Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

30,907 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

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

Comparative evaluation of classical and machine learning methods for medical image enhancement

10.12928/telkomnika.v24i4.27700
Md.; University of Frontier Technology Mehedi Hasan , Sujon; University of Frontier Technology Chandra Sutradhar , Zannatul; University of Frontier Technology Ferdushie , Rabeya; University of Frontier Technology Basri
Medical imaging is critical for diagnostic accuracy, yet raw images often suffer from noise and low contrast. This study provides a comparative evaluation of classical methods, namely the Laplace transform (LT), Sobel operator (SO), and histogram equalization (HE), against a data-driven convolutional neural network (CNN) using the musculoskeletal radiographs (MURA) and Human Metapneumovirus (HMPV) lung computed tomography (CT) datasets. While quantitative analysis shows that HE and SO significantly outperform other methods in isolated contrast enhancement and edge definition, they often introduce artifacts. In contrast, the CNN based approach demonstrates superior detail preservation and entropy, offering a more balanced and adaptive solution for diverse diagnostic requirements. Our findings statistically validate that although classical operators remain highly effective for specific boundary detection tasks, machine learning (ML) frameworks provide the most robust performance for cross-modality image enhancement, bridging the gap between raw data acquisition and clinical interpretation.
Volume: 24
Issue: 4
Page: 1331-1341
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

A systematic review and conceptual roadmap for sky computing: AI-enabled orchestration, interoperability, and governance beyond multi-cloud

10.11591/ijece.v16i4.pp2247-2253
Abdullah Al-Bakri
The concept of sky computing is becoming known as an industry-independent “cloud of clouds” approach intended to address the issues of fragmentation inherent in today’s multi-cloud and hybrid cloud implementations. The objective of this systematic literature review is to examine the challenge of existing multi-cloud architectures, which despite delivering high reliability and purchasing flexibility, suffer from API heterogeneity, fragmented intercloud orchestration, insufficient workload mobility, and unresolved sovereignty and compliance challenges. Based on the PRISMA methodology, 325 papers released between January 2020 and June 2025 have been systematically selected in five scientific databases: ACM Digital Library, IEEE Xplore, SpringerLink, ScienceDirect, and Scopus. Upon applying a process of elimination for duplicates, title-and-abstract screening, full-text evaluation, and quality assessment, a total of 35 peer-reviewed publications from the same timeframe have been thematically analyzed. Four thematic areas were examined: architectural architecture, intercloud orchestration, automation through artificial intelligence/machine learning (AI/ML), and security, privacy, and compliance. Not a single article predating the year 2020 was part of the final systematic literature review (SLR) database or bibliography. The results reveal that compatibility layers and intercloud brokers increase portability of workloads; scheduling based on artificial intelligence is useful in automating operations and achieving optimal cost performance; while zero trust architecture, self-sovereign identity, confidential computing, and policy-driven compliance are key in achieving trustworthy cross jurisdictional operations. The two major conclusions that arise from this study are: Firstly, future studies need to investigate explainability and energy-efficient AI orchestration in a real-world setting with multiple cloud providers, whereas secondly, cloud computing professionals need to incorporate principles of privacy, sovereignty, and compliance directly into the orchestration policies, not as an afterthought.
Volume: 16
Issue: 4
Page: 2247-2253
Publish at: 2026-08-01

Adaptive trading system for sustainable forex markets

10.12928/telkomnika.v24i4.27479
Joni; Universitas Trisakti Fat , Parwadi; Universitas Trisakti Moengin , Pudji; Universitas Trisakti Astuti , Sally; Universitas Trisakti Cahyati
This study presents a sustainable and ethically aligned algorithmic trading system for the Euro/United States Dollar (EURUSD) currency pair, integrating reinforcement learning (RL) with a Sugeno-type fuzzy inference mechanism. The framework emphasizes responsible AI principles by combining adaptability and interpretability to support transparent and explainable financial decision-making. Historical EURUSD M15 data from 2020 to 2023 were used for training, while 2024 data served for out-of sample testing. The system employs EMA50-based state classification, tabular state–action–reward–state–action or SARSA learning, and a fuzzy logic layer comprising 27 expert-defined rules. During backtesting, the agent executed 785 trades, achieving a net profit of USD 61.85, a profit factor of 1.04, and a balanced win–loss ratio. Risk-adjusted analysis showed moderate resilience (sharpe ratio = 0.53) and a maximum drawdown of 59.74%. The model demonstrated strong equity stability (ESI = 0.9672) and sensitivity to macroeconomic events identified through cumulative sum (CUSUM) analysis. While the system maintained capital preservation and interpretability, responsiveness under volatile conditions requires improvement. Future work will focus on adaptive exit logic, volatility-aware reward mechanisms, and regime-sensitive policy optimization. This study contributes to advancing sustainable, transparent, and risk-aware artificial intelligence (AI) frameworks in algorithmic trading.
Volume: 24
Issue: 4
Page: 1267-1277
Publish at: 2026-08-01

Beyond reductionism: systems thinking for the next generation of electrical and computer engineering

10.12928/telkomnika.v24i4.3776
Tole; Universitas Ahmad Dahlan Sutikno
Classical electrical and computer engineering has achieved remarkable progress through reductionist methodologies that decompose complex systems into manageable, analyzable, and optimizable components. While this paradigm remains indispensable for scientific rigor and engineering design, it is increasingly challenged by contemporary systems characterized by interconnectedness, dynamic interactions, and multi-scale complexity. This editorial argues that future engineering requires extending, rather than replacing, reductionist thinking with systems thinking capable of capturing interdependence, emergence, resilience, and holistic system behaviour. Beyond component-level optimization, engineering must increasingly consider interactions among technological, human, environmental, and societal dimensions that collectively shape system performance and long term sustainability. Systems thinking therefore provides a complementary paradigm for understanding how complex engineering systems adapt, evolve, and generate behaviours that cannot be inferred solely from individual subsystems. This perspective redefines electrical and computer engineering as an integrated socio-technical discipline in which analytical precision is combined with systemic understanding to address increasingly complex real-world challenges. Moving beyond reductionism does not diminish the value of analytical methods but expands their scope within broader interconnected contexts. This paradigm shift establishes the conceptual foundation for the subsequent evolution toward adaptive, human centred, and ultimately responsible engineering.
Volume: 24
Issue: 4
Page: 1083-1090
Publish at: 2026-08-01

Reconfigurability of graphene-based hexagonal patch operating in the Ku band

10.12928/telkomnika.v24i4.27737
Hassna; Moulay Ismail University Agoumi , Seddik; Moulay Ismail University Bri , Youssef; Mohammed V University El Amraoui , Adil; Moulay Ismail University Saadi
This paper presents a novel graphene-based impedance reconfigurability approach for Ku-band hexagonal patch antennas, demonstrated at both the single-element and 4×4 array levels. Unlike conventional metallic or diode based reconfigurable antennas, frequency tuning is achieved by exploiting the tunable surface conductivity of graphene integrated into E-shaped slots, without altering the antenna geometry or employing active lumped components. Antenna performance is evaluated using full-wave electromagnetic simulations for two graphene states (“on” state and “off” state), representing distinct surface impedance conditions. The single element exhibits dual resonances at 14.81 GHz and 15 GHz, with reflection coefficients of -52.56 dB and -38.18 dB, bandwidths of 783 MHz and 517 MHz, and a peak gain of 7.8 dBi. The 4×4 array exhibits multiple resonances between 12.5–16.8 GHz (off state) and 12.6–17.15 GHz (on state), achieving bandwidths up to 2750 MHz and a maximum gain of 13.64 dBi. These results demonstrate a scalable, geometry-preserving reconfigurable antenna solution for compact Ku-band systems.
Volume: 24
Issue: 4
Page: 1372-1384
Publish at: 2026-08-01

Mapping research trends on tropical cyclone–induced flood susceptibility: a bibliometric and systematic review method

10.12928/telkomnika.v24i4.27614
Soenardi; IPB University Soenardi , Bambang; IPB University Dwi Dasanto , Yonny; IPB University Koesmaryono , I; IPB University Putu Santikayasa , Giarno; Agency of Meteorology, Climatology, and Geophysics Giarno
Climate change has intensified tropical cyclones (TC), increasing extreme rainfall and flood hazards in many regions. Flood susceptibility (FS) mapping is therefore essential for understanding flood risk. This study analyzes global research trends on TC-induced FS by integrating bibliometric analysis and a preferred reporting items for systematic reviews and meta-analyses (PRISMA)-based systematic literature review (SLR) using Google Scholar (GS) publications from 2014 to 2024. A total of 993 journal articles were analyzed, yielding an h-index of 101 and a g-index of 168, indicating strong and growing research interest. The results reveal an increasing application of machine learning (ML), deep learning (DL), remote sensing (RS), and geographic information systems (GIS) for FS mapping. Several gaps remain, including limited use of high-resolution data, underrepresentation of data-scarce and equatorial regions, restricted integration of hybrid models, and a lack of long-term assessments considering climate change and socio-economic factors. The model’s performance is also highly dependent on data quality and regional characteristics, limiting its generalizability across different conditions. The main contribution of this study is the knowledge mapping and synthesis of TC-induced FS research, providing a structured foundation for future studies and supporting evidence-based flood risk management and climate adaptation.
Volume: 24
Issue: 4
Page: 1253-1266
Publish at: 2026-08-01

Acoustic and vibration side channel analysis on post-quantum cryptography using image-based deep learning

10.12928/telkomnika.v24i4.27791
Abdul; Muhammadiyah University of North Maluku Haris Muhammad , Gamaria; Muhammadiyah University of North Maluku Mandar , Adelina; Muhammadiyah University of North Maluku Ibrahim
Post-quantum cryptography (PQC) is designed to resist quantum-era attacks; however, practical implementations remain vulnerable to physical side channel leakage. This work proposes an image-based acoustic–vibration side-channel analysis framework to assess non-invasive leakage in PQC systems. Acoustic and vibration signals from secret-dependent executions are modeled and transformed into time–frequency spectrograms using short time fourier transform (STFT). The dataset comprises 1,545 samples (1,236 training and 309 testing), acquired at 16 kHz and segmented into 2.5-second windows. Leakage classification is performed using convolutional neural networks (CNNs) and vision transformers (ViTs) under single-modality and multimodal fusion settings. Results show that acoustic signals yield strong leakage, achieving up to 100% accuracy with CNN, while vibration signals reach up to 98.75%. Multimodal fusion improves training stability and overall performance, and ViT models demonstrate better generalization across modalities. These findings confirm that multimodal spectrogram based deep learning is effective for PQC side-channel analysis and underscore the need for rigorous physical security evaluation in real-world PQC implementations.
Volume: 24
Issue: 4
Page: 1241-1252
Publish at: 2026-08-01

A model for flexible learning in graduate teacher education programs

10.11591/ijere.v15i4.39252
Marilyn U. Balagtas , Adonis P. David , Erminda C. Fortes , Arceli M. Amarles , Alvin B. Barcelona , Marla C. Pampango , Marjorie Naquita
This study aimed to develop a model for flexible learning (FL) appropriate to graduate teacher education programs (GTEP) based on the different practices of the graduate faculty and students in a teacher education institution (TEI) before and during the COVID-19 pandemic. A multimethods approach was employed, utilizing survey questionnaires, semi-structured interviews, and focus group discussions (FGD). Data were collected from 215 graduate students and 43 graduate faculty members who were selected through convenience sampling. The study resulted in the development of a model of FL for GTEP (MFL-GTEP), reflected in an outcome-based syllabus that highlights 10 areas of FL, all beginning with P: purpose, process, pedagogy, platform, people, place, pace, performance, product, and policy of learning. The MFL-GTEP promotes self-agency, self-regulation, and self-determination among education professionals pursuing GTEP. The challenges that graduate faculty and students experience in the implementation of FL are addressed in the (MFL-GTEP) to make the model more relevant, inclusive, and sustainable in a graduate teacher education program.
Volume: 15
Issue: 4
Page: 3193-3203
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

Experimental validation of a low-cost microcontroller-based rack-level thermal control prototype

10.12928/telkomnika.v24i4.27838
Wandercleiton; University of Genoa Cardoso , Danyelle; University of Pavia Santos Ribeiro , Thiago Augusto; Federal Institute of Espírito Santo Pires Machado , Saulo Alexandre; Bastianello Consultancy Inacio , Elielton; Hidrovias do Brasil A. Cometti , Marcelo; Federal Institute of Espírito Santo Margon , Fernando; uConnect Telecom Baptista dos Santos Neves
The rapid growth of data centers (DCs), driven by digital transformation and the increasing adoption of artificial intelligence (AI), has intensified challenges related to thermal management and operational reliability. Cooling systems account for a substantial portion of total energy consumption and often show limited effectiveness in mitigating localized hotspots and dynamic temperature variations in high-density server environments. This study presents the development and experimental validation of a low-cost, microcontroller-based (MCU-based) localized thermal control system. The proposed architecture integrates a temperature sensor, an Arduino-based control unit, pulse-width modulation (PWM) driven fan actuation, and Ethernet communication for remote monitoring. The system was implemented in a standard 19-inch rack under controlled laboratory conditions using a simulated thermal load. Experimental results, based on the average of five independent tests, demonstrated that combined operation of the prototype with rack ventilation reduced the cooling time from 45 °C to 40 °C to 50 ± 2 s, compared to approximately 5 minutes with rack ventilation alone and more than 12 minutes under natural convection. The corresponding cooling rates were 0.10 °C/s, 0.015 °C/s, and 0.007 °C/s. These results indicate that simple, distributed thermal control strategies can effectively mitigate localized overheating and support rack-level thermal stability in data center microenvironments.
Volume: 24
Issue: 4
Page: 1131-1142
Publish at: 2026-08-01

XGBoost modeling for sparse spare-parts demand forecasting

10.12928/telkomnika.v24i4.27777
Brian Qaedi; Institut Teknologi Sepuluh Nopember (ITS) Laksono Putra , Jerry Dwi; Institut Teknologi Sepuluh Nopember (ITS) Trijoyo Purnomo
Spare parts demand in many industrial systems is inherently sparse and intermittent. In practice, long periods of zero usage are common, even though inventory must still be maintained to ensure operational reliability. This situation increases holding costs and the risk of obsolescence, while also limiting the effectiveness of conventional forecasting techniques. This study demonstrates that a global XGBoost model trained across multiple spare-part items significantly outperforms item-specific models under sparse demand conditions. Using six years of historical spare-parts usage and procurement data from the energy sector, this study compares global and single-item extreme gradient boosting (XGBoost) modeling strategies. Forecast accuracy is evaluated using mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and median absolute error (MdAE), which is particularly suitable for zero-inflated demand patterns. The results consistently show that the global XGBoost model achieves lower errors across all metrics. In particular, the global model attains a markedly lower MdAE (0.00018), indicating greater robustness when demand is irregular and intermittent.
Volume: 24
Issue: 4
Page: 1287-1293
Publish at: 2026-08-01
1 2 3
...
2060 2061
Show 6 of 2061

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration