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31,291 Article Results

Analysis of software-defined network on Proxmox with quality of-service testing using Open vSwitch

10.12928/telkomnika.v24i5.27549
Shafira; STMIK AMIK Bandung Febriani , Riezkan; STMIK AMIK Bandung Aprianda Firmansyah
This study designs and implements a virtual network based on software defined networking (SDN) in the Proxmox Virtual Environment (VE) using Open vSwitch (OVS) and OpenDaylight (ODL) controller, with performance testing of quality of service (QoS). The parameters tested include rate limiter, throughput, packet loss, and forwarding rate. Proxmox VE is selected for its centralized virtualization and clustering capabilities, while OVS manages network traffic among virtual machines (VMs). ODL is chosen for its compatibility with OpenFlow and OVS database (OVSDB) protocols, essential for OVS management. Testing uses iPerf3 to generate network traffic and analyze performance, including database service access simulation to mimic typical cloud application traffic loads. Forwarding rate measures the system’s packet forwarding capability per unit time, providing a comprehensive network performance overview. Results contribute to optimized SDN-based virtual network implementations in Proxmox environments and provide scientific and practical references for developing QoS-based virtual networking systems.
Volume: 24
Issue: 5
Page: 1504-1512
Publish at: 2026-10-01

CLARA: a unified hybrid ai-human receptionist framework using WebRTC and Monorepo architecture

10.11591/ijece.v16i5.pp2769-2781
Dhanush Sridhar Babu , Nagashree Nagesh , Amble Nagendra Aashuthosh , Adithya Nimmala Chandra , Muthusamy Naveen Kumar
The rapid digitalization of enterprise infrastructure has caused a major change in institutional communication. This change requires the traditional front desk to transform into a high-tech center for security and logistics. However, current automated solutions often create what is known as the “automation paradox.” In this situation, removing human oversight increases the importance and complexity of the remaining interactions. This work outlines the design, implementation, and evaluation of conversational low-latency AI receptionist agent (CLARA). CLARA is a new hybrid system which solves the problems of unattended kiosks by combining a conversational AI, which uses the Google Gemini API, with a low-latency, real-time WebRTC video calling system. The design features a strong 3-Tier Monorepo built with React, TypeScript, Express.js, Socket.IO, and PostgreSQL. This setup ensures type safety and consistency in the code across the entire stack. Key contributions of this project include a custom Socket.IO signaling server for secure peer-to-peer connections, a “Human-in-the-Loop” workflow that reduces handoff issues, and an “Offline-First” strategy ensuring the system keeps working during network disruptions. Performance tests show that CLARA achieves less than 200 ms glass-to-glass latency and handles high traffic well, proving it is an effective and reliable solution for modern visitor management.
Volume: 16
Issue: 5
Page: 2769-2781
Publish at: 2026-10-01

Temporal deep representation learning based remaining useful lifecyle prediction of electric vehicle lithium-ion batteries

10.11591/ijece.v16i5.pp2365-2378
Pradish Vaidya S. , Kevin Denzil V. , Monish S. , S. Cloudin , S. Saradha
In recent years, rapid growth of renewable energy systems and electric vehicle technologies has intensified the focus on lithium-ion batteries as critical energy storage components. Consequently, evaluating their performance and lifespan has become a significant concern in both scientific research and industrial applications. The accurate remaining useful life (RUL) prediction of lithium-ion batteries are major importance to increase energy management efficacy and increase the battery lifetime. Recently, the quick advancement of machine learning (ML) and artificial intelligence (AI) knowledge, data-driven approaches for forecasting the lithium-ion battery duration are involved extensive attention. Numerous researchers are employing deep learning (DL) approaches to make several techniques for predicting RUL, namely recurrent neural network (RNN) and convolutional neural network (CNN). This paper introduces a temporal deep representation learning based remaining useful Lifecyle prediction (TRDL-RULP) approach for electric vehicle lithium-ion batteries. The main goal of the TRDL-RULP framework is to support intelligent battery management methods and contribute to increasing the operational safety and lifecycle management of electric vehicle energy reserves systems. Initially, raw battery degradation data are processed using data normalization to eliminate scale variations and improve model stability. To enhance feature relevance and reduce dimensionality, a snake optimization-based feature selection strategy is employed to enable the extraction of the most informative degradation indicators. For RUL prediction, a long short-term memory autoencoder has been exploited to capture nonlinear temporal dependencies and extract robust latent representations. Finally, the tuna swarm optimization algorithm can be applied for optimal optimize parameters to improve convergence speed and predictive performance. The simulation study of the proposed TRDL-RULP algorithm is conducted using a benchmark lithium-ion battery degradation dataset obtained from the Kaggle repository. Extensive comparative results demonstrate the superior performance of the TRDL-RULP approach over recent methodologies.
Volume: 16
Issue: 5
Page: 2365-2378
Publish at: 2026-10-01

A systematic review and conceptual framework for cloud-based ontology-driven knowledge management systems in higher education institutions

10.11591/ijece.v16i5.pp2858-2870
Muhammad Younas , Ahmad Shukri Mohd Noor
Universities nowadays rely on digital platforms for organizing and sharing their institutional courses data. However, institutional information is scattered across different learning management systems, administrative databases, and research repositories, and there is no common system connecting them. Data were extracted from the relevant major academic database Springer Link, Scopus and IEEE for the year ranging from 2015 to 2025. Initially, 248 researches were observed out of which 30 studies review by expert were selected after filtering. The studies were analyzed for the cloud technologies in system design, the ontologies for the structured representation of knowledge and the knowledge graphs for the connected data systems. 22 out of 30 studies reported measurable improvements in knowledge accessibility. However, 78% of the reviewed systems lacked scalable reasoning mechanisms to handle large datasets, and fewer than 30% provided practical implementation guidance. Our research has three contributions: (i) literature synthesis of ontology-driven systems and cloud-based knowledge management in higher education; (ii) a system architecture that integrates cloud service models (IaaS, PaaS, SaaS), SPARQL-based knowledge graphs, and semantic technologies (OWL, RDF ontologies), grounded in a deployment scenario at Jazan University, Saudi Arabia; and (iii) a step-by-step implementation framework.
Volume: 16
Issue: 5
Page: 2858-2870
Publish at: 2026-10-01

Real-time depth measurement and stability control of AUV using regression approximation and filtered pressure data

10.11591/ijece.v16i5.pp2417-2430
Senanjung Prayoga , Dhaniel Beny Wardhana , Ryan Satria Wijaya
This paper presents the development and experimental validation of a prototype-scale autonomous underwater vehicle (AUV) depth control system using a proportional-integral-derivative (PID) controller with depth feedback from a SEN0257 water-pressure sensor. Raw sensor readings are filtered and calibrated using linear regression, reducing the depth estimation error, as indicated by a decrease in root mean square error (RMSE) from 1.88 to 0.63 cm. The calibrated depth signal is implemented in real time as the feedback source for closed-loop control on the testbed. Controller performance is evaluated by comparing two tuning strategies: Ziegler–Nichols (ZN) closed-loop tuning and manual fine-tuning. Experiments were conducted at depth setpoints of 70 and 100 cm under consistent pool conditions, and additional trials were performed while the AUV executes forward motion to assess robustness under dynamic disturbances. System responses are quantified using rise time, overshoot, settling time, and steady-state error. Results show that calibration significantly improves sensor suitability for feedback, while the fine-tuned PID controller produces a more stable depth response with lower overshoot, smaller steady-state error, and shorter settling time than the ZN controller, despite the faster initial rise achieved by ZN tuning. Overall, combining calibrated pressure-based depth estimation with fine-tuned PID gains enables stable and accurate depth regulation for prototype AUV operation.
Volume: 16
Issue: 5
Page: 2417-2430
Publish at: 2026-10-01

A chip level design of a multi-mode compressive sensing image sensor

10.11591/ijece.v16i5.pp2393-2404
Zahra Sepehri , Sayed Masoud Sayedi , Ehsan Yazdian
This paper presents the full chip-level design of a multi-mode CMOS vision sensor, emphasizing the detailed implementation of its circuit architecture. The proposed chip incorporates our previously developed photodiode sensing array together with the on-chip design of control circuitry. By embedding these building blocks, the chip enables pixel-level compressive sensing and supports dual operation modes, allowing the transmission of image data in both compressed and non-compressed formats. In either mode, the sensor is capable of capturing both scene images and difference images between consecutive video frames, operating at a frame rate of 40 fps. A 64*64 vision chip is implemented using TSMC 0.18um standard CMOS technology. In the normal scene image mode, with compression (N-C) and  without compression (N-nC), the structure consumes 36.99uW and 37.09uW, respectively. Meanwhile, in the difference scene image mode, with compression (D-C) and  without compression (D-nC), it consumes 38.67uW and 38.75uW, respectively.
Volume: 16
Issue: 5
Page: 2393-2404
Publish at: 2026-10-01

A comparative analysis of hybrid FFNN-LSTM and FFNN-RNN architectures for short term electricity load forecasting

10.11591/ijece.v16i5.pp2347-2356
Temitope Akinyede , Josephine Adenike Akinyede , Paul Kehinde Olulope , Emmanuel Taiwo Fasina , Temitope Adewale Olominu
Short-term accurate forecasting of electricity demand is crucial for power-system operation and energy scheduling and the equilibrium between electricity generation and consumption. The predicting of electric power consumption is however difficult because electricity-demand profiles are non-linear and time varying. In this paper, we consider two hybrid deep learning architectures feedforward neural network long short-term memory (FFNN-LSTM) and feedforward neural network recurrent neural network (FFNN-RNN) for multi-horizon electricity-load forecasting. The architectures we proposed combine the ability of FFNN to represent data non-linearly with the sequential modelling capability of LSTM and RNN. The authors assess the models based on historical electricity-load observations from Ado-Ekiti, at three different forecasting horizons of 24 hours, 72 hours, and 168 hours. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) assessed predictive performance. The findings reveal that FFNN-LSTM achieves a consistently lower RMSE across the evaluated horizons, resulting in greater effectiveness to confine relatively large forecasting errors. On the other hand, the separate LSTM and RNN models achieve lower MAE and MAPE in various scenarios, suggesting stronger short-term reaction to electricity demand. The results, therefore, show a trade-off between forecast stability and sensitivity to rapid load changes. In general, the performance of hybrid architecture is more stable compared to the standalone recurrent models which are more responsive to short-term changes when we look at most of the forecasting horizons. According to the research paper, the forecasting architectures can now be selected for smart grid applications.
Volume: 16
Issue: 5
Page: 2347-2356
Publish at: 2026-10-01

Performance and quality analysis of brain MRI image transmission over free-space optical communication systems under severe atmospheric conditions

10.11591/ijece.v16i5.pp2526-2536
Entidhar Mhawes Zghair , Seham Hashem , Ali Hammadi
Reliable transfer of brain magnetic resonance imaging (MRI) data over atmospheric free-space optical (FSO) links is a key enabler of telemedicine, yet conventional FSO studies judge link quality by communication metrics such as the bit error rate (BER) alone, which cannot guarantee the structural and contrast fidelity that diagnosis demands. This study proposes a quality-aware FSO transmission framework for brain MRI in which link performance is assessed jointly through BER, peak signal-to-noise ratio (PSNR), and the structural similarity index (SSIM). A physical-layer FSO channel is modelled in OptiSystem 20 and co-simulated with MATLAB R2023b, which performs image serialization, reconstruction, and quality analysis. Thirty axial T2-weighted slices (256×256, 8-bit) from the public IXI dataset are transmitted at 1550 nm over clear-air, rain, and fog channels at 500, 1000, and 2000 m. Adopting conservative diagnostic thresholds of PSNR ≥ 30 dB and SSIM ≥ 0.85, the link is diagnostically usable in clear air at all tested distances (PSNR = 42.1 dB, SSIM = 0.98 at 500 m) and in rain up to 2000 m (PSNR ≥ 31.2 dB), whereas fog degrades quality below the thresholds at every distance, reaching PSNR = 22.7 dB and SSIM = 0.68 at 2000 m. A concatenated forward-error-correction (FEC) scheme is then shown to restore diagnostic quality under fog up to 1000 m, extending the usable fog range, while 2000 m remains infeasible and motivates hybrid FSO/RF operation. The framework provides quantitative deployment limits for FSO-based medical image transport.
Volume: 16
Issue: 5
Page: 2526-2536
Publish at: 2026-10-01

BDLock: A blockchain-enabled two tier privacy-aware federated idam service platform using RBAC

10.11591/ijece.v16i5.pp2537-2548
Muhammad Shakil Pervez , Md. Nasim Adnan , Sarker Tanveer Ahmed Rumee , Moinul Islam Zaber
Centralized identity and access management (IDAM) systems suffer from sin-gle points of failure, lack of authorization transparency, and susceptibility to in-sider threats and privilege abuse. While role-based access control (RBAC) of-fers structured permission management, its enforcement through centralized pol-icy engines introduces auditability gaps unacceptable in modern distributed service delivery environments. This paper presents BDLock, a blockchain-enabled two-tier privacy-aware federated IDAM platform integrating OAuth 2.0, OpenID Connect (OIDC), and Hyperledger Fabric 2.4. The first tier validates JSON Web Tokens (JWT) issued by Keycloak against a Spring Boot resource server, the second tier enforces immutable scope-based RBAC rights on the Hyperledger Fabric ledger, ensuring every access decision is tamper-proof and auditable. Unlike prior approaches, BDLock uniquely bridges OAuth-authenticated off-chain identities to cryptographic on-chain Fabric wallet identities, satisfying all six STRIDE-modelled threats categories across both Web2 and Web3 identity models. Validated with up to 1,800 concurrent users, BDLock achieves a peak throughput of approximately 200 transactions per second using round-robin load balancing. At high concurrency, it outperforms single-peer fallback by up to 25%. Furthermore, it maintains uninterrupted access control during peer failures, eliminating the single point of failure found in all nine compared state-of-the-art systems.
Volume: 16
Issue: 5
Page: 2537-2548
Publish at: 2026-10-01

Design science research in developing a religious chatbot based on Bulugh al-Maram

10.11591/ijece.v16i5.pp2782-2794
Aris Tjahyanto , Irmasari Hafidz , Faizal Johan Atletiko
Chatbots have recently gained significant popularity. For instance, ChatGPT has become a preferred tool for many individuals seeking instant answers without relying on human responses. This immediacy sets chatbots apart from books, which require users to search for information manually. This time-consuming process does not align with millennials' preference for convenience and efficiency. Studying hadith independently using the Bulugh al-Maram book demands considerable time and effort. The limited use of natural language processing technologies in religious chatbots restricts their ability to handle complex inquiries effectively. A chatbot capable of answering hadith-related questions could greatly assist the public in studying hadith texts by providing direct responses without extensive searching. This chatbot was designed for web browsers, utilizing deep learning as its core technology. This research led to the development of a chatbot prototype for learning hadith from Bulugh al-Maram. Built using the design science research (DSR) methodology, the prototype achieves an intent recognition rate (IRR) of 86.82%. However, its capabilities are below the BERT model, demonstrating a strong ability to accurately interpret user questions and statements.
Volume: 16
Issue: 5
Page: 2782-2794
Publish at: 2026-10-01

Calibration-guided score fusion for robust multimodal traffic anomaly detection

10.11591/ijece.v16i5.pp2516-2525
Quang Hiep Do , Thien Tan Nguyen
Multimodal traffic anomaly detection is affected by differences in visual and audio score ranges, temporal fluctuations, and unstable decision thresholds. This paper proposes a calibration-guided score fusion (CGSF) framework that processes video frames and audio spectrograms through separate reconstruction-based models. The resulting anomaly scores are temporally smoothed, normalized using validation data, and combined at the score level. A percentile estimated from normal validation samples is then used as the decision threshold. The framework was evaluated on the MAVD and DADA2000 datasets. On MAVD, CGSF achiev,,,,,,ed a ROC-AUC of 0.553, a PR-AUC of 0.082, and an F1-score of 0.129. It outperformed direct fusion in precision, recall, and F1-score, although the gain in ROC-AUC was small. Analysis on DADA2000 showed smoother temporal score behaviour after calibration and smoothing. The results indicate that CGSF mainly improves score comparability and threshold consistency rather than producing a large increase in detection accuracy. Its modular design also allows the visual and audio branches to be trained and updated independently.
Volume: 16
Issue: 5
Page: 2516-2525
Publish at: 2026-10-01

High-performance 28 GHz antenna array design for millimeter wave 5G systems

10.12928/telkomnika.v24i5.27857
Moussab; University Abdelmalek Essaâdi Chbeine , Abderrahman; University Abdelmalek Essaâdi Chbeine , Mohamed; University Abdelmalek Essaâdi Bayjja , Lahcen; Hassan II University of Casablanca Sellak
This paper presents the design and performance evaluation of compact 28 GHz microstrip antenna arrays for millimeter-wave (mmWave) fifth generation (5G) wireless communication applications. The proposed antennas are implemented on a Rogers RT/duroid 5880 substrate with a relative permittivity of 2.2 and a thickness of 0.508 mm and were evaluated using full-wave electromagnetic simulations in Computer Simulation Technology (CST) Microwave Studio. Starting from a single antenna element, several scalable array configurations, including 1×2, 1×4, 1×8, and 2×8 structures, were designed to improve antenna gain and radiation directivity while maintaining good impedance matching characteristics. The novelty of this work lies in the development of compact scalable antenna arrays using a simple feeding network configuration to achieve progressive gain enhancement with reduced structural complexity. The simulation results show that the antenna performance improves as the number of array elements increases, achieving a maximum gain of 16.2 dB at 28 GHz with satisfactory return loss, voltage standing wave ratio (VSWR), and radiation behavior. In addition, the proposed arrays provide an appropriate trade-off between gain, bandwidth, and compact size, making them suitable candidates for future high-data-rate mmWave 5G communication systems.
Volume: 24
Issue: 5
Page: 1492-1503
Publish at: 2026-10-01

Exploration of thesis topic trends of students majoring in informatics and computer engineering with LDA method

10.12928/telkomnika.v24i5.27674
Ruslan; University of Makassar Ruslan , Rezki Nurul; University of Makassar Jariah S. Intam , Sasmita; University of Makassar Sasmita , Dewi; University of Makassar Fatmarani Surianto , Andi Akram; University of Makassar Nur Risal , Nur Azizah; University of Makassar Eka Budiarti
The selection of a thesis topic is very important because it determines the focus of the research and its contribution to knowledge. However, many students find it difficult to choose a topic that suits their interests and expertise. This study models the thesis topics of students in the Department of Informatics and Computer Engineering (JTIK) using the latent Dirichlet allocation (LDA) method, with term frequency–inverse document frequency (TF–IDF) as the model input. The data set includes 969 thesis titles from 2009 to 2024. The optimized LDA model identifies 17 main topics by adjusting parameters such as the number of topics, alpha, and beta. The best coherence value (0.7431) is achieved with alpha = 0.81, beta = 0.01, and 17 topics. The dominant themes included information system development, computer networks, and multimedia, reflecting the main research areas of JTIK. In addition, a web-based system was developed and integrated with the best model to help students identify relevant topics and find thesis references. This study demonstrates the effectiveness of topic modeling in higher education and provides insights into academic research trends.
Volume: 24
Issue: 5
Page: 1550-1560
Publish at: 2026-10-01

Real-time multimodal fatigue detection using facial vision and alert integration via ESP32 for occupational health applications

10.11591/ijece.v16i5.pp2750-2768
Andrés Enrique Rojas Primo , Alfredo Lazaro Gutierrez , Felix Pucuhuayla-Revatta
Early detection of work fatigue is a major challenge in industrial settings due to the lack of non-invasive, accessible, and low-cost systems capable of operating in real time. In this context, this research proposes a multimodal real-time fatigue detection system using facial vision and artificial intelligence, aimed at risk prevention and promoting occupational health. The system integrates geometric and behavioral parameters, such as eye aspect ratio (EAR), head tilt, and mouth opening, processed on a Raspberry Pi 5 using MediaPipe and a hybrid convolutional neural network (CNN) MobileViT model. Visual and audible alerts are managed by an ESP32 microcontroller using the message queuing telemetry transport (MQTT) protocol, while a graphical interface developed in Tkinter allows real-time monitoring of operator status. Experimental results, evaluated in a simulated work environment using AI-generated synthetic videos, show an accuracy greater than 97% and a latency of less than 250 ms, confirming the system's effectiveness in the early detection of signs of drowsiness and attention deficit. In conclusion, the proposal represents a non-invasive, scalable, and efficient solution that combines computer vision, deep learning, and the Internet of Things (IoT) to strengthen workplace safety and well-being.
Volume: 16
Issue: 5
Page: 2750-2768
Publish at: 2026-10-01

Robust resource allocation in multi-cell UE-specific RIS-assisted D2D relay networks under imperfect CSI

10.11591/ijece.v16i5.pp2575-2594
Kayode Popoola , Ayodeji Ajani , Stuart Nicholson , Muheeb Ahmed , Srilatha Narayangari Pamuri , Ibrahim Bala Alhassan
Device-to-device (D2D) communication enhances spectral efficiency but remains constrained by limited transmission range, underlay interference, and the half-duplex overhead of conventional relays. User equipment-specific reconfigurable intelligent surfaces (UE-RIS) offer a promising alternative by enabling passive beamforming to strengthen D2D links without additional spectrum consumption. However, existing studies typically assume perfect channel state information (CSI) and single-cell operation, limiting their applicability to practical deployments. This paper proposes a robust multi-cell resource allocation (RMRA) framework for UE-RIS-assisted D2D relay networks under imperfect CSI. A hybrid uncertainty model is adopted, combining statistical Gauss-Markov CSI errors for intra-cell links with bounded norm-ball uncertainty for inter-cell links. The joint optimisation of resource reuse, transmit power allocation, and RIS phase configuration is formulated as a stochastic mixed-integer nonlinear program that maximises network spectral efficiency while satisfying outage and quality-of-service constraints. To efficiently solve the problem, a three-stage algorithm is proposed comprising distance-pruned Hungarian assignment, robust power control using Bernstein-type inequality and S-procedure based semidefinite programming, and soft actor-critic (SAC) based passive beamforming. Simulation results show that RMRA achieves a 94% D2D access rate at light load and over 75% at full load, improves sum spectral efficiency by 34.7% and 70.2% over AF relaying and direct D2D, respectively, attains 118.5 bits/s/Hz/W energy efficiency, and maintains 30.2 bits/s/Hz under severe CSI uncertainty.
Volume: 16
Issue: 5
Page: 2575-2594
Publish at: 2026-10-01
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