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30,735 Article Results

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

Evaluating the methodological admissibility of generative AI tools for linear regression in graduate-level research

10.11591/ijere.v15i4.38496
Valery Okulich-Kazarin , Kanat Kozhakhmet
With the growing use of generative artificial intelligence (AI) in academia, a key methodological question concerns the statistical correctness of AI-assisted quantitative analysis. This study empirically evaluates the use of generative AI tools for linear regression in graduate-level research. The authors used a methodological approach in which estimates from four AI systems (ChatGPT 4.0, DeepSeek v3.2, Gemini 3 Pro, and Grok 4.1) were compared with estimates obtained using Microsoft Excel (Windows 10). The analysis was performed on five time series using a fixed prompt structure. Comparability was assessed using thresholds for regression coefficients, the coefficient of determination (R²), and predicted results for 2030. The results show that under controlled conditions and within the ordinary least squares (OLS) method, the AI tools generate statistical results with varying degrees of accuracy. However, deviations in coefficients and predictions highlight the need for systematic validation. The study concludes that AI tools can serve as auxiliary methodological support, provided transparency, reproducibility, and threshold-based verification are ensured in graduate research practice.
Volume: 15
Issue: 4
Page: 2874-2882
Publish at: 2026-08-01

Exploring the association among trait resilience, well-being, and coping strategies in Sicilian teenagers

10.11591/ijere.v15i4.37761
Sagone Elisabetta , Indiana Maria Luisa
Resilience is a personality trait strictly influenced by several protective and risk individual factors and analysis of its strengthness is useful to enhance the psychological well-being of teenagers. The aim of this quantitative cross-sectional study was to explore the relationships among resilience in terms of personality trait, psychological well-being, and coping strategies in a large group of Sicilian teenagers. We hypothesized that: the more the teenagers used functional coping strategies (e.g., active and supportive coping strategies), the more they were highly resilient, and they scored higher in dimensions of psychological well-being; the more the teenagers showed high levels of psychological well-being, the more they were highly resilient. The sample consisted of 467 teenagers (age-range: 11–13), 235 girls and 232 boys, randomly recruited from two state junior schools in Catania, Sicily (Southern Italy). For data collection, we used comprehensive inventory of thriving (CIT) for psychological well-being, resilience scale, and children’s coping strategies checklist-R1. Results indicated that there were statistically significant correlations between resilience and dimensions of psychological well-being, as well as between resilience and coping strategies. In addition, multiple regression analyses showed that the use of functional coping strategies and high values of psychological well-being had a positive impact on resilience of teenagers. Future research will compare these findings with those deriving from samples of children to highlight the presence of similarities or differences in the use of coping strategies.
Volume: 15
Issue: 4
Page: 3038-3048
Publish at: 2026-08-01

ChatGPT as scaffold: quiz performance across session complexity

10.11591/ijere.v15i4.39915
Fatima Ezzahra Kabba , Zouhair Ejbari
Many studies examine the use of ChatGPT in education, but most measure student perceptions, not performance, and few track performance across multiple sessions of different complexity. In particular, no study has tracked whether this association varies across sessions of different cognitive complexity. Using a quasi-experimental design, first-year undergraduates (N=193) at the Higher International Institute of Tourism (ISITT) in Tangier, Morocco were followed across seven introductory statistics sessions. One group had access to ChatGPT during learning activities, while the other followed the same instruction without artificial intelligence (AI) access. Performance was measured through end-of-session quizzes (1,136 observations) and analyzed using a linear mixed-effects model. No consistent overall advantage was associated with either condition. However, a significant interaction between condition and session was identified (χ²(6)=61.50, p
Volume: 15
Issue: 4
Page: 3172-3181
Publish at: 2026-08-01

Balancing support and bureaucracy: the enabling–constraining dynamics of research funding policies in a Philippine state university

10.11591/ijere.v15i4.38882
John Michael D. Aquino , Rushid Jay S. Sancon
Research funding policies has a critical role in shaping research productivity and governance in higher education institutions (HEIs). However, limited empirical studies have examined how these policies are experienced by faculty members and administrators, particularly in state universities within developing country contexts. This study explores the implementation of research funding and assistance guidelines in a Philippine state university using a phenomenological approach. Data were collected from 34 participants (15 administrators and 19 faculty members) through semi-structured interviews and document analysis and were analyzed thematically. The findings reveal an enabling–constraining dynamic in the implementation of research funding policies. Institutional support, including funding, incentives, and structured processes, enhances faculty motivation, research engagement, and planning quality. Conversely, bureaucratic complexity, procedural rigidity, and delays in fund release constrain efficiency, limit participation, and discourage sustained engagement. Anchored in self-determination theory (SDT) and structuration theory, the study demonstrates how research funding policies simultaneously influence faculty motivation and are shaped by institutional structures. These findings highlight the need for streamlined processes, consistent implementation, and faculty-centered support systems. The study contributes to the literature by providing empirical evidence of the enabling–constraining paradox of research funding policies and offers insights for improving research governance in higher education.
Volume: 15
Issue: 4
Page: 2750-2763
Publish at: 2026-08-01

Phono-syntactic error pattern analysis in impromptu speaking among English learners: a content analysis

10.11591/ijere.v15i4.39044
Angelie V. Temario , Harold John U. Mamites , April Jane G. Sales , Marcelina S. Deiparine
Second language learners, particularly Filipino learners of English, often experience linguistic difficulties in producing accurate phonological and syntactic forms, especially during spontaneous speaking tasks. This study examines the dominant phono-syntactic error patterns found in impromptu speaking performances and explores their pedagogical implications using Corder’s error analysis framework. Employing a qualitative descriptive research design, the study analyzed recorded impromptu speaking performances of 36 Bachelor of Arts in English Language (BAEL) students. The analysis revealed that in the phonological aspect, segmental errors were more prevalent than suprasegmental errors, with vowel substitution emerging as the most frequent error type, accounting for 22 errors (57.1%). In the syntactic aspect, misformation was identified as the most dominant error category, with 164 errors (54.88%). These findings suggest that while learners demonstrate emerging grammatical awareness, they still encounter difficulties in maintaining phonological and syntactic accuracy during spontaneous speech production. The study highlights the importance of integrating pronunciation and grammar instruction within communicative speaking activities. Furthermore, impromptu speaking tasks serve as an effective diagnostic tool for identifying learners’ linguistic challenges and guiding instructional strategies aimed at improving spoken language proficiency.
Volume: 15
Issue: 4
Page: 3646-3657
Publish at: 2026-08-01

Motivational and normative drivers of generative AI substitution in academic work: a mixed-methods study from Saudi higher education

10.11591/ijere.v15i4.38745
Mazin Mansory , Zilal Meccawy
The rapid integration of generative artificial intelligence (GenAI) in higher education has intensified tensions between legitimate learning support and unauthorized task substitution, particularly where institutional guidance remains ambiguous. This mixed-methods study investigates how attitudes toward AI, moral rationalization strategies, and perceived institutional clarity interact to shape AI-based substitution behavior among 249 undergraduates at a Saudi university. Partial least squares structural equation modeling (PLS-SEM) revealed that positive attitudes and rationalization together explained 46% of the variance in substitution behavior, with perceived clarity of institutional guidance significantly moderating the rationalization–substitution link. Complementary interviews with seven students and ten instructors revealed that linguistic burden, peer norms, and fragmented faculty guidance facilitated boundary crossing from scaffolding to shortcutting. By exploring the relationship between moral neutralization and environmental clarity, this research offers a new approach to evaluating the effectiveness of institutional AI guidance beyond common technology acceptance models. The findings can be used to inform the design of multi-tiered, inclusive AI usage policies, assessments that value process over product, and culturally responsive academic integrity education within a multilingual higher education context.
Volume: 15
Issue: 4
Page: 2946-2958
Publish at: 2026-08-01

Moral disengagement, character strengths, and maladaptive behavior among university students: a structural equation modeling approach

10.11591/ijere.v15i4.38470
Akhmad Syahri , Nimatul Dinawisda , Sri Afsinatun
The increasing integration of digital technology in higher education has raised concerns about students’ maladaptive behaviors (MB), including academic dishonesty and cyber aggression. This study aims to examine the structural relationships among moral disengagement (MD), character strengths (CS), and MB, as well as the mediating role of CS in digitally mediated learning environments. A cross-sectional quantitative design was employed using data collected from 500 undergraduate students at a State Islamic University in Indonesia through purposive sampling. Data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that MD significantly predicts MB (β=0.830, p
Volume: 15
Issue: 4
Page: 3049-3059
Publish at: 2026-08-01

Integration of phenomenon-based and play-based learning in primary science

10.11591/ijere.v15i4.39040
Prapatchit Techa , Sakda Swathanan , Wichaya Pewkam , Natad Assapaporn
Primary school students in Thailand continue to struggle with science process skills and creative thinking, a challenge underscored by Thailand’s lowest Programme for International Student Assessment (PISA) science scores in 20 years. Traditional instruction emphasizing content memorization while neglecting real-world application has been identified as a major contributing factor. Addressing this issue is critical, as both competencies are foundational for 21st-century learning. This study examined the effects of an integrated phenomenon-based learning and play-based learning (PhBL-PBL) innovation on Grade 2 students’ science process skills and creative thinking skills. A one-group posttest-only design was adopted with 41 students in an authentic Thai primary classroom. Data were collected using validated instruments and analyzed through descriptive statistics and one-sample t-tests against predefined performance criteria. Results showed that 92.68% of students achieved creative thinking skills at a good level or above, and 85.37% reached the same threshold for science process skills, with mean scores for both domains significantly exceeding the criterion level (p
Volume: 15
Issue: 4
Page: 3099-3110
Publish at: 2026-08-01
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