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31,377 Article Results

Development model of remote sensing data management for long-term storage using big data

10.12928/telkomnika.v24i5.27639
Riyan; National Research and Innovation Agency Mahendra Saputra , Agnes; National Research and Innovation Agency Sondita Payani , Ogi; National Research and Innovation Agency Gumelar
The growing volume of remote sensing data requires efficient approaches for long-term storage and data management. The National Remote Sensing Data Bank (BDPJN) stores multi-resolution satellite imagery acquired since 1993 to support disaster monitoring, climate studies, natural resource management, and other Earth observation applications. However, managing continuously growing archives remains challenging due to manual migration processes, inefficient storage utilization, and limited scalability. This study proposes an integrated remote sensing data management model that combines hierarchical storage management (HSM) with Apache Spark based parallel processing to support policy-driven data migration and long term storage optimization. The proposed model enables data migration across multiple storage tiers based on data characteristics and storage policies while improving migration efficiency through distributed processing. Experimental evaluation was conducted using Landsat-8, SPOT 6, SPOT-7, and Pleiades datasets under both 1 Gbps and 10 Gbps network environments. The results demonstrate migration performance improvements ranging from 80.14% to 91.04% compared with the existing manual migration system. These findings indicate that integrating HSM with Spark-based parallel processing can reduce migration bottlenecks, improve storage utilization, and provide a scalable and cost-effective approach for managing large-scale remote sensing archives while supporting long-term data governance and accessibility.
Volume: 24
Issue: 5
Page: 1535-1549
Publish at: 2026-10-01

Optimization of MSMEs promotion strategy using GIS with random forest algorithm and A-star algorithm

10.12928/telkomnika.v24i5.26692
Sulyono; Darmajaya Institute of Informatics and Business Sulyono , Suci; Darmajaya Institute of Informatics and Business Mutiara , Agus; Darmajaya Institute of Informatics and Business Rahardi , Sri; Darmajaya Institute of Informatics and Business Lestari , Yulmaini; Darmajaya Institute of Informatics and Business Yulmaini , Ruki Rizal; Darmajaya Institute of Informatics and Business Nul Fikri , Ali; Darmajaya Institute of Informatics and Business Syarifuddin
Micro, small, and medium enterprises (MSMEs) make a main contribution to improving a country’s economy, but it still faces various problems including limited market access, ineffective promotion, and lack of understanding in business profiles. The purpose of this study is to design a geographic information system (GIS) combined with the random forest (RF) algorithm and the A-star algorithm. The RF algorithm functions are to classify MSMEs. Meanwhile, the A-star algorithm is to recommend the shortest route to support accessibility. In addition, it is equipped with a dashboard and MSMEs profile as a reference for stakeholders in making MSMEs development policies and as information to the public in order to expand the reach of promotion in introducing MSMEs and attracting investors, thus optimizing MSMEs promotion strategies. In addition, the evaluation results of the classification model show an accuracy value of 99.6%, indicating that the model is able to predict very well, making it highly recommended for MSME classification.
Volume: 24
Issue: 5
Page: 1526-1534
Publish at: 2026-10-01

Enhancing educators’ digital literacy through digital leadership and organizational culture

10.11591/ijere.v15i5.39153
Sugiarto Sutomo , Heni Rochimah
Despite rapid advances in digital technology and online learning, educators at the education and training center face limited digital access, low information and communication technology (ICT) proficiency, and difficulty integrating technology, reflecting the slow national rise in digital literacy. This study tests whether strengthening digital leadership and aligning organizational culture can improve educators’ digital competencies. A cross-sectional survey of 120 educators was analyzed using path analysis in JASP. Findings indicate that digital leadership strongly predicts organizational culture (β=0.869, z=28.834, p
Volume: 15
Issue: 5
Page: 3777-3789
Publish at: 2026-10-01

Robustness analysis of distributed CFAR detection with K-out-of-L fusion in heterogeneous environments

10.12928/telkomnika.v24i5.27784
Hung Van; Le Quy Don Technical University Pham , Tinh Dinh; Le Quy Don Technical University Nguyen
To operate effectively in complex environments, distributed radar systems re quire a robust constant false alarm rate (CFAR) processor. Conventional distributed cell-averaging (D-CA) CFAR detectors are optimal in homoge neous noise. However, they suffer severe performance degradation in non homogeneous scenarios, specifically under multiple interfering targets and abrupt clutter edges. This study presents a comprehensive performance analysis of distributed order statistics (D-OS) CFAR systems employing the K-out-of-L fusion rule. We derive the exact global detection and false alarm probabilities using the Poisson binomial distribution. These models are implemented via vari able precision arithmetic to ensure numerical stability under extreme masking conditions. Detection thresholds are rigorously calibrated to maintain a fixed global false alarm rate of 10−4 in homogeneous backgrounds. Quantitative re sults demonstrate that D-OS-CFAR significantly outperforms the D-CA-CFAR baseline. It maintains stable detection performance (PD > 0.9) under clutter power variation and suppresses false alarm surges at clutter edges to approxi mately 4.5 × 10−4. The analysis concludes that lower-K rules (e.g., K = 1,2) offer the optimal trade-off, limiting the homogeneous CFAR loss to below 0.5 dB while ensuring stable operation in highly heterogeneous environments.
Volume: 24
Issue: 5
Page: 1699-1709
Publish at: 2026-10-01

Solid oxide fuel cells as next‑generation high‑efficiency alternatives to combined cycle gas turbines

10.12928/telkomnika.v24i5.27887
Ignatius; Politeknik Negeri Bandung Riyadi Mardiyanto , Handrea; Politeknik Negeri Bandung Bernando Tambunan , Haryadi; Politeknik Negeri Bandung Haryadi , Lina; Politeknik Negeri Bandung Troskialina , Sri; Politeknik Negeri Bandung Paryanto Mursid , Yusuf; Politeknik Negeri Bandung Sofyan , Lidya; Politeknik Negeri Bandung Elizabeth
This study provides a direct thermodynamic comparison between a combined cycle gas turbine (CCGT) and a solid oxide fuel cell (SOFC) system, each designed for approximately 450 MW gross output. The benchmark CCGT, based on Siemens STG5‑4000F and SST5‑5000 units, achieved 61% gross efficiency under baseline conditions, with moderate sensitivity to fuel composition. In contrast, the SOFC system, modeled with an active cell area of 250,000 m², demonstrated superior performance, reaching nearly 70% gross efficiency and exceeding 63% net efficiency, while requiring lower fuel input (18.5 kg/s versus 19.5 kg/s for CCGT). Efficiency trends highlight fundamental differences: CCGT shows incremental gains with increased fuel flow but remains below 57% net efficiency, whereas SOFC sustains higher values despite greater sensitivity to input variations. These findings emphasize SOFC’s potential as a next‑generation utility‑scale power technology, offering higher efficiency and reduced fuel demand, while acknowledging challenges in scalability, durability, and cost that must be addressed for practical deployment.
Volume: 24
Issue: 5
Page: 1805-1815
Publish at: 2026-10-01

High-performance 28 GHz antenna array design for millimeter wave 5G systems

10.12928/telkomnika.v24i5.27857
Moussab; University Abdelmalek Essaâdi Chbeine , Abderrahman; University Abdelmalek Essaâdi Chbeine , Mohamed; University Abdelmalek Essaâdi Bayjja , Lahcen; Hassan II University of Casablanca Sellak
This paper presents the design and performance evaluation of compact 28 GHz microstrip antenna arrays for millimeter-wave (mmWave) fifth generation (5G) wireless communication applications. The proposed antennas are implemented on a Rogers RT/duroid 5880 substrate with a relative permittivity of 2.2 and a thickness of 0.508 mm and were evaluated using full-wave electromagnetic simulations in Computer Simulation Technology (CST) Microwave Studio. Starting from a single antenna element, several scalable array configurations, including 1×2, 1×4, 1×8, and 2×8 structures, were designed to improve antenna gain and radiation directivity while maintaining good impedance matching characteristics. The novelty of this work lies in the development of compact scalable antenna arrays using a simple feeding network configuration to achieve progressive gain enhancement with reduced structural complexity. The simulation results show that the antenna performance improves as the number of array elements increases, achieving a maximum gain of 16.2 dB at 28 GHz with satisfactory return loss, voltage standing wave ratio (VSWR), and radiation behavior. In addition, the proposed arrays provide an appropriate trade-off between gain, bandwidth, and compact size, making them suitable candidates for future high-data-rate mmWave 5G communication systems.
Volume: 24
Issue: 5
Page: 1492-1503
Publish at: 2026-10-01

MRI segmentation and classification using 1-D and 2-D empirical mode decomposition

10.12928/telkomnika.v24i5.27976
Giang Hong; University of Science Nguyen , Yen Thi Hoang; University of Science Hua , Ngan Vuong Thuy; University of Science Nguyen , Liet Dang; Dong Nai University Van
This study proposes a new framework for brain magnetic resonance imaging (MRI) tumor detection and classification using 1-D and 2-D empirical mode decomposition (EMD) / bidimensional empirical mode decomposition (BEMD). These adaptive algorithms optimize image denoising, segmentation and feature extraction. Tumors are detected using a global threshold derived from EMD-generated bimodal curves. A visual geometry group 19 (VGG19) model extracts features from the regions of interest (ROI) across both denoised images and BEMD components. The method was validated on the public Figshare brain tumor dataset. For segmentation, the proposed method performed similarly to multilevel Otsu, Kapur-Harmonic, and K-means algorithms. It achieved an average precision of 96.70%, slightly trailing K-means of 97.07%, and a sensitivity of 91.07%, outperforming K-means but falling behind Kapur and Otsu. Notably, it achieved the highest F1-score of 93.70%, tying the Otsu technique. These results highlight enhanced tumor localization, reflecting segmentation that closely aligns with the ground truth. For tumor classification, ensemble learning was used to develop nine models, all of which surpassed 93% accuracy; one achieved 100%, and three reached 96.7%. The proposed method outperformed seven state-of-the-art techniques with a peak accuracy of 99.02%, demonstrating its clinical viability. Overall, integrating EMD and BEMD algorithms provides a superior framework for brain tumor segmentation and classification compared traditional methods.
Volume: 24
Issue: 5
Page: 1728-1744
Publish at: 2026-10-01

Hybrid feature selection strategies for strong network intrusion detection

10.11591/ijai.v15.i5.pp4366-4380
Josephine Rathinam , Anandaraj Shanthi Pichandi
High dimensional network traffic datasets pose great challenges to network intrusion detection systems (NIDS) due to redundant and irrelevant features, which decrease detection accuracy and increase the computational cost. Traditional feature selection methods often cannot achieve a balance between classification performance and processing efficiency. This paper proposes hybrid feature selection for network intrusion detection systems (HybFS-NIDS), a hybrid feature selection framework using filter- and wrapper-based optimization methods to detect the most significant intrusion detection features to do this. The proposed model was evaluated on the CIC-IDS2017 dataset using performance metrics such as accuracy, precision, recall, F1-score, and Matthew’s correlation coefficient (MCC). The experimental results selected 18 optimal features from the initial 78 features of the dataset and achieved 90.68% accuracy, 91.74% precision, 90.64% recall, 91.19% F1-score, and an MCC of 0.9148. The proposed method also achieved a 41.5% reduction in training time compared with the full-feature baseline model while improving the overall classification performance. The results show that HybFS-NIDS can successfully improve the efficiency of intrusion detection and fit for real-time cybersecurity applications with a large amount of network traffic data.
Volume: 15
Issue: 5
Page: 4366-4380
Publish at: 2026-10-01

LLM-FS-Agent: a deliberative role-based large language model architecture for transparent feature selection

10.11591/ijai.v15.i5.pp4026-4038
Mohamed Bal-Ghaoui , Fayssal Sabri
High-dimensional data can reduce interpretability and increase computational cost in machine learning (ML) pipelines. Large language models (LLMs) offer a promising approach for dimensionality reduction through feature selection (FS), but existing LLM-based methods lack structured reasoning and do not provide clear justifications for their choices. This study propose LLM-FS-Agent, a multi-agent architecture where specialized LLM roles collaborate through a deliberative process to evaluate and rank features. Each selection decision is accompanied by an explicit rationale. We tested our method on the CIC-DIAD 2024 internet of things (IoT) intrusion detection dataset and compared it against LLM-select and traditional techniques like principal component analysis (PCA) across multiple subset sizes. LLM-FS-Agent achieves comparable or better accuracy while reducing classifier training time by 46% on average (statistically significant, 0.094 s, p = 0.028 for extreme gradient boosting (XGBoost)). These results show that multi-agent deliberation improves transparency and computational efficiency in FS.
Volume: 15
Issue: 5
Page: 4026-4038
Publish at: 2026-10-01

Improving PaddleOCR optical character recognition for food composition labels using hybrid SymSpell and LayoutLM correction

10.12928/telkomnika.v24i5.27607
Ahmad; Universitas Qomaruddin Wahyu Rosyadi , Siti; Universitas Qomaruddin Ma’shumah , Muhammad; Institut Teknologi Sepuluh Nopember Qomaruz Zaman
A high level of text recognition accuracy is crucial for optical character recognition (OCR) applications, particularly those designed to extract information from food composition labels. However, packaging materials often introduce distortions, reflections, or irregular printing, which lead to recognition errors and word concatenation. To mitigate this issue, this study proposes a novel post-processing system that integrates PaddleOCR with the spelling correction (SymSpell) algorithm and layout language model (LayoutLM)-guided bigram-based word segmentation to enhance OCR accuracy. The implementation begins with capturing a composition label image using a smartphone camera, followed by initial text extraction performed by PaddleOCR. Consequently, SymSpell corrects misspelled tokens through a domain-specific food and beverage lexicon, while LayoutLM leverages spatial and contextual information to separate concatenated words. The performance was evaluated using the character error rate (CER) metric across four packaging surface categories: flat, curved, reflective, and textured. The results demonstrate consistent improvements across all categories, achieving an average CER of 0.0991, with the greatest reduction on flat surfaces (from 0.0885 to 0.0659). The experiments show that the system is robust across label conditions, lightweight for real-time use, and domain-aware. Its model-agnostic design adds flexibility, enabling effective post-OCR correction across different engines and improving readability for real-world food label applications.
Volume: 24
Issue: 5
Page: 1668-1679
Publish at: 2026-10-01

Binary BAT algorithm with greedy repair for the discounted 0-1 knapsack problem

10.11591/ijece.v16i5.pp2680-2687
Tung Khac Truong
The discounted 0-1 knapsack problem (D0-1KP) generalizes the classical knapsack problem by partitioning items into groups of three, in which the third item of every group represents a discounted bundle of the first two and at most one item per group may be loaded. This grouping constraint enlarges the search space and makes the problem markedly harder than its classical counterpart. This paper presents BBAT, a binary bat algorithm for the D0-1KP. Bat velocities are mapped to selection probabilities through a sigmoid transfer function, and a greedy repair-and-optimization operator restores feasibility while raising the profit of every candidate solution. The algorithm keeps the small parameter set of the original bat metaheuristic, which limits the tuning effort required before deployment. BBAT was assessed on 14 benchmark instances drawn from the inverse strongly correlated and strongly correlated families, with 30 independent runs per instance. Against two elite genetic algorithms, BBAT improved the best profit found on 12 of the 14 instances and improved the mean profit on 7 of them. The gain in mean profit is concentrated on the inverse strongly correlated family, whereas on strongly correlated instances BBAT attains better peaks at the cost of higher run-to-run variance. These results identify BBAT as a competitive but variance-sensitive solver for the D0-1KP.
Volume: 16
Issue: 5
Page: 2680-2687
Publish at: 2026-10-01

Real-time depth measurement and stability control of AUV using regression approximation and filtered pressure data

10.11591/ijece.v16i5.pp2417-2430
Senanjung Prayoga , Dhaniel Beny Wardhana , Ryan Satria Wijaya
This paper presents the development and experimental validation of a prototype-scale autonomous underwater vehicle (AUV) depth control system using a proportional-integral-derivative (PID) controller with depth feedback from a SEN0257 water-pressure sensor. Raw sensor readings are filtered and calibrated using linear regression, reducing the depth estimation error, as indicated by a decrease in root mean square error (RMSE) from 1.88 to 0.63 cm. The calibrated depth signal is implemented in real time as the feedback source for closed-loop control on the testbed. Controller performance is evaluated by comparing two tuning strategies: Ziegler–Nichols (ZN) closed-loop tuning and manual fine-tuning. Experiments were conducted at depth setpoints of 70 and 100 cm under consistent pool conditions, and additional trials were performed while the AUV executes forward motion to assess robustness under dynamic disturbances. System responses are quantified using rise time, overshoot, settling time, and steady-state error. Results show that calibration significantly improves sensor suitability for feedback, while the fine-tuned PID controller produces a more stable depth response with lower overshoot, smaller steady-state error, and shorter settling time than the ZN controller, despite the faster initial rise achieved by ZN tuning. Overall, combining calibrated pressure-based depth estimation with fine-tuned PID gains enables stable and accurate depth regulation for prototype AUV operation.
Volume: 16
Issue: 5
Page: 2417-2430
Publish at: 2026-10-01

BDLock: A blockchain-enabled two tier privacy-aware federated idam service platform using RBAC

10.11591/ijece.v16i5.pp2537-2548
Muhammad Shakil Pervez , Md. Nasim Adnan , Sarker Tanveer Ahmed Rumee , Moinul Islam Zaber
Centralized identity and access management (IDAM) systems suffer from sin-gle points of failure, lack of authorization transparency, and susceptibility to in-sider threats and privilege abuse. While role-based access control (RBAC) of-fers structured permission management, its enforcement through centralized pol-icy engines introduces auditability gaps unacceptable in modern distributed service delivery environments. This paper presents BDLock, a blockchain-enabled two-tier privacy-aware federated IDAM platform integrating OAuth 2.0, OpenID Connect (OIDC), and Hyperledger Fabric 2.4. The first tier validates JSON Web Tokens (JWT) issued by Keycloak against a Spring Boot resource server, the second tier enforces immutable scope-based RBAC rights on the Hyperledger Fabric ledger, ensuring every access decision is tamper-proof and auditable. Unlike prior approaches, BDLock uniquely bridges OAuth-authenticated off-chain identities to cryptographic on-chain Fabric wallet identities, satisfying all six STRIDE-modelled threats categories across both Web2 and Web3 identity models. Validated with up to 1,800 concurrent users, BDLock achieves a peak throughput of approximately 200 transactions per second using round-robin load balancing. At high concurrency, it outperforms single-peer fallback by up to 25%. Furthermore, it maintains uninterrupted access control during peer failures, eliminating the single point of failure found in all nine compared state-of-the-art systems.
Volume: 16
Issue: 5
Page: 2537-2548
Publish at: 2026-10-01

Calibration-guided score fusion for robust multimodal traffic anomaly detection

10.11591/ijece.v16i5.pp2516-2525
Quang Hiep Do , Thien Tan Nguyen
Multimodal traffic anomaly detection is affected by differences in visual and audio score ranges, temporal fluctuations, and unstable decision thresholds. This paper proposes a calibration-guided score fusion (CGSF) framework that processes video frames and audio spectrograms through separate reconstruction-based models. The resulting anomaly scores are temporally smoothed, normalized using validation data, and combined at the score level. A percentile estimated from normal validation samples is then used as the decision threshold. The framework was evaluated on the MAVD and DADA2000 datasets. On MAVD, CGSF achiev,,,,,,ed a ROC-AUC of 0.553, a PR-AUC of 0.082, and an F1-score of 0.129. It outperformed direct fusion in precision, recall, and F1-score, although the gain in ROC-AUC was small. Analysis on DADA2000 showed smoother temporal score behaviour after calibration and smoothing. The results indicate that CGSF mainly improves score comparability and threshold consistency rather than producing a large increase in detection accuracy. Its modular design also allows the visual and audio branches to be trained and updated independently.
Volume: 16
Issue: 5
Page: 2516-2525
Publish at: 2026-10-01

A compact tri-band THz patch antenna using slot-loaded radiator and defected ground structure

10.11591/ijece.v16i5.pp2549-2558
Tran-Thi Bich Ngoc , Truong Thi Phuong Nhi
Terahertz (THz)- based antennas are essential for future 6G sensing and short range communications, especially when multi-band flexibility and compact di mensions are required. This paper presents a compact microstrip patch an tenna designed for THz applications with tri-band operation. The antenna is formed out of a rectangular patch filled with slots, a defective ground structure (DGS), and a microstrip feedline. Slot loading and the DGS generate various current paths, exciting several resonant modes and enhancing impedance match ing across the resonant frequency range. The proposed design is implemented on a 30 µm thick RO3003 substrate with a relative permittivity of 3 and loss tan gent of 0.001. The antenna has physical dimensions of 0.27×0.28×0.03 mm3. Simulation results show the antenna operates at three frequencies with a remark ably low return loss of up to-61.35 dB over the range of 1.59-3.2 THz. It offers tri-band gains of 6.01 dBi, 7.30 dBi, and 7.31 dBi, with a voltage standing wave ratio (VSWR) close to the ideal value of 1 for most bands. Furthermore, field distributions and power breakdown calculations are performed to better under stand the radiation mechanisms and efficiency characteristics at THz frequen cies. The antenna radiated power across the band, achieving up to 86.5% of the power accepted. These results indicate that the suggested design provides tri-band capabilities while being a simple, compact and efficient candidate for the demand for improved antennas in next-generation wireless communication systems.
Volume: 16
Issue: 5
Page: 2549-2558
Publish at: 2026-10-01
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