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30,185 Article Results

Dynamic pooling using average-thresholding to improve image classification performance

10.12928/telkomnika.v24i2.27619
Pajri; President University Aprilio , Tjong Wan; President University Sen
Pooling layers are essential in convolutional neural networks (CNNs) for reducing data size while preserving key features. Traditional methods such as Max and Average pooling have limitations. Max pooling is sensitive to noise, while Average pooling treats all activations equally. Although T-Max-Avg pooling addresses these limitations through adaptive top-k selection, its rigid decision rule requires multiple threshold comparisons and limits efficiency, motivating a simpler decision mechanism. This study introduces average-thresholding pooling (ATP), a simplified adaptive method that replaces multiple threshold comparisons with a single decision based on the average of the top-k activations. This design improves computational efficiency and reduces sensitivity to outliers. Experiments on the STL-10 dataset using a LeNet-5 architecture show that the proposed method achieves accuracy comparable to T-Max-Avg pooling (~55.5%) while consistently improving both training efficiency and inference speed. These results indicate that ATP provides a lightweight and practical alternative for CNN-based image classification, offering an improved balance between classification performance and computational efficiency.
Volume: 24
Issue: 2
Page: 663-675
Publish at: 2026-04-01

Application of the traveling salesman problem to optimize skeletonization and stroke reconstruction

10.12928/telkomnika.v24i2.27504
Alifah; Universitas Yudharta Pasuruan Alifah , Dian; National Research and Innovation Agency (BRIN) Andriana , Muhammad Zulhaj; Universitas Pembangunan Nasional Veteran Jawa Timur Aliansyah , Lukman; Universitas Yudharta Pasuruan Hakim , Kholid; Universitas Yudharta Pasuruan Murtadlo
The preservation of Turots Nusantara manuscripts written in Pegon script faces significant challenges due to physical deterioration and the complexity of handwritten styles. This study proposes a novel digitization approach based on image processing to extract and reconstruct handwriting strokes by combining skeletonization and the travelling salesman problem (TSP) algorithm. The novelty of this research lies in the application of a modified Greedy TSP algorithm capable of recognizing branching and cyclic structures typical of Arabic–Pegon characters, enabling accurate reconstruction of handwritten stroke sequences. The process involves preprocessing (grayscale, thresholding, and morphological operations), skeleton extraction using a thinning method, and weighted graph construction based on Euclidean distance between skeleton points. The proposed system achieved an average precision of 0.552, recall of 0.815, F1-score of 0.657, and accuracy of 0.82. These results demonstrate the method’s effectiveness in detecting and reconstructing character shapes from Pegon manuscripts. Practically, this approach offers potential applications in the automatic digitization, preservation, and analysis of Pegon script, contributing to the conservation of Indonesia’s Islamic intellectual and cultural heritage.
Volume: 24
Issue: 2
Page: 635-647
Publish at: 2026-04-01

Noise-suppression method for UAV-OFDM systems by introducing CV-VSS-NLMS algorithm and single-antenna architecture

10.12928/telkomnika.v24i2.27396
Walid; Université Abbes Laghrour Khenchela Lebbou , Laid; Université Abbes Laghrour Khenchela Chergui , Saad; Setif 1 University Ferhat Abbas Bouguezel
In this paper, we address the critical challenge of impulsive interference in orthogonal frequency division multiplexing (OFDM)-based unmanned aerial vehicle (UAV) communication systems, which can severely degrade data transmission reliability. Specifically, we propose a novel complex-valued variable step-size normalized least mean square (CV-VSS-NLMS) adaptive filtering algorithm dedicated for adaptive filtering of complex-valued signals, providing real-time, lightweight, and efficient impulsive-noise suppression for UAV-OFDM signals. In contrast, real-valued VSS-LMS filters treat the real and imaginary parts separately, resulting in poorer mean square error (MSE) convergence for complex signals. The algorithm is developed by efficiently adapting LMS-based filtering strategies to impulsive interference scenarios and adequately integrating prior concepts of electromagnetic pulse suppression within a well-designed single-antenna UAV architecture. This new configuration is especially suited for size, weight, and power-constrained UAV platforms, where reducing complexity is highly desirable. In contrast to conventional blind source separation approaches, the proposed solution ensures reliable communication without excessive processing demands, since it efficiently suppresses impulsive noise and greatly reduces the number of matrix operations. Simulation results demonstrate a significant improvement in bit error rate (BER), confirming that the proposed CV-VSS-NLMS technique provides a robust, dependable, and practical solution for modern UAV communication links.
Volume: 24
Issue: 2
Page: 407-419
Publish at: 2026-04-01

Hybrid intrusion detection in IoT devices: a deep learning approach using Kitsune and quantized autoencoder

10.12928/telkomnika.v24i2.27316
Md. Rifat E; Comilla University Noor , Md. Tofael; Comilla university Ahmed , Dulal; Comilla University Chakraborty , Pintu Chandra; Comilla University Paul , Sohana; Comilla University Nowar , Rejwan; Comilla University Ahmed , Tanjina; Comilla University Akter
Internet of things (IoT) has been transforming the way to connect and communicate in smart homes, healthcare, and businesses so fast and rapidly around the world. But this growth has complicated security, because IoT devices are more likely to be hacked as they’re smaller, without even regular security practices, and under attack by more sophisticated threats. Traditional intrusion detection systems (IDS) are not functioning well in IoT environments as they are computationally expensive and struggle to accommodate the heterogeneous nature of IoT networks. This paper introduces a cross-domain intrusion detection based on adaptive adversarial training using Kitsune and quantized autoencoders (QAE) for anomaly detection and classification. The model is capable of capturing different attacking techniques, such as distributed denial of service (DDoS), Mirai botnet attacks, address resolution protocol (ARP) spoofing, and data exfiltration, by leveraging the reconstruction error generated by Kitsune autoencoders. The degree-based classification enables the system to dynamically categorize anomalies according to their severity, rendering the model exceptionally adaptive to various attacks. The anomalies are also classified into different types of attacks (normal, suspicious, and malicious) based on binarized error values. The approach achieves a high accuracy with an F1 score of 85.9% and supports real-time characterization to increase security in IoT scenarios.
Volume: 24
Issue: 2
Page: 452-465
Publish at: 2026-04-01

Design of vehicle to vehicle communication: accident collision prevention using light fidelity and wireless fidelity technology

10.12928/telkomnika.v24i2.27570
Folashade Olamide; Landmark University Omua-ran Nigeria Ariba , Yusuf Isaac; Landmark University Omu-Aran Onimisi , Adedotun; Landmark University Omu-Aran Ijagbemi , Dickson Ogochukwu; Landmark University Omu-Aran Egbune
Vehicle-to-vehicle (V2V) communication is a key component of intelligent transportation systems (ITS), enabling seamless data exchange between vehicles to limit collision risks. This study presents a hybrid communication framework that integrates light fidelity (LiFi) and wireless fidelity (WiFi) technologies to enhance safety and reliability in accident prevention. Lifi using visible light communication, provides line-of-sight for short-range communication, while WiFi ensures long-range coverage in dynamic traffic environments. The proposed system allows vehicles to share speed, braking, and positional data, enabling timely warnings to drivers in high-risk scenarios. The system fuses data communication protocol design, simulation, prototype development, testing, and evaluation. The prototype model was designed and simulated to evaluate the performance of the system in terms of functionality, timing and reliability. Results indicate that the hybrid LiFi-WiFi system improves data transmission efficiency and reduces delay compared to standalone wireless systems. This approach demonstrates significant potential in developing safer transportation networks by combining complementary wireless technologies for V2V communication.
Volume: 24
Issue: 2
Page: 396-406
Publish at: 2026-04-01

Performance assessment of an adaptive model predictive control with torque braking for lane changes

10.12928/telkomnika.v24i2.27167
Zulkarnain; Universitas Sriwijaya Zulkarnain , Irwin; Universitas Sriwijaya Bizzy , Armin; Universitas Sriwijaya Sofijan , Mohd Hatta Mohammed; Universiti Teknologi Malaysia Ariff
The growing demand for autonomous vehicles requires robust control systems that can maintain safety during complex maneuvers like lane changes. However, a significant research gap exists in developing controllers that effectively manage the combined challenges of steering and braking across diverse and unpredictable driving conditions, such as varying speeds and low-friction road surfaces. This research addresses this gap by proposing and evaluating an adaptive model predictive control (MPC) system integrated with a torque braking distribution strategy. The key advantage of our adaptive method is its ability to continuously update its internal model in real-time, allowing it to anticipate and respond to changing road friction and vehicle dynamics more effectively than a static controller. In simulations of a lane change maneuver across speeds of 10-25 m/s and road friction levels from 0.3 (icy) to 1 (dry asphalt), the proposed system demonstrated a substantial performance improvement. The proposed framework demonstrated a 52.8% average reduction in lateral tracking error and enhanced stability by reducing the yaw rate by up to 41.8% on low-friction surfaces, compared to a non-adaptive MPC baseline. These results quantitatively confirm that our framework’s synergistic coordination of steering and braking significantly enhances the safety, precision, and reliability of autonomous lane change maneuvers.
Volume: 24
Issue: 2
Page: 696-706
Publish at: 2026-04-01

Attributes conducive to anthropomorphism in artificial intelligence

10.12928/telkomnika.v24i2.27483
Rizwan; Murray State University Syed , Hassan; Murray State University Mistareehi
The rapid development of artificial intelligence (AI), particularly large language models (LLMs), has generated both enthusiasm and concern regarding its role in society. While these systems demonstrate impressive technical capabilities, public acceptance is often hindered by perceptions of unpredictability, mistrust, and fears amplified by media narratives. One potential strategy to improve user acceptance is anthropomorphism, the attribution of human-like qualities to AI systems which can make interactions feel more natural and trustworthy. This paper investigates the attributes most conducive to anthropomorphism by conducting a structured review across psychology, human-robot interaction, communication studies, and business applications. The analysis identifies key traits such as emotional expressiveness, conversational coherence, adaptive social behavior, and role-based framing that enhance perceptions of AI as relatable and dependable. By synthesizing these insights, we propose a conceptual framework that highlights the psychological, social, and technical dimensions of anthropomorphism in AI. The findings provide guidance for designing AI systems that balance efficiency with user trust, thereby supporting more effective integration of AI into business, research, and everyday life.
Volume: 24
Issue: 2
Page: 588-598
Publish at: 2026-04-01

Simultaneous faults diagnosis and prognostic in induction motor drives under nonstationary conditions

10.12928/telkomnika.v24i2.27624
Ameur Fethi; University Tahar Moulay of Saida Aimer , Ahmed Hamida; University of Sciences and Technology of Oran Boudinar , Mohamed El-Amine; University of Sciences and Technology of Oran Khodja , Azeddine; University of Sciences and Technology of Oran Bendiabdellah
In this paper, an auto regressive (AR) model-based approach is applied in the stator current analysis under non-stationary conditions (case of frequency variation due to variable speed operation). Under these conditions, the identification of fault signatures is almost impossible due the variation of the fundamental frequency using conventional analysis methods. Moreover, this approach is used in the diagnosis of multiple faults occurring simultaneously in induction motor drives. In this aim, the stator current signal is decomposed into short segments then the AR modeling approach is applied on each segment. This approach called short-time ROOT-AR is then applied to solve the problem of the non-stationarity of the stator current signal under variable speed operation. The efficiency of the short-time ROOT-AR approach is evaluated through experimental tests in the diagnosis of multiple faults occurring simultaneously in induction motor drive. Finally, the superiority of the proposed approach is highlighted in comparison with conventional techniques in terms of accuracy, computational time and robustness against the noise.
Volume: 24
Issue: 2
Page: 717-726
Publish at: 2026-04-01

Evaluating learning rate effects on long short-term memory for Indonesian sentiment classification

10.12928/telkomnika.v24i2.27398
Serly; Universitas Maritim Raja Ali Haji Eldina , Tekad; Universitas Maritim Raja Ali Haji Matulatan , Novrizal Fattah; Universitas Maritim Raja Ali Haji Fahmitra
Hyperparameter optimization is a crucial process for enhancing the performance of deep learning models, particularly in the context of Indonesian sentiment classification. This study examines the impact of varying learning rates on a long short-term memory (LSTM) architecture trained with the adaptive moment estimation (Adam) optimizer. The dataset comprises 9,295 Indonesian comments automatically labeled by the Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) model. Stratified k-fold cross-validation was employed to maintain class balance during training. Learning curves were analyzed to evaluate convergence and identify potential overfitting, while early stopping was applied when performance improvements became insignificant. The one-way analysis of variance (ANOVA) test (p-adj = 0.000575 < 0.05) revealed significant differences among the learning rate variations. Post-hoc analysis indicated the learning rates of 0.0001, 0.001, and 0.002 differ significantly from 0.02. Descriptive statistics showed that a learning rate of 0.001 was the most optimal, achieving the highest validation accuracy while maintaining a relatively low variance. Evaluation across two data categories demonstrated that lower learning rates (0.0001 and 0.002) achieved the best accuracy, 78.71% on in-domain data, whereas higher learning rates (0.01 and 0.02) performed better on cross-domain data with 36% accuracy. These findings highlight the crucial role of learning rate selection in determining model stability and generalization capability.
Volume: 24
Issue: 2
Page: 564-573
Publish at: 2026-04-01

Improving multilabel classification of hate speech and abusive language in Indonesian using MAML

10.12928/telkomnika.v24i2.27332
Jasman; Institut Teknologi Nasional Bandung Pardede , Ghixandra; Institut Teknologi Nasional Bandung Julyaneu Irawadi , Rizka; Institut Teknologi Nasional Bandung Milandga Milenio
This study investigates automated multi-label detection of hate speech and abusive language (HSAL) in Indonesian social media, addressing challenges of data imbalance, especially in minority labels. Two training approaches are compared: standard supervised learning and meta-learning using the model-agnostic meta-learning (MAML) algorithm. IndoBERTweet-BiGRU is adopted as the baseline model, while MAML is leveraged to enhance generalization and adaptability with limited training data. Both models are trained on a multilabel dataset with 13 HSAL categories exhibiting highly imbalanced distributions. The best supervised model achieved an F1-Micro of 84.02% and an F1-macro of 77.97%, whereas the best MAML-trained model reached 84.12% and 76.85%, respectively. Although the overall gap is small, MAML demonstrates notable improvements on minority classes such as hate speech (HS) physical, gender, and race, shown through higher F1-score and area under the receiver operating characteristic curve (AUROC) values. These results highlight its strength in low-resource classification settings. This study is limited to Indonesian language and YouTube transcript contexts, and MAML incurs higher training complexity. Cultural and linguistic nuances also present potential bias in real-world use. Despite these constraints, the proposed system offers practical benefits by enabling fine-grained HSAL classification and supporting earlier detection of harmful online content.
Volume: 24
Issue: 2
Page: 549-563
Publish at: 2026-04-01

A comprehensive analysis of feature selection and XAI for machine learning classifiers to recognize guava disease

10.12928/telkomnika.v24i2.27599
Sujon Chandra; University of Frontier Technology, Bangladesh (UFTB) Sutradhar , Md. Mehedi; University of Frontier Technology Hasan
Recognizing and classifying diseases in guava is crucial for managing farms to keep crops healthy and increase harvest quality. Cultivators face the most severe challenges when it comes to recognizing and diagnosing guava fruit and leaf illnesses, a task that is nearly impossible to perform manually. This research focuses on developing a robust disease identification model using image data collected locally from guava trees. After data collection, various image processing techniques, including scaling and contrast enhancement, are utilized to make the data more suitable for use. K-means clustering is employed to quickly divide the images into groups, followed by the extraction of important characteristics. Two separate feature ranking approaches, analysis of variance (ANOVA) and least absolute shrinkage selection operator (LASSO), are used to select the best characteristics, identifying the 10 most important attributes. The adaptive boosting (AdaBoost) classifier achieves the highest accuracy among six classifiers for the top seven characteristics indicated by LASSO among the specified features. To enhance the model’s interpretability, two explanation methods, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP), are employed to illustrate how the classifier reaches its conclusions. This approach not only simplifies disease identification but also clarifies the reasoning behind predictions, opening the door to real-world applications in detecting and preventing dangerous diseases.
Volume: 24
Issue: 2
Page: 574-587
Publish at: 2026-04-01

The impact of EPS on procurement performance: the mediating role of supplier relationship quality in Ghana

10.12928/telkomnika.v24i2.27514
Isaac; University of Professional Studies, Accra - Ghana Asampana , Felix Acquah; Public Procurement Authority Baiden
This study examines the effect of e-procurement systems on procurement performance (PP) in Ghana, highlighting the mediating role of supplier relationship quality (SRQ). A quantitative, cross-sectional survey of 370 procurement professionals from public and private organisations was conducted to assess four dimensions of e-procurement: system integration, data transparency, user-friendliness, and automation. Results indicate that all four dimensions significantly enhance PP, with system integration and user-friendliness emerging as the strongest predictors. Mediation analysis further reveals that SRQ, characterised by trust, communication, and collaboration, partially strengthens the relationship between e-procurement and procurement outcomes. Nonetheless, challenges such as inadequate staff training, limited supplier digital skills, weak infrastructure, and insufficient managerial support hinder optimal system effectiveness. Grounded in the resource-based view (RBV) and transaction cost economics (TCE), the study demonstrates the importance of combining technological and relational capabilities. Recommendations include enhancing digital skills training, strengthening supplier engagement, improving system design, and fostering institutional support.
Volume: 24
Issue: 2
Page: 490-499
Publish at: 2026-04-01

Academic self-regulation as a bridge between mindfulness and emotional self-efficacy among undergraduate students

10.11591/ijere.v15i2.38258
Samer Adnan Abdel Hadi , Mahmoud Alquraan
The research investigated the function of academic self-regulation as a mediator in the correlation between mindful attention awareness and emotional self-efficacy. A quantitative questionnaire-based study approach was used. A total 647 undergraduate participants (77.1% female), aged 18–24 (74.5%), completed self-report questionnaires, including the mindful attention awareness scale (MAAS), the academic self-regulation scale (ASRS), and the emotional self-efficacy scale (ESES). The proposed model demonstrated acceptable global fit based on the two-index criteria applied in this study, with RMSEA meeting the ≤0.06 threshold and SRMR meeting the ≤0.09 threshold; based on the data, the model appears to be suitable. The findings indicated statistically significant relationships between mindfulness and the subscales of academic self-regulation: self-instruction and self-evaluation. Academic self-regulation showed significant path coefficients with emotional self-efficacy: self-planning, self-monitoring, and self-reaction were statistically significantly associated with using and managing one’s own emotions and perceiving emotions through facial expressions. Self-planning and self-monitoring were statistically significantly associated with dealing with emotions in others. Self-planning, self-monitoring, self-instruction, and self-reaction were statistically significant in their association with identifying and understanding one’s own emotions. Academic self-regulation elucidates the correlation between mindfulness and emotional self-efficacy. Emotional self-efficacy and well-being can be bolstered by improving mindfulness and self-regulation.
Volume: 15
Issue: 2
Page: 1212-1226
Publish at: 2026-04-01

Enhancing creative thinking skills through project-based learning and SCAMPER: a study in Vietnam

10.11591/ijere.v15i2.38148
Nguyen Thi Phuong Thanh , Le Huong Hoa
Creative thinking skills (CTS) are essential 21st-century skills for adapting to the rapidly changing world. This study aims to investigate the effectiveness of project-based learning (PBL) and substitute, combine, adapt, modify, put to another use, eliminate, reverse (SCAMPER) in enhancing high school students’ CTS, including fluency, flexibility, originality, and usefulness. This quasi-experimental study involved 120 high school students from three classes at a public high school in Vietnam. The study employed a pretest-posttest control group design with one experimental group (n=40) and all two control groups (n=80). The creative engineering design assessment (CEDA) was used. The 15-week intervention involved students in PBL-SCAMPER, while two control groups (n=40 each) followed traditional instruction. One-way analysis of variance (ANOVA) revealed significant improvements in all CTS indicators for the experimental group (p<0.05), with the largest effect on usefulness (η²=0.27). These findings suggest that the PBL-SCAMPER provides a practical pedagogical framework for developing CTS, warranting broader implementation in high school education.
Volume: 15
Issue: 2
Page: 1407-1414
Publish at: 2026-04-01

Lecturer support and student academic performance: the moderating role of age

10.11591/ijere.v15i2.35756
Noor Hafiza Zakariya , Hadziroh Ibrahim , Muhammad Waseem , Nurul Shahidah Ahmad Nasir
Lecturer support is an important factor influencing students’ academicoutcomes in higher education. This study examines the effects of lecturersupport dimensions, accessibility and approachability (AccApp), expectationand guidance (E&G), and positive encouragement (PE) on undergraduatestudents academic performance (SAP), with age tested as a potentialmoderating variable. Using a quantitative cross-sectional design, data werecollected from 250 undergraduate students at the School of BusinessManagement, Universiti Utara Malaysia (UUM), through conveniencesampling. Data were analyzed using partial least squares structural equationmodeling (PLS-SEM) with SmartPLS 4.0. The results indicate that lecturerAccApp are positively associated with academic performance, whereasexcessive expectations and directive guidance are negatively associated. PEwas not found to have a significant direct influence. Although age exhibiteda positive direct effect on academic performance, it did not significantlymoderate the relationships between lecturer support dimensions and studentoutcomes. These findings highlight the importance of accessible andequitable E&G from lecturers to enhance academic performance inMalaysian higher education. This study contributes to a thoroughunderstanding of the factors impacting SAP and offers insights for lecturers,institutions and government to work holistically to foster an inclusiveenvironment for all parties involved. Recommendations and practicalimplications for future research are discussed.
Volume: 15
Issue: 2
Page: 1237-1252
Publish at: 2026-04-01
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