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Smartphone data privacy and security awareness among university students in Malaysia

10.11591/ijece.v16i2.pp850-862
Ahmed Al-Rassas , Zaheera Zainal Abidin
This study examines the level of data privacy and security awareness (DPSA) among Malaysian university students who depend on smartphones for academic activities. An enhanced cybersecurity education (CE) technological proficiency–perceived control (CTP) model is proposed, incorporating technological innovation and cultural norms (TICN) as a mediating factor between technological proficiency (TP) and awareness. A total of 356 students from public and private institutions in Melaka participated. The Krejcie and Morgan table was used to determine the sample size. Descriptive analysis was conducted using IBM SPSS 27, and SmartPLS-SEM was used to evaluate both measurement and structural models. Reliability and validity were confirmed through a pilot study with 50 respondents. Findings show that TICN significantly strengthens the translation of technical skills into protective behavior, outperforming the original model that used frequency of smartphone usage (FSU) as a mediator. The enhanced model provides a deeper understanding of the socio-technical determinants of smartphone privacy awareness. Implications, limitations, and directions for future research are also discussed.
Volume: 16
Issue: 2
Page: 850-862
Publish at: 2026-04-01

Design and simulation of an electric vehicle charger with integrated interleaved boost converter and phase-shifted full-bridge converter using MATLAB/Simulink

10.11591/ijece.v16i2.pp687-698
Ahmad Saudi Samosir , Tole Sutikno , Alfin Fitrohul Huda , Luthfiyyatun Mardiyah
This paper presents the design and simulation of a high-efficiency electric vehicle (EV) charger that integrates a two-phase interleaved boost converter (IBC) with a phase-shifted full-bridge (PSFB) converter using MATLAB/Simulink. In contrast to existing studies that treat these converter stages independently, this work introduces a unified AC–DC–DC architecture that simultaneously minimizes input current ripple, improves DC-bus stability, and enables soft-switching operation for reduced switching losses. The values of the inductors and capacitors are derived analytically based on ripple constraints and switching frequency considerations, and example calculations are explicitly provided. Simulation results demonstrate that the proposed charger maintains a stable 600-V DC bus with less than 2% voltage ripple, delivers a controlled charging current of 100 A with 3 A ripple, and achieves an overall efficiency of 95%. These findings indicate that the integrated interleaved–PSFB topology provides superior conversion efficiency and power quality, making it a strong candidate for future EV fast-charging infrastructure.
Volume: 16
Issue: 2
Page: 687-698
Publish at: 2026-04-01

The developing a smart grid control system based on Konnex electrical equipment and internet of things technology

10.11591/ijece.v16i2.pp1020-1029
Tran Duc Chuyen , Mai Van Tao , Hoang Dinh Co
In this research, the authors present a method for developing a smart grid control system based on Konnex (KNX) electrical equipment and internet of things (IoT) technology to control and monitoring electrical energy processes such as: voltage, current, frequency, and power for independent or grid-connected power systems in industry and civil use. The system includes: KNX electrical equipment (KNX-connectivity), IoT control board, Solar panels that produce electricity to supply the system, battery storage devices, converters and controllers, power consumption loads, and many measuring, switching and protection devices for the system. With computer control programming devices, software, and control algorithms, access is possible via website, computer, smartphone, iPhone, and iPad. The goal is to monitor electricity and automatically control the smart building system, which is being used for high-end apartment buildings (luxury housing estate); offices, hotels, and garden villas. The system was researched and tested at the practice workshop for industrial factories and enterprises, bringing high results. The system aims to save energy in the context of increasingly depleted fossil energy, both in Vietnam and around the world.
Volume: 16
Issue: 2
Page: 1020-1029
Publish at: 2026-04-01

Wearable and implantable antennas for healthcare applications: advancements, challenges, and future directions

10.11591/ijece.v16i2.pp827-841
Sameera P. , Priyadarshini K. Desai , Keerthi Kulkarni
The rise of personalized and remote healthcare solutions has accelerated the demand for reliable wireless communication systems integrated into medical devices. Among these, wearable and implantable antennas play a crucial role by enabling the seamless exchange of data between in-body or on-body sensors and external monitoring equipment. These antennas are key components in systems designed for continuous health monitoring, early diagnosis, and patient rehabilitation. Unlike conventional antennas, those used in medical applications must function efficiently in close contact with or inside the human body, often under challenging conditions such as body movement, varying tissue properties, and limited space. As a result, the design and development of these antennas require careful consideration of factors like flexibility, biocompatibility, low power operation, and electromagnetic safety. This study reviews recent publications from 2017 onwards on wearable and implantable antennas. The material type, operating frequency band, and operational environment are considered for the design of the wearable and implantable antenna. To minimize loss, the research employed a high-thickness substrate, gold, and graphene material for the radiating patch in most of the design. This review presents a detailed overview of recent advancements in wearable and implantable antennas tailored for healthcare applications, highlights current design challenges, and outlines future research opportunities in this rapidly evolving field.
Volume: 16
Issue: 2
Page: 827-841
Publish at: 2026-04-01

Energy management in smart grids using internet of things and price-based demand response with a hybrid EVO-PDACNN approach

10.11591/ijece.v16i2.pp699-716
Manju Jayakumar Raghvin , Manjula R. Bharamagoudra , Ritesh Dash
Network control systems for energy distribution play an essential role when renewable energy sources (RES) expand and the smart grid (SG) infrastructure increases. A new approach to energy management (EM) in SGs combines energy valley optimizer (EVO) with pyramidal dilation attention convolutional neural network (PDACNN) to achieve its objectives. Through EVO-PDACNN, the system performs accurate energy consumption forecasting with PDACNN, while the EVO algorithm supports systematic scheduling capabilities. Due to its use, this method reduces the peak-to-average ratio (PAR) by 22% also the cost of electricity (COE) by 12%. This method performs better than the wind-driven bacterial forging algorithm (WBFA), genetic algorithm (GA), particle swarm optimization (PSO), modified elephant herd optimization algorithm (MEHOA), and ant colony optimization (ACO) because it has a new prediction ability and quick response. EVO-PDACNN establishes better performance through lower root mean square error (RMSE), together with mean squared error (MSE) and mean absolute error (MAE), which indicates enhanced cost efficiency and resource management capabilities for SGs. The method strengthens both energy forecasting and operational scheduling operations while effectively dealing with changes in supply and demand, which helps build resilient power systems.
Volume: 16
Issue: 2
Page: 699-716
Publish at: 2026-04-01

The ethics of AI technology in academic work: assessing the line between assistance and plagiarism

10.11591/ijece.v16i2.pp924-944
Md. Owafeeuzzaman Patwary , Md. Reazul Islam , Abtahi Islam , Nur-e Sarjina Khan , Md. Abdullah Al–Jubair , Md. Jakir Hossen , M. F. Mridha
The integration of artificial intelligence (AI) into academia has transformed educational practices and enhanced personalized learning and problem-solving capabilities. However, this raises significant ethical concerns regarding the balance between legitimate assistance and plagiarism. This study investigated public perceptions of AI in academic settings, focusing on its impact on effectiveness, dependency, and ethical considerations of AI use. A survey of 498 respondents from various educational roles was conducted, and the data were analyzed using SPSS for descriptive statistics, chi-square tests, and regression analyses. The results identified a significant correlation between people’s educational roles and their interaction with AI tools (χ2(6) = 16.488, p = 0.036), reflecting the diverse patterns of interaction within the academic community. More frequent use of AI was linked to less dependency (β = −0.298, p < 0.001), contradicting the widespread belief of over-reliance on AI. Age and educational role had limited explanatory value in perception of AI dependency issues (R2 = 0.033). The findings indicate a strong correlation between AI usage frequency and dependency levels, with increased exposure to AI fostering a more critical approach rather than a dependent one. Concerns regarding the unethical use of AI, inaccuracies in AI-generated content, and the need for clear institutional policies were also highlighted. This study underscores the importance of responsible AI integration, advocating for ethical frameworks and educational interventions to ensure that AI enhances learning without compromising academic integrity.
Volume: 16
Issue: 2
Page: 924-944
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

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

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

Transforming e-government projects by developing a RAF using Scrum integrated with CASE tool in Botswana

10.12928/telkomnika.v24i2.27431
Thapelo; North-West University Monageng , Bukohwo Michael; North-West University Esiefarienrhe
The digital transformation in Botswana has placed strong emphasis on e-government initiatives aimed at improving public service delivery. However, these projects continue to face low success rates due to challenges such as inadequate and reactive risk management practices, limited technical expertise, and fragmented implementation. This study proposes an integrated risk assessment framework (RAF) that combines Scrum methodology with computer-aided software engineering (CASE) tools that allows for the development of an automated, proactive, and iterative approach to risk management that is specific to the socioeconomic circumstance of Botswana. A quantitative survey was conducted with 32 project management specialists involved in e-government projects to assess their familiarity with agile methods and CASE tools, perceptions of traditional risk management approaches, and acceptance of the proposed model. The results revealed that 90.6% of respondents were familiar with Scrum, 78.1% had used CASE tools, and 81.25% supported the new framework, highlighting the urgent need for real-time risk tracking and continuous stakeholder engagement. The proposed e-government risk assessment framework (e-GRAF) model offers a flexible and adaptive solution to strengthen risk management processes, increase the success rate of e-government projects, and improve the quality and resilience of digital governance systems in Botswana.
Volume: 24
Issue: 2
Page: 466-480
Publish at: 2026-04-01

Fraud detection in financial transactions: state of the art

10.11591/ijeecs.v42.i1.pp272-282
Hamza Badri , Youssef Balouki , Fatima Guerouate
The surge in digital financial transactions, fueled by the proliferation of online banking, ecommerce, and emerging technologies, has brought significant oppor- tunities and equally critical vulnerabilities. Fraudulent activities have evolved in parallel, leveraging the complexity and global reach of digital systems to exploit weaknesses. This paper investigates the multifaceted nature of fraud in financial transactions, focusing on key types such as credit card fraud, money laundering, insurance fraud, and emerging threats in cryptocurrency systems. In this paper, we establish a state-of-the art overview of fraud detection method- ologies, analyzing their strengths and limitations. Traditional rule-based ap- proaches are contrasted with modern machine learning (ML) models, hybrid frame- works, and the application of advanced technologies. The study highlights the critical role of systems capable of identifying complex fraud patterns while ad- dressing persistent challenges. By synthesizing findings from existing research and evaluating innovative methods, this paper provides actionable insights into enhancing the effectiveness and resilience of fraud detection systems.
Volume: 42
Issue: 1
Page: 272-282
Publish at: 2026-04-01

Development of BAPOLAIC: AI chatbot for optical character recognition based-document extraction and voice assistant

10.11591/ijece.v16i2.pp1002-1009
Rival Fahreji , Ryan Satria Wijaya
Conventional chatbots often lack integrated functionalities for complex academic tasks, such as multi-format document handling and multimodal interaction. This paper presents the design, implementation, and performance evaluation of BAPOLAIC, a web-based, multimodal AI assistant developed to address this gap. The system architecture integrates optical character recognition (OCR), a dual-strategy natural language processing (NLP) module, and voice assistance, all orchestrated by the Gemini API. Quantitative evaluation confirmed high performance: the OCR module achieved a 98.69% average accuracy, and the retrieval-based NLP path correctly handled 90% of test queries. Furthermore, the API integration demonstrated exceptional efficiency with a median latency as low as 0.06 ms. Task-based evaluations validated BAPOLAIC's effectiveness in performing intelligent functions like summarization and content-based Q&A, with a superior capacity for handling up to 10 consecutive documents. The results validate BAPOLAIC as a successful proof-of-concept for a specialized academic tool, providing a framework for integrating multiple AI technologies to enhance educational productivity.
Volume: 16
Issue: 2
Page: 1002-1009
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

Taxonomy of cooperative adaptation level for cooperative adaptive mobile applications

10.12928/telkomnika.v24i2.27542
Berhanyikun Amanuel; Addis Ababa University Gebreselassie , Nuno M.; University of Lisbon Garcia , Dida; Addis Ababa University Midekso
Adaptive mobile applications (AMAs) are software systems designed to dynamically adjust their behavior in response to contextual changes. When multiple AMAs coexist on the same device, they create an ecosystem of heterogeneous applications with distinct functionalities, interaction models, and sensor requirements. This diversity enables opportunities for cooperative adaptation, where applications synchronize their behavior for collective benefit. Building on prior work that identified cooperation as a key dimension of adaptive mobile systems, this study proposes a refined taxonomy of cooperation levels for AMAs. The taxonomy is validated through case studies and formal specification methods, demonstrating its theoretical soundness and practical applicability. The findings advance the understanding of cooperative adaptation mechanisms and provide structured guidance for designing and classifying cooperative AMAs.
Volume: 24
Issue: 2
Page: 500-513
Publish at: 2026-04-01

Hybrid classical–quantum ensemble learning for real-time flight delay prediction at Tribhuvan International Airport

10.12928/telkomnika.v24i2.27240
Pavan; Civil Aviation Authority of Nepal Khanal , Nanda Bikram; Tribhuvan University Adhikari
This study investigates ensemble learning using classical and quantum-inspired models to predict flight delays at Tribhuvan International Airport (TIA), Nepal. It combines traditional machine learning algorithms with quantum-based approaches, quantum boosting (QBoost) and the hybrid QBoostPlus, leveraging quantum properties for faster computation. The dataset includes flight records from 2020 to 2024 and Meteorological Aerodrome Reports (METAR), analyzed across four sea- sons to capture delay patterns in domestic and international flights. A combined seasonal dataset assesses model generalization. Six models; VotingClassifier, adaptive boosting (AdaBoost), xtreme gradient boosting (XGBoost), categorical boosting (CatBoost), QBoost, and QBoostPlus are evaluated based on accuracy, precision, recall, F1 score, area under the curve(AUC), and execution time. CatBoost achieved high accuracy (up to 0.97) but slower execution (up to 10,570.63 ms). QBoostPlus provides competitive AUC scores (0.83–0.95) with faster execution, improving speed by up to 99.94% and generating predictions in as little as 6.46 ms. Although quantum-inspired models have slightly lower accuracy, their computational efficiency and stability show strong potential for real-time flight delay prediction. This is the first study applying quantum-inspired ensemble learning to Nepalese aviation data, showing promise for regional airports with limited infrastructure.
Volume: 24
Issue: 2
Page: 527-535
Publish at: 2026-04-01
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