only on system adoption but also on user satisfaction. However, most post-adoption evaluation studies have focused on voluntary systems, leaving mandatory public sector deployments substantially underexplored. This study evaluates the determinants of employee satisfaction with the Coretax Administration System, a nationwide integrated tax platform implemented by the Directorate General of Taxes in Indonesia. To provide a comprehensive explanation of post-adoption evaluation, this research integrates the Expectation Confirmation Model (ECM) and the Information System Success Model (ISSM), examining how system quality, information quality, and service quality influence confirmation and perceived usefulness, which in turn determine user satisfaction. Using a quantitative approach, data were collected from 292 employees actively using the system and analyzed through Partial Least Square Structural Equation Modeling (PLS-SEM). The results demonstrate that the integrated model exhibits strong predictive power, explaining 76.4% of the variance in user satisfaction. System quality emerged as the most influential determinant, significantly affecting confirmation and perceived usefulness, which subsequently drives satisfaction. Meanwhile, information quality and service quality showed selective effects, indicating that technical reliability plays a more critical role than supportive features in a mandatory environment. The findings offer actionable guidance for policymakers and IS architects engaged in large-scale compulsory digital transformation initiatives in the public sector.
Volume: 16
Issue: 4
Page: 2031-2041
Publish at: 2026-08-01

Real-time anomaly detection system using best performed machine learning model

10.12928/telkomnika.v24i4.27561
Victor; North-West University Mathebula , Bukohwo; North-West University Michael Esiefarienrhe
Effective anomaly detection is critical for protecting organizational networks against increasingly sophisticated cyber threats. However, most machine learning-based intrusion detection models are developed and validated using public benchmark datasets, which may not reflect the operational characteristics, traffic behavior, and threat patterns of real institutional networks. In the case of Umalusi, there is currently no anomaly detection model customized and validated using Umalusi-specific network traffic, creating a practical gap in deployable cybersecurity capability. This study proposes a hybrid machine learning framework tailored to support accurate, efficient, and operationally relevant anomaly detection. Using knowledge discovery in databases (KDD) process, network traffic data were collected and pre-processed through normalization, label encoding, missing value treatment, and dimensionality reduction using principal component analysis (PCA). The 16 hybrid models integrating unsupervised anomaly detection with supervised classification were implemented and comparatively evaluated. Experimental findings indicate that the density-based spatial clustering of applications databasescan (DBSCAN) + random forest (RF) model achieved 99.92% accuracy while maintaining a low false positive (FP) cost, making it suitable for a security operations centre (SOC). In addition, a Flask-based web application was developed to enable real-time deployment by sniffing live network traffic, executing inference, and persisting results in an SQLite database.
Volume: 24
Issue: 4
Page: 1204-1215
Publish at: 2026-08-01

Intelligent routing-based attack detection in Internet of Things networks using artificial intelligence

10.11591/ijece.v16i4.pp2169-2181
Huda Saloom Sultan , Asseel Jabbar Almahdi , Murteza Hanoon Tuama , Athar Hussein Mohammed
The fast-growing Internet of Things (IoT) networks have posed considerable security risks because of decentralized network designs, dynamic topologies, and inadequate computation capabilities. Current intrusion detection strategies are primarily traffic-based, but without paying attention to routing-layer dynamics, which are paramount in multi-hop IoT systems. To overcome this drawback, this paper suggests a smart routing-conscious attack detection model which combines routing-layer monitoring with methods of artificial intelligence to improve the security of IoT networks. The suggested framework constantly compares routing metrics, such as packet loss, change in hop count, end to end delay and energy consumption to detect malicious routing behavior in real time. Two types of artificial neural networks, feedforward neural network (FFNN) and convolutional neural network (CNN) are used to categorize routing activities as normal or malicious. The experimentation on simulation was carried out by using NS-2 in a dynamic multi-hop IoT environment where routing-based DoS attacks were implemented. The experimental results reveal that CNN model had a higher detection accuracy of 85.76% with lower execution time of 17 s compared to the FFNN model which had an accuracy of 82.76% and an execution time of 18 s. Moreover, the suggested framework enhanced reliability of routing by minimizing the packet loss and communication delay and having low routing overhead. These results support the hypothesis that routing-aware intelligence can be used to enhance AI-based intrusion detection to create an adaptive, routing-aware, and resource-efficient security solution to decentralized networks of IoT devices.
Volume: 16
Issue: 4
Page: 2169-2181
Publish at: 2026-08-01

Predictive safety helmet for miners using internet of things and artificial intelligence

10.12928/telkomnika.v24i4.27421
Vijayalakshmi; Thiagarajar College of Engineering Murugesan , Irudhaya Ronisha Innasi; Thiagarajar College of Engineering John Benedict , Janani; Thiagarajar College of Engineering Vigneswaran , Pooja; Thiagarajar College of Engineering Senthamarai Kannan
Mining is still responsible for many deaths since mines have dangerous environmental conditions including mine collapses, gas emissions, and high temperatures. However, traditional helmets do not provide adequate protection; besides, they cannot analyze miners’ health as well as the environmental hazards. In order to solve this issue, this work presents a predictive safety helmet equipped with several sensors and means of communication. Specifically, the helmet comprises a micro-electro mechanical systems (MEMS) accelerometer for vibration monitoring, a gas sensor for detecting the presence of harmful gases, a heartbeat sensor for assessing workers’ well-being, and a temperature sensor for monitoring the environmental parameters. Additionally, the device is provided with a global positioning system (GPS) module for location determination and a global system for mobile (GSM) module for transmitting alert notifications in case of emergency situations. The collected data is analyzed on an internet of things (IoT)-based system; any signs of danger cause alerts to be sent immediately.
Volume: 24
Issue: 4
Page: 1177-1186
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

From data to intelligence: foundations of learning systems, representation, and computational perception

10.11591/ijece.v16i4.pp1669-1676
Tole Sutikno
The rapid evolution of intelligent systems has shifted the focus of electrical and computer engineering from isolated data processing toward integrated models of machine cognition. This editorial introduces a foundational perspective on machine intelligence systems, emphasizing the transformation from raw data to meaningful intelligence through learning systems, representation mechanisms, and computational perception. In contemporary AI-driven environments, intelligence is no longer defined solely by algorithmic performance, but by the ability to construct structured representations of the world and interpret complex multimodal signals. Learning systems, particularly those grounded in machine learning and deep learning paradigms, serve as the core mechanism enabling this transformation. Representation learning provides the bridge between unstructured data and abstract knowledge, while computational perception enables machines to interpret visual, auditory, and sensor-based information in real time. Together, these components form the foundational architecture of intelligent systems that underpin emerging applications in engineering, automation, and cyber-physical environments. This editorial sets the stage for understanding intelligence as an emergent computational construct, highlighting its role as the first phase in the broader cognitive intelligence systems continuum that progresses toward adaptive, autonomous, and socio-cognitive systems in future research directions.
Volume: 16
Issue: 4
Page: 1669-1676
Publish at: 2026-08-01

The quality of advocacy services in primary school social work from the perspective of Vietnamese teachers

10.11591/ijere.v15i4.38789
Ha Van Hoang , Pham Thi Kieu Duyen
This study aimed to assess the level of satisfaction among primary school teachers with advocacy services in school social work and to identify influencing factors. A quantitative method was applied through a questionnaire survey of 398 primary school teachers, focusing on evaluating aspects of advocacy services such as reliability, responsiveness, professional competence, empathy, and implementation conditions. The results showed that the overall satisfaction level of teachers was quite high (M=4.01, SD=0.27), and all components of the service were viewed positively. Simultaneously, factors such as gender, age, location, and region influenced how teachers evaluated the quality of the service, while years of service and educational level had only a limited impact. On the other hand, all service components showed a positive correlation with the level of satisfaction with advocacy services in school social work. In this study, responsiveness, reliability, empathy, and implementation conditions showed statistically significant results. Therefore, the study suggests policy directions and further research, particularly in applying the service quality (SERVQUAL) model to measure and improve aspects of social work services.
Volume: 15
Issue: 4
Page: 3508-3517
Publish at: 2026-08-01

Psychosocial factors of cyberbullying experienced by Malaysian public university undergraduates

10.11591/ijere.v15i4.39582
Siti Nurfathin Idris , Mastura Mahfar , Faizah Mohd Fakhruddin , Azlina Mohd Kosnin , Aslan Amat Senin , Halimah Mohd Yusof , Nur Azmina Paslan
Cyberbullying has emerged as a critical issue among university students in Malaysia, driven by the widespread use of digital technologies and associated with serious psychological consequences. Despite its increasing prevalence, limited research has explored how psychosocial factors shape cyberbullying victimization within the Malaysian higher education context. This study addresses this gap by exploring the psychosocial factors influencing cyberbullying victimization among university students in Malaysia. A qualitative case study approach was employed, involving five university students who had experienced cyberbullying. Data was collected through semi-structured, in-depth interviews and analyzed using thematic analysis. The findings suggest that cyberbullying victimization is influenced by an interplay of psychological and social factors. Psychological factors include passive behavior, low self-esteem, irrational beliefs and personality traits, while social factors encompass peer relationships, parenting styles, social media engagement, and online gaming environments. This study suggests that cyberbullying is a multifaceted phenomenon shaped by both individual vulnerabilities and environmental influences. These findings highlight the need for comprehensive, psychosocial-based interventions to support students’ wellbeing and promote safer digital environments in higher education institutions.
Volume: 15
Issue: 4
Page: 2795-2805
Publish at: 2026-08-01

Developing a transdisciplinary design-based in-service science teacher training framework

10.11591/ijere.v15i4.38783
Joelash R. Honra , Ma. Kristina B. B Dela Cruz , Jermae B. Dizon-Yi , Raianne Joy V. Maulion , Sean Derrick M. Oliquiano , James C. Ollero , John Lorence A. Villamin
Contemporary science education requires teachers to facilitate learning that addresses complex, real-world problems beyond disciplinary boundaries. Yet, many in-service science teachers lack professional development that supports transdisciplinary problem-solving and innovative pedagogy. This qualitative study used a grounded theory (GT) approach to examine teachers’ experiences in a transdisciplinary, design-based training program and to develop a framework for effective professional learning. Participants engaged in sustained training grounded in design thinking and authentic problem contexts. Data were collected through semi-structured interviews, focus groups, reflective journals, training artifacts, and observations, and analyzed using constant comparative methods. Findings indicated shifts in teachers’ conceptions of problem-solving, enhanced capacity to integrate disciplinary and non-disciplinary perspectives, and changes in instructional planning and classroom practice. Design thinking functioned as a mediating process that helped teachers navigate ambiguity, collaboration, and iterative reflection. The resulting transdisciplinary design-based in-service science teacher training framework highlights key principles: authentic problem contexts, structured yet flexible design processes, collaborative inquiry, and iterative reflection. The study offers an empirically grounded framework with implications for teacher professional development, curriculum design, and policy.
Volume: 15
Issue: 4
Page: 2814-2822
Publish at: 2026-08-01

Bridging ethics and performance in engineering education through predictive learning analytics

10.11591/ijere.v15i4.38767
Hamza Abu Owida , Areen Arabiat
This literature review examines the opportunities, implementation challenges, ethical implications, and emerging directions of predictive learning analytics (PLA) in engineering education. Using a structured review of the literature, the study synthesizes evidence from several publications with emphasis on studies examining risk prediction, personalized support, curricular improvement, interpretability, fairness, and intervention design. The review shows that PLA can improve early identification of at-risk students, support adaptive learning pathways, and inform data-driven refinements in engineering curricula; however, its impact depends on data quality, model transparency, institutional capacity, and the availability of timely human support. The analysis further indicates that the most consequential barriers are fragmented data ecosystems, the difficulty of translating predictions into effective interventions, and unresolved ethical concerns related to privacy, bias, consent, and student agency. The article contributes to educational research by offering an integrated synthesis that connects technical development with pedagogical evaluation and ethical governance in engineering education. It concludes by proposing that future PLA adoption should align predictive modeling with explainable artificial intelligence, learning-theory-informed intervention design, and institution-level implementation strategies. Publications were selected for relevance to PLA in engineering education and then synthesized narratively across opportunities, challenges, ethics, and future directions.
Volume: 15
Issue: 4
Page: 2973-2982
Publish at: 2026-08-01

Galperian epistemology, human thought, and artificial intelligence in education

10.11591/ijere.v15i4.39338
Querubín Patricio Flores Núñez , Tiago Felipe Vera-Assaoka , Juan Carlos Huircalaf Diez
This theoretical review examines the relationship between Piotr Galperin’s epistemology, the development of human thought, and the incorporation of artificial intelligence (AI) in education. The analysis is organized around five issues: the stage-by-stage formation of mental actions, the zone of proximal development as oriented mediation, the pedagogical role of AI, the ethical and cultural risks of automation, and practical principles for educational design and regulation. The review synthesizes contributions from historical-cultural psychology, activity theory, neuroeducation, ethics of technology, and recent AI-in-education studies in order to argue that AI should not be understood as a substitute for human thinking but as a mediated resource whose value depends on pedagogical intentionality, transparency, and cultural grounding. The paper’s explicit novelty lies in articulating Galperin’s orienting basis of action with current debates on AI design, educational policy, and ethical regulation, thereby moving from philosophical interpretation to concrete pedagogical criteria for practice. From this perspective, the teacher remains a reflective mediator, and education retains its formative responsibility to cultivate autonomy, critical judgment, and consciously regulated action.
Volume: 15
Issue: 4
Page: 3415-3421
Publish at: 2026-08-01

Enhancement of YOLOv8 for object detection in adverse weather conditions using generative adversarial network

10.11591/ijece.v16i4.pp2230-2246
Talifhani Calvin Tshipota , Chunling Tu , Mukatshung Claude Nawej , Sempe Thom Leholo
Detecting objects in bad weather like rain, fog, snow, or low light is still difficult because visibility drops, noise increases, and contrast gets worse, all of which hurt detection accuracy. Most current methods either improve detector designs or use image preprocessing on their own. They usually focus on just one type of weather and do not use a common way to evaluate results. This paper introduces a YOLOv8 framework improved with a generative adversarial network (GAN) for image enhancement before detection. Instead of just making images look better, the GAN is trained to help the object detector work better in tough conditions, so it can extract features more effectively when images are degraded. The model was tested on datasets with different weather conditions using standard metrics like Precision, Recall, F1-score, and mean average precision (mAP). Results show that this method consistently improves performance, with up to a 6.5% increase in mAP@0.5 over YOLOv8-STE and 9.2% over IA-YOLO, especially in foggy and low-light situations. These results show that adding GAN-based preprocessing to YOLOv8 makes detection more reliable and still keeps the process fast. This framework offers a practical and scalable solution for real-world uses like self-driving cars, smart transportation, and surveillance.
Volume: 16
Issue: 4
Page: 2230-2246
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

The adoption of artificial intelligence in government technology in developing countries: a systematic literature review

10.11591/ijece.v16i4.pp2192-2209
Maria Florentina Rumba , Flourensia Sapty Rahayu
This study systematically reviews empirical evidence on Artificial Intelligence adoption in Government Technology across developing countries, addressing three research questions regarding determinant factors for success, strategies to overcome absorptive capacity constraints, and governance frameworks for responsible implementation. To answer these questions, a systematic literature review was carried out using a structured methodology: The Scopus database was searched with a comprehensive keyword strategy, which initially yielded ninety-three publications. Through rigorous screening and eligibility assessment, fourteen articles meeting all inclusion criteria were analyzed thematically. The findings demonstrate that successful AI adoption requires holistic alignment among institutional factors including policy frameworks and public trust, organizational elements such as human resource capacity and bureaucratic culture, and environmental conditions encompassing infrastructure and societal demands. Effective strategies identified include strengthening digital infrastructure, developing human capital, implementing adaptive governance, fostering multi-stakeholder collaboration, and ensuring contextual adaptation. The study further reveals that legitimate and sustainable implementation necessitates integrating collaborative governance as a legitimacy foundation, Governance, Risk, and Compliance (GRC) frameworks as ethical control systems, and Explainable AI as a transparency mechanism. This integrated approach enables AI to generate both operational efficiency and strategic public value, including social inclusion and progress toward sustainable development objectives.
Volume: 16
Issue: 4
Page: 2192-2209
Publish at: 2026-08-01
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