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30,907 Article Results

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

Fast-decoupled power flow optimization in 20 kV systems

10.12928/telkomnika.v24i4.27567
Abrar; Lancang Kuning University Tanjung , David; Lancang Kuning University Setiawan
Power flow optimization in electrical power distribution systems is crucial for maintaining voltage stability and energy efficiency. 20 kV distribution systems often face voltage profile instability issues due to high power losses, where conventional power flow analysis methods are sometimes inefficient in handling load fluctuations in medium-voltage networks. This study proposes power flow calculation optimization using the fast-decoupled approach to evaluate and improve voltage profiles quickly and accurately, as well as integrating reactive power compensation through strategic capacitor bank placement. Simulation results show that under existing conditions, the system experiences significant voltage drops, especially at Bus 27 (17.740 kV) and Bus 53 (7.640 kV). After implementing a 2×900 kVAr capacitor bank, the voltage profile increased dramatically: Bus 27 rose to 19.160 kV and Bus 52 reached 19.020 kV. This proves that the integration of the fast decoupled method and reactive power compensation is effective in minimizing voltage deviation and improving the operational stability of the distribution system.
Volume: 24
Issue: 4
Page: 1440-1448
Publish at: 2026-08-01

Miniaturized patch antenna for the S-band communication subsystem of the 3U University CubeSat

10.11591/ijece.v16i4.pp1913-1926
Nabil El Hassainate , Loubna Berrich , Nabil Benjelloun , Ahmed Oulad Said , Zouhair Guennoun
This paper introduces a miniaturized patch antenna for the reception module of the 3U University CubeSat in the S-band communications subsystem. In order to reduce the physical characteristics of the antenna (dimensions, mass) and achieve circular polarization (CP), as well as increasing its performances, two techniques are used: the first consists of introducing semicircle truncation on both sides of the square patch, and the second consists of modifying the ground plane with networks of symmetrical slots along the main axes (x,y). The fabricated antenna prototype has overall dimensions of 55×55×3.27 mm and a total mass of 20.59 g. The developed antenna spans the uplink band (2.025 to 2.110 GHz) for payload and telemetry operations. The designed antenna achieves a reflection coefficient below minus 10 dB across the target frequency band, along with a minus 3 dB axial ratio bandwidth that is well appropriate to space communication links. The comparisons of the prototype results to the simulation results using CST and HFSS provide close agreement of around 90%.
Volume: 16
Issue: 4
Page: 1913-1926
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

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

Beyond coal: optimization of hybrid floating PV–hydropower systems for Indonesia’s decarbonization

10.12928/telkomnika.v24i4.27858
Firsta; IPB University Zukhrufiana Setiawati , Tania; IPB University June , Muh; IPB University Taufik , Rudi; Tanjungpura University Kurnianto
Indonesia’s failure to meet the 23% renewable energy target, resulting in continued reliance on fossil fuels, necessitated a multi-criteria evaluation of hybrid energy systems. This study aimed to simultaneously minimize the levelized cost of energy (LCOE), reduce carbon dioxide (CO2) emissions (decarbonization), and maximize the renewable energy penetration by integrating a coal-fired power plant (coal), hydroelectric power plant (hydro), and floating photovoltaic (FPV) power plant at sites in western Java (West Java and Banten provinces). Thirteen configurations were evaluated using European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 climate data (1991-2020) and hybrid optimization of multiple energy resources professional (HOMER Pro) simulations. The optimal coal hydro-FPV-Li-Ion hybrid configuration achieved 91.1% renewable energy penetration, 94.5% decarbonization, avoided ~4.1 million kg CO2 annually, delivered an LCOE of $0.1201/kWh with an internal rate of return (IRR) of 7.6%, return on investment (ROI) of 5.9%, and payback period of 8.7 years. This configuration outperformed the pumped hydro storage alternative, demonstrating 38% lower capital expenditure (CAPEX) and 49% faster payback period, confirming its economic suitability for Indonesia’s fiscal constraints. These findings indicate that a hybrid energy system with battery storage technology is a technically and economically viable pathway toward Indonesia’s decarbonization agenda.
Volume: 24
Issue: 4
Page: 1427-1439
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

Privacy-preserving messaging for medical device networks

10.12928/telkomnika.v24i4.27794
Rachmad; Universitas Brawijaya Andri Atmoko , Salnan; Universitas Brawijaya Ratih Asriningtias , Akas; Politeknik Negeri Jember Bagus Setiawan , Devasis; Acharya Institute of Technology Pradhan , Ismail; Karabuk University Rakip Karas
Internet of medical things (IoMT) deployments rely on lightweight messaging, but the message queuing telemetry transport (MQTT) protocol still exposes sensitive metadata through plaintext topic names and stable client identifiers. In healthcare settings, this visibility can reveal patient identity, location, and monitored condition even when payloads are encrypted. This paper presents a gateway-based privacy architecture that replaces semantic MQTT topics and client identifier (ClientIDs) with rotating pseudonyms managed by a topic and ID privacy manager (TPM). The design uses hash-based message authentication code using secure hash algorithm 256-bit (HMAC-SHA256) for pseudonym generation, advanced encryption standard-galois/counter mode (AES-GCM) for payload protection, and a short overlap phase that preserves message delivery during rotation without modifying the broker. Experiments from 5 to 10,000 patients show a consistent 5.0x increase in topic diversity, 2.50 μs per message cryptographic overhead, 0.063 ms maximum latency overhead, and zero packet loss. These results indicate that practical IoMT deployments can improve metadata privacy while still meeting real-time clinical communication requirements.
Volume: 24
Issue: 4
Page: 1320-1330
Publish at: 2026-08-01

An improved harvested energy management mechanism for wireless sensor networks

10.12928/telkomnika.v24i4.27486
Abdelmalek; University of Ibn-Khaldoun Bengheni , Messaoud; Ahmed Ben Yahia El Wancharissi University Hameurlaine
Wireless sensor networks (WSN) play a vital role in monitoring and communication applications, but their performance is often constrained by limited battery power. Energy harvesting (EH) technologies have emerged as a promising solution to extend network lifetime by supplying supplementary energy from the environment. However, efficiently balancing harvested and consumed energy remains a significant challenge. This paper introduces an improved harvested energy management mechanism (IHE2M) that dynamically adjusts the duty cycle of sensor nodes based on residual energy availability. Unlike traditional approaches that rely on fixed duty cycles, IHE2M allows each node to determine its sleep and active periods adaptively, reducing collisions, idle listening, and unnecessary retransmissions. The mechanism was evaluated through OMNeT++/MiXiM simulations and compared with existing schemes such as EH2M and dynamic source routing (DSR). Results demonstrate that IHE2M achieves higher packet delivery ratios, lower latency, and better throughput while reducing average energy consumption per node. The findings confirm that IHE2M provides a more sustainable and efficient solution for energy harvesting WSN, improving reliability and extending network lifetime.
Volume: 24
Issue: 4
Page: 1102-1112
Publish at: 2026-08-01

A convolutional neural network -based driver monitoring system for drowsiness and distraction detection

10.11591/ijece.v16i4.pp2220-2229
Sara Benkouider , Nasreddine Lagraa
Road accidents caused by driver drowsiness and distraction are a major global concern, as fatigue and inattention significantly slow reaction time and increase accident risk. To address this issue, this paper proposes a vision-based driver monitoring system using facial analysis from an on-board camera. The system detects the face and extracts key regions of interest, including the eyes, mouth, and head, which are analyzed independently using convolutional neural networks. Temporal information is captured by aggregating the convolutional neural network (CNN) outputs over a fixed time window. Drowsiness is estimated by fusing eye and mouth features with a multilayer perceptron, while distraction is detected based on head movements. An important advantage of the proposed approach is its robustness to partial input loss, allowing the system to remain functional even when some facial regions are missing or occluded, such as when wearing sunglasses or face masks. Experimental results show high detection accuracy, reaching 97.3% for drowsiness and 98% for distraction under ideal conditions, with only limited performance degradation in challenging scenarios. These results confirm the suitability of the proposed system for real-time driver monitoring applications.
Volume: 16
Issue: 4
Page: 2220-2229
Publish at: 2026-08-01

Physics-based modeling of cobalt-doped nickel-zinc on-chip ferrite inductors

10.11591/ijece.v16i4.pp1805-1816
Bambang Mulyo Raharjo , Dicky Rezky Munazat , Sudirman Rohadi
The miniaturization of integrated voltage regulators (IVRs) for multi-core processors is fundamentally bottlenecked by the high-frequency magnetic losses of conventional inductor cores. This study presents a rigorous computational framework to optimize Cobalt-doped Nickel-Zinc ferrite (Ni_(1-x) Zn_0.4 Co_x Fe_2 O_4) for 10 MHz on-chip power delivery. Utilizing Landau-Lifshitz-Gilbert (LLG) relaxation dynamics and Maxwell-Wagner interfacial polarization, the complex electromagnetic dispersion was modeled and quantitatively validated against recent empirical literature. Furthermore, high-temperature power loss density was bounded using the trust region reflective (TRF) numerical curve fitting algorithm to validate an anisotropy-compensated "thermal valley" at 80 °C. A multi-objective sensitivity analysis identified a high-efficiency Cobalt "sweet spot" at a concentration of x=0.04. This specific formulation optimally stiffens domain walls, safely shifting the resonance frequency to 40 MHz and maximizing the quality factor (Q) at the 10 MHz operational target. When applied to a simulated 3.5 V to 1.0 V DC-DC buck converter, the optimized x=0.04 core demonstrated its adequacy for on-chip applications by maintaining >90% power efficiency under a rigorous 2.0 A load. These predictive results mathematically prove that precision Cobalt doping is a highly viable strategy for suppressing parasitic losses in next-generation 3D-IC power delivery networks.
Volume: 16
Issue: 4
Page: 1805-1816
Publish at: 2026-08-01

Semantic-aligned multimodal human activity recognition using visual and audio data

10.11591/ijece.v16i4.pp2087-2095
Yeeun Park , Junhoo Byun , Siwoo Byun
Human activity recognition (HAR) requires robust performance under heterogeneous sensing conditions for practical deployment. However, single-modality approaches are limited in capturing the rich contextual information inherent in complex human behaviors. This paper presents a semantic-aligned multimodal HAR framework that integrates visual and audio information without assuming instance-level synchronization. To address dataset heterogeneity, samples from the HMDB51 video dataset and the ESC-50 audio dataset are aligned by mapping fine-grained classes into a shared high-level activity label space. For each modality, ResNet-18-based models are trained independently using frame-based visual inputs and 64-bin Mel-spectrogram-based audio representations. During inference, the output logits of the two models are combined through score-level weighted linear fusion. Experimental results show that the proposed multimodal approach consistently outperforms unimodal baselines in terms of accuracy and Macro-F1 score, with particularly notable improvements in activity groups where environmental context plays a significant role. These findings indicate that semantic-aligned score-level fusion can improve recognition robustness even under mismatched dataset conditions.
Volume: 16
Issue: 4
Page: 2087-2095
Publish at: 2026-08-01

Flicker noise suppression and tuning range linearization techniques for RF CMOS voltage-controlled oscillators

10.11591/ijece.v16i4.pp1792-1804
Nam-Jin Oh
This paper proposes a differential RF CMOS voltage-controlled oscillator (VCO) employing a series LC (SLC) network to suppress 1/f³ flicker noise and linearize the tuning range. The SLC network incorporates two coupling capacitors connected to each node of a parallel inductor-varactor tank. Each series connection node is cross-coupled to the gates of switching transistors, facilitating a large signal swing. By optimizing the coupling capacitance, 1/f³ flicker noise is effectively mitigated. Designed in 180 nm CMOS technology, the proposed NMOS-only SLC VCO is compared with a conventional differential VCO using a parallel LC (PLC) network. Targeted for 3.3 GHz applications, the VCO maintains a consistent phase noise slope of −20 dB/decade across offset frequencies from 1kHz to 10 MHz. The SLC VCO achieves a phase noise of −71.6 dBc/Hz at a 1 kHz offset and −131.5 dBc/Hz at a 1 MHz offset with a power consumption of 11.9 mW from a 1.5 V supply. The resulting figure of merit (FOM) is 191.3 dBc/Hz at a 1 MHz offset.
Volume: 16
Issue: 4
Page: 1792-1804
Publish at: 2026-08-01

From climate time series to planting windows in chili (Capsicum frutescens): a SARIMA–SVM–XGBoost framework with balanced-accuracy thresholding

10.11591/ijece.v16i4.pp2210-2219
Efrans Christian , Nova Noor Kamala Sari , Ressa Priskila , Septian Geges
This study proposes a spatio-temporal decision-support framework that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) to derive adaptive planting windows for chili (Capsicum frutescens) at the sub-district level. The framework addresses key challenges in climate-sensitive agriculture, including spatial data leakage and class imbalance, by employing Leave-One-Group-Out (LOGO) cross-validation and Balanced Accuracy–based threshold optimization. The proposed system transforms heterogeneous environmental data into actionable recommendations by combining climate forecasting, land suitability assessment, and yield prediction within a unified pipeline. Experimental results indicate that the framework effectively captures seasonal climate dynamics and produces consistent planting recommendations aligned with agronomic conditions, enabling multiple planting cycles per year. The primary contribution of this work lies in a transparent and generalizable integration of statistical and machine learning models into a practical decision-support framework. The proposed approach bridges predictive modeling and real-world agricultural decision-making and can be extended to other crops and regions for climate-adaptive agricultural planning.
Volume: 16
Issue: 4
Page: 2210-2219
Publish at: 2026-08-01
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