Articles

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

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

30,895 Article Results

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

Evaluation of the efficiency of delay tolerant network routing protocols for smart environment development in Makassar

10.12928/telkomnika.v24i4.27102
Abdul; Universitas Handayani Makassar Latief Arda , Symsu; Universitas Handayani Makassar Alam , Agussalim; Universitas Pejuang Nasional Veteran Surabaya Agussalim , Matalangi; Universitas Kristen Indonesia Paulus Matalangi
Delay tolerant network (DTN) has become a promising communication paradigm for internet of things (IoT)-based smart environments, where intermittent connectivity and heterogeneous node mobility challenge reliable data delivery. However, selecting an appropriate DTN routing protocol remains difficult because existing protocols offer different trade-offs among delivery ratio, latency, communication overhead, hop count, and energy consumption. Although many studies have evaluated DTN routing protocols, few have examined their performance under heterogeneous urban mobility conditions representing emerging smart cities such as Makassar. This study compares four DTN routing protocols Epidemic, probabilistic routing protocol using history of encounters and transitivity version 2 (PRoPHETv2), spray and wait, and MaxProp using The ONE Simulator with a Makassar-inspired mobility scenario involving 50–125 nodes over a 12-hour simulation. Performance was evaluated using delivery ratio, average latency, overhead ratio, average hop count, and estimated energy consumption. The results show that MaxProp achieved the highest delivery ratio (0.69 at 125 nodes), whereas spray and wait consistently produced the lowest latency (≈118 s), overhead ratio (< 45,000), and average hop count (≈2). In contrast, epidemic generated excessive overhead and energy consumption because of uncontrolled message replication. These findings indicate that spray and wait is the most resource-efficient protocol, while MaxProp is preferable for applications prioritizing delivery reliability.
Volume: 24
Issue: 4
Page: 1143-1156
Publish at: 2026-08-01

Scenario-driven fault injection for realistic bugs in web application testing

10.11591/ijece.v16i4.pp2014-2030
Asri Maspupah , Joe Lian Min , Yadhi Aditya
Conventional mutation-based fault injection techniques generally produce single-line syntactic faults, which often fail to represent realistic errors at the functional requirement level because requirement context, execution paths, and functional dependencies are not considered. To address this limitation, this study proposes scenario-driven fault injection (SDFI), a scenario-based fault insertion approach that derives faults from functional requirements and test cases. SDFI integrates operational fault localization, web fault taxonomy, fault injection patterns, and functional scenario mapping to produce targeted fault injections at relevant code locations, resulting in a realistic bug dataset with multi-line faults. An experimental evaluation on a real web application produced 29 mutants, achieving a fault detection rate of 89.29% based on the RIP model. Further analysis shows that the generated mutants replicate common real-world bug characteristics, including logic errors, validation anomalies, inter-function data propagation, and multi-line faults affecting client–server application behavior. These results demonstrate that SDFI is effective in producing realistic bug datasets for evaluating software testing quality, improving test case effectiveness, and supporting further research on requirement-based fault realism.
Volume: 16
Issue: 4
Page: 2014-2030
Publish at: 2026-08-01

NLP-driven hate speech detection on TikTok: a case study from UIN Sunan Ampel Surabaya

10.12928/telkomnika.v24i4.27419
Achmad; UIN Sunan Ampel Surabaya Teguh Wibowo , Aris; UIN Sunan Ampel Surabaya Fanani , Mujib; UIN Sunan Ampel Surabaya Ridwan , Bramasta; UIN Sunan Ampel Surabaya Kurnia Aji
This study examines hate speech detection in TikTok comments using natural language processing (NLP) techniques within the student community of UIN Sunan Ampel Surabaya. A dataset of 10,000 comments associated with the hashtag #PBAKUINSA2023 was analyzed using a lexicon-based sentiment analysis approach implemented through the TextBlob library, combined with Indonesian text preprocessing techniques, including tokenization, normalization, stopword removal, and stemming using the Sastrawi library. The results indicate that the proposed approach achieved an accuracy of 0.85, with precision of 0.88, recall of 0.83, and an F1-score of 0.854. Most comments were classified as neutral, while 31.8% were positive, and only a small proportion were negative. These findings suggest that discussions related to campus activities tend to be neutral or supportive. However, the findings also reveal that sentiment polarity does not always directly correspond to hate speech, as certain harmful expressions may appear neutral in lexicon-based analysis. This limitation highlights the need for more context-aware approaches. Overall, the proposed method provides an efficient solution for monitoring online discourse in academic environments.
Volume: 24
Issue: 4
Page: 1157-1167
Publish at: 2026-08-01

Immersive technology in English language learning: a bibliometric analysis

10.11591/ijere.v15i4.37947
Zhou Bo , Lim Seong Pek , Nahdia Kabir , Mohamed Bouteraa , Asna Asna
This bibliometric analysis, drawing on data from the Web of Science (WoS) core collection, explores the expanding role of immersive technologies in English language education. Virtual reality (VR) and augmented reality (AR) have shown strong potential to improve learner motivation, engagement, and communicative competence, yet their integration into formal English language settings remains uneven. By analyzing 248 peer-reviewed articles published between 2021 and 2025, this study finds significant trends, influential contributors, and emerging areas of interest within the field. The findings show a steady increase in publications and citations, reflecting growing recognition of the educational value of immersive environments. Prominent themes include emotional engagement, lowered language anxiety, and improved performance in vocabulary, speaking, listening, and cultural understanding. Much of the literature underlines authentic and situated learning, VR-based interactive environments, VR-supported problem-based learning, and AR-assisted vocabulary development. The analysis also identifies leading countries, with China and the United States producing the largest share of research, a pattern supported by strong institutional participation worldwide. These insights help guide educators and policymakers as they consider how to bring immersive technologies into English instruction. The study also establishes a foundation for future research on effective, engaging, and sustainable immersive language learning practices. Overall, these findings clarify how research on immersive technologies in English language education has evolved between 2021 and 2025 and identify influential studies and key contributors. They also point to persisting gaps, such as equity, teacher readiness, and cognitive-load–informed design, that warrant further investigation.
Volume: 15
Issue: 4
Page: 3623-3635
Publish at: 2026-08-01

Pedagogical environments for the formation of research competence in students within practice-oriented chemistry education

10.11591/ijere.v15i4.39216
Kanat Sadykov , Nesipkhan Bektenov , Nursulu Zhussupbekova , Ainash Baidullayeva
Despite the importance of research competence in modern chemical education, traditional instructional methods often fail to sufficiently prepare students for independent scientific inquiry and professional growth, creating a gap between theoretical knowledge and practical application. The research aimed to experimentally validate a multifaceted system of pedagogical environments (research-focused, metacognitive-digital, and motivational) designed to foster research competence in chemistry students through practice-oriented learning. The research design of the research was a 30-week longitudinal pedagogical experiment utilizing a quasi-experimental design with pre- and post-test assessments. The sample comprised of 325 third- and fourth-year future chemistry teachers (experimental group (EG)=165; control group (CG)=160) from a national university in Kazakhstan. Statistical analysis (Pearson χ² test) confirmed significant growth in the EG across all competence criteria, with the proportion of students reaching “high-level” research proficiency increasing from 10.9% to 38.8%. The key implication is integrating structured pedagogical environments into teacher-training curricula effectively bridges the gap between theoretical knowledge and practical inquiry, providing a scalable framework for modernizing science, technology, engineering and mathematics (STEM) education.
Volume: 15
Issue: 4
Page: 3463-3478
Publish at: 2026-08-01

Rethinking computer-based examinations in higher education: psychological, technical, and pedagogical challenges from students’ learning experience and future directions

10.11591/ijere.v15i4.39228
Ahmad Adnan AlZyoud , Eman Mohammad Qudah
The swift adoption of computer-based examinations (CBEs) in higher education has revolutionized assessment methods; nonetheless, there is a lack of thorough research investigating the psychological, technical, and pedagogical experiences of students using these systems. This research explores the various challenges associated with CBEs at Yarmouk University and assesses their influence on students’ learning experiences. A cross-sectional quantitative survey was conducted among 545 undergraduate students from various academic fields during the first semester of the 2025–2026 academic year. Both descriptive and inferential analyses were performed to evaluate students’ perceptions. Research shows a moderate level of acceptance for CBEs, but notable worries remain. The main source of stress was found to be technical reliability, often overshadowing worries about educational content. The one-way navigation aspect was recognized as a significant obstacle, which restricted students’ ability to review answers and added to cognitive strain and hasty decision-making. Furthermore, a significant gap in feedback was noted, as students mostly viewed the system as a tool for grading rather than a resource for ongoing learning. The research finds that successful digital assessment necessitates not just operational effectiveness but also adaptable design, alignment with pedagogical goals, and valuable feedback systems. Practical implications involve rethinking navigation elements, improving technical support, and offering focused training for faculty.
Volume: 15
Issue: 4
Page: 2861-2873
Publish at: 2026-08-01

Blended-language instructional-approach as a determinant of science learning in rural classrooms

10.11591/ijere.v15i4.39263
Atomatofa Rachel Ovuezirie , Sekegor Crescentia Ojenikoh , Avbenagha Andrew , Ewesor Stella
Scientific concepts such as gravity continue to pose challenges for students in rural and under-resourced classrooms, where reliance on a single language of instruction often restricts access to meaning and limits conceptual understanding. This study investigated the impact of a blended English and Urhobo (BEAU) instructional approach on junior secondary one students’ learning and retention of gravity concepts in rural Nigeria. A quasi-experimental pre-test–post-test non-equivalent control group design was employed with 243 students assigned to English-only, Urhobo-only, or BEAU instructional conditions. A validated 30-item multiple-choice gravity test was administered, and analysis of covariance (ANCOVA) was used to examine differences while controlling for pre-test scores. The findings show that students taught using the BEAU language approach achieved better significantly in both post-test and retention tests compared to those taught using English-only or Urhobo-only instructional approaches. The findings provide empirical evidence that the blended language instructional approach enhances science learning outcomes in rural Nigerian contexts. Linguistically responsive instructional approach improves conceptual understanding and supports long-term retention of abstract scientific ideas, underscoring the importance of leveraging students’ linguistic resources to strengthen science education in rural and under-resourced classrooms.
Volume: 15
Issue: 4
Page: 3636-3645
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
1 2 3
...
2059 2060
Show 11 of 2060

Discover Our Library

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

Explore Now
Library 3D Ilustration