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30,938 Article Results

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

Effects of digital learning intervention on pre-service teachers’ innovation competence: evidence from Kazakhstan

10.11591/ijere.v15i4.39363
Zhazira Stambekova , Aziya Zhumabayeva , Zhanna Zhussupova , Saule Zhorayeva
Developing innovation competence (IC) is essential for pre-service teachers, as it enables them to effectively design, implement, and adapt instruction in increasingly complex and digitally enriched learning environments where technology integration, student-centered approaches, and pedagogical flexibility are critical. However, empirical evidence on how structured digital learning interventions (DLIs) foster multidimensional IC remains limited. This study examined the effect of a twelve-week structured DLI on IC in 156 third-year pre-service teachers using a quasi-experimental pretest–posttest design with a non-equivalent control group (CG). Participants completed the IC scale before and after the intervention. Results showed that the intervention significantly improved overall IC and all four dimensions: creative problem-solving (CPS), pedagogical adaptability, proactive initiative, and technology-enhanced instructional design, with the largest gains in CPS and technology-enhanced instructional design. Engagement with digital learning activities further predicted competence development, particularly in the intervention group. These findings suggest that integrating structured digital learning with intentional pedagogical design can effectively enhance innovation-oriented competencies. Teacher education programs can apply these insights to design interventions that prepare future educators for innovative, technology-rich classrooms.
Volume: 15
Issue: 4
Page: 3241-3252
Publish at: 2026-08-01

Improving students’ scientific argumentation through AI-supported feedback

10.11591/ijere.v15i4.38648
Joelash R. Honra , John Lorence A. Villamin
Scientific argumentation is a core practice in science education, yet many students struggle to construct arguments that effectively integrate claims, evidence, and reasoning. With the growing use of artificial intelligence (AI) in education, AI-supported feedback has emerged as a potential tool to scaffold students’ argumentation processes. This study examined the effects of AI-supported feedback on students’ scientific argumentation using a quasi-experimental, explanatory sequential mixed-methods design. Two intact groups participated: an experimental group receiving AI-supported formative feedback on written arguments and a control group receiving conventional teacher feedback. Quantitative data were collected using a validated rubric based on the claim–evidence–reasoning (CER) framework and Toulmin’s argument pattern (TAP), while qualitative data from student interviews and written responses provided contextual insights. Results showed that the experimental group achieved greater improvements in overall argumentation quality, particularly in evidence use and reasoning. Qualitative findings further indicated that AI feedback supported iterative revision and strengthened students’ understanding of evidence–claim relationships.
Volume: 15
Issue: 4
Page: 2741-2749
Publish at: 2026-08-01

Effect of PhET simulations and YouTube videos on polytechnic students’ conceptual understanding in fluid mechanics

10.11591/ijere.v15i4.37002
Jean D'Amour Iradukunda , Lakhan Lal Yadav
Fluid mechanics is an important branch of engineering and science with various technological and scientific applications. However, students often struggle to develop a solid conceptual understanding of related concepts due to their abstract nature, which involves invisible forces and complex scientific phenomena. This study investigated the effect of physics education technology (PhET) interactive simulations combined with educational YouTube videos on students’ conceptual understanding in ten areas of fluid mechanics. Using a quasi-experimental pre-test and post-test design, 168 construction technology students from two Rwanda Polytechnic (RP) colleges were assigned to the experimental group (n=81) and the control group (n=87). The experimental group got instruction using PhET interactive simulations and YouTube videos, while the control group was taught using traditional methods. A validated test assessed students’ conceptual understanding before and after the intervention. Descriptive and inferential statistics analyses of the study show that the students in the experimental group demonstrated far enhanced conceptual understanding in different areas of fluid mechanics than those in the control group. Results using different measures (normalized learning gains and effect sizes, using different approaches) show that the experimental group gained significantly greater improvement in their level of conceptual understanding compared to that of the control group. For example, Cohen’s d for the post-test scores for the two groups was found to be 1.85; ratios of Hake’s normalized learning gains and Cohen’s d for post- and pre-test for the experimental group to the control group were respectively 2.2669 and 2.1623. Based on these findings, the study provided actionable recommendations for educational policy and practice for polytechnic colleges.
Volume: 15
Issue: 4
Page: 3025-3037
Publish at: 2026-08-01

Developing a 2-DOF robotic arm kit for embodied AI learning in middle school technology education

10.11591/ijere.v15i4.39254
Dasol Kim , Sooin Kim
Artificial intelligence (AI) literacy is increasingly emphasized in technology education, yet many middle-school classroom activities remain screen-based and offer limited opportunities to experience the full AI pipeline in an authentic design context. To address this gap, this study developed a low-cost, classroom-ready 2-degree-of-freedom (2-DOF) robotic arm kit and an eight-lesson AI-integrated instructional unit in which students assembled the robot arm, collected and labeled image data, trained and improved a classification model, and applied model outputs to robot-arm control. A quasi-experimental pretest–posttest control-group design was employed with 98 ninth-grade students in four intact classes (experimental group, n=46; comparison group, n=52). Quantitative data were analyzed using ANCOVA with pretest scores as covariates, and qualitative data from student reflection reports were analyzed thematically. The experimental group significantly outperformed the comparison group on value of AI, efficacy of AI, AI literacy, and total scores. Qualitative findings further showed that students recognized both benefits and risks of AI and proposed multi-layered safety strategies involving physical safeguards, operational rules, and data/model management. These findings suggest that embedding the full AI pipeline in a tangible engineering-design task can support embodied, responsibility-oriented AI learning in middle school technology education.
Volume: 15
Issue: 4
Page: 3060-3074
Publish at: 2026-08-01

Digital ecopedagogy-based counseling for cyberbullying prevention among vocational high school students

10.11591/ijere.v15i4.39314
Nina Permata Sari , Hendro Yulius Suryo Putro , Muhammad Andri Setiawan
Cyberbullying has become a growing concern among adolescents in vocational high schools, particularly in Indonesian contexts where conventional counseling often struggles to address online aggression effectively. This study evaluated the effectiveness of digital ecopedagogy-based counseling (DEBC), a structured digital counseling intervention that combines interactive online modules, ecological reflection tasks, peer mentoring, and counselor-guided discussions, in preventing cyberbullying among vocational high school students in South Kalimantan, Indonesia. A quasi-experimental design involved 180 students and six school counselors from three vocational schools, with an eight-week intervention for the experimental group and conventional face-to-face counseling for the control group. Data were collected through pre-test and post-test cyberbullying behavior scales, supported by interviews, focus group discussions, and observations. The experimental group showed significantly greater reductions in cyberbullying behavior (N-Gain=0.45–0.67, p
Volume: 15
Issue: 4
Page: 3497-3507
Publish at: 2026-08-01

A study on exploring the effects of the school climate and teacher’s accountability on academic performance of students in early childhood care and education

10.11591/ijere.v15i4.36631
Kalpana Nagar , G. S. Prakasha
This study investigates parental perceptions of school climate, teachers’ accountability, and students’ academic performance, as well as the relationship among these three factors. The descriptive research uses the demographic background of parents as a stratified sampling method to collect primary data. The statistical procedure shows that Pearson’s product-moment correlations between school climate and teachers’ accountability affect the academic achievement, with effect sizes of r=0.632, r=.646, which significantly correlate with each other. Regression analysis reveals the variability of the effect sizes of school climate and teachers’ accountability on academic achievement, with the values of β=0.070 and β=0.115. The independent variable, school climate and teachers’ accountability, explains 45.4% of the variability of academic performance of the students in early childhood care and education. The present study recommends that training teachers would link their accountability and measures to track the supportive climate of school to improve the student’s academic performance. Future research should conduct longitudinal studies to examine how relationships between school climates and teachers’ accountability affect different student populations and their academic performance.
Volume: 15
Issue: 4
Page: 2883-2890
Publish at: 2026-08-01
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