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30,185 Article Results

Deep intelligence for sustainable farming: a swarm-empowered data analytics architecture

10.11591/ijai.v15.i1.pp901-908
Kiran Muniswamy Panduranga , Roopashree Hejjaji Ranganathasharma
The inclusion of complex patterns of data in precision agriculture (PA) induces a greater degree of challenges from the perspective of carrying out conventional analytical operations. Although proliferated use of artificial intelligence (AI) has been noticed to yield some promising results to address such issues, yet they too have many shortcomings. Hence, the current manuscript introduces an innovative hybrid AI scheme towards enhancing the analytical operations necessary for decision-making in smart farming. The proposed scheme hybridizes a deep neural network (DNN) with a novel swarm intelligence (SI) model for optimizing the performance of its adopted deep learning (DL) model. Tested on a standard dataset of agriculture, the proposed model exhibited a 10% increase in accuracy and 40% faster response time when compared with conventional machine learning (ML) models, DL models, and SI models. The study contributes to a novel benchmark towards time-efficient, scalable, and intelligent analytics on PA.
Volume: 15
Issue: 1
Page: 901-908
Publish at: 2026-02-01

Optimizing planar micro-transformer performance

10.12928/telkomnika.v24i1.27276
Tahar; University of Science and Technology of Oran USTO-MB Alili , Fatima Zohra; University of Science and Technology of Oran USTO-MB Medjaoui , Azzedine; Nour El Bachir University Center Hamid , Abderahim; National Polytechnic School of Oran Maurice Audin Mokhefi , Yacine; Nour El Bachir University Center Guettaf , Hocine; Nour El Bachir University Center Guentri
Faced with new requirements for isolated switching power supplies with high efficiency and power density, planar transformer technology has emerged as a serious alternative to wound components. The work presented in this paper addresses the issue of developing planar transformers in the context of low-power electronics, where volume and weight constraints are paramount. The flat shape of the coils and the interlacing of the windings do not allow for control of magneto-thermal phenomena. Although scientific literature offers numerous simulation tools to aid in the design of such transformers, it must be noted that they do not allow for a rigorous account of these phenomena. In this paper, methods and a geometric and electrical sizing tool in planar technology are used for the design of flyback direct current to direct current (DC/DC) converters. Methods for dimensioning and estimating temperature rise are presented and compared in order to develop calculation tools for design purposes. This study enabled us to observe the distribution of the magnetic field, the role of ferrite, the distribution of currents and voltages in the coils, and the distribution of temperature in our device. It should be noted that conductive and convective heat transfer processes were considered in steady state.
Volume: 24
Issue: 1
Page: 313-328
Publish at: 2026-02-01

Technology levels in artificial intelligence robotics and industrial automation: impacts and implications

10.12928/telkomnika.v24i1.27253
Ratna; Universitas Esa Unggul Yulika Go , Agnes; National Research and Innovation Agency (BRIN) Sondita Payani , Siti; Universitas Hasanuddin Rabiatul Adawiyah , Ogi; National Research and Innovation Agency (BRIN) Gumelar
Robotics technology has progressed rapidly since its debut in 1922, evolving from simple programmable automation to highly sophisticated systems. This study employs a hybrid methodology, combining qualitative analysis of key robotic components manipulators, controllers, end effectors, and geometric configurations with quantitative comparison of performance metrics to classify robots according to their technological level (low-tech versus high tech). The findings show clear distinctions across these levels. Low-tech robots typically achieve positioning accuracy of about 0.025 mm and rely mainly on single electric motor actuation, making them suitable for simple, repetitive tasks. In contrast, high-tech robots can perform complex operations with positioning accuracy of up to 3 mm, integrating multiple actuation systems such as electric, pneumatic, and hydraulic mechanisms for enhanced flexibility and control. Moreover, high-tech robots exhibit greater manipulative capabilities and advanced control systems that enable multi axis and adaptive operations not feasible for low-tech counterparts. These results demonstrate how the technological level directly shapes a robot’s precision, actuation complexity, and functional range, providing a clear framework for selecting appropriate robotic solutions in both industrial and research settings.
Volume: 24
Issue: 1
Page: 175-183
Publish at: 2026-02-01

Implementation of markerless augmented reality and cyber physical-social systems for smart tourism application

10.12928/telkomnika.v24i1.27414
Ilham; Institut Teknologi Sumatera Firman Ashari , Fanesa; Institut Teknologi Sumatera Hadi Permana , Muhammad; Universitas Muhammadiyah Malang Zainal Arifin , Purwono; Institut Teknologi Sumatera Prasetyawan
Lampung province holds substantial tourism potential that remains underutilized due to fragmented information and limited promotional strategies. This study introduces a smart tourism application integrating markerless augmented reality (AR) with cyber-physical-social systems (CPSS), representing the first implementation of its kind for location-based tourism in the region. The novelty lies in the hierarchical coordinate transformation architecture (HCTA), a multi-layer computational framework employing the Haversine formula to achieve high-precision mapping of geographic coordinates into AR-optimized perceptual views. The system was evaluated for geolocation accuracy, resource utilization, backend scalability, AR rendering robustness, and user experience. Results show strong performance: geolocation tests across seven destinations yielded a mean error rate of 1.5%; AR operations remained efficient with 8–10% central processing unit (CPU) and 140–160 MB random access memory (RAM) usage; and rendering was stable across 360° device orientation. Backend tests confirmed scalability, sustaining 56 requests per second with zero failures under 100 concurrent users. A user study with 20 participants using the user experience questionnaire-short (UEQ-S) revealed highly positive outcomes, with overall scores 2.275, all within the Excellent benchmark. These findings confirm that the application is not only technically robust and efficient but also engaging and enjoyable, offering a scalable framework for immersive smart tourism ecosystems.
Volume: 24
Issue: 1
Page: 71-94
Publish at: 2026-02-01

SELLA: An IoT-based smart shopping trolley with real-time RFID tracking and automated checkout

10.12928/telkomnika.v24i1.27464
Hadj; Djillali Liabes University of Sidi Bel Abbes Zerrouki , Salima; Djillali Liabes University of Sidi Bel Abbes Azzaz-Rahmani
The contemporary retail sector faces a persistent challenge in enhancing in store customer experience, primarily due to inefficiencies at checkout. This paper presents smart e-cart for lean logistics application (SELLA), a smart shopping trolley system engineered to eliminate this bottleneck. The system’s architecture is centered on a Raspberry Pi 4 microcontroller, orchestrating an ultra-high frequency (UHF) radio frequency identification (RFID) subsystem for instantaneous, non-line-of-sight product identification, and a responsive 7-inch touchscreen graphical user interface (GUI) developed in PyQt. The core contribution lies in developing a self contained shopping solution with integrated payment processing, supported by comprehensive performance validation. We present a detailed methodology, including the system’s multi-threaded software architecture and core operational algorithm. Experimental evaluation demonstrates a mean tag detection accuracy of 98.2% under optimal conditions, a robust user interface (UI) latency of under 500 ms, and an average central processing unit (CPU) utilization of 28%, proving system efficiency. Comparative analysis confirms that SELLA’s integration of on-trolley automated payment and detailed performance metrics represents a significant advancement over existing prototypes. The system provides a validated, high-performance solution for next-generation smart retail environments.
Volume: 24
Issue: 1
Page: 184-195
Publish at: 2026-02-01

A practical approach to Candi Siwa 3D reconstruction with COLMAP and Nerfstudio

10.12928/telkomnika.v24i1.27212
Helena; Prasetiya Mulya University Widiarti , Rokhmat; Prasetiya Mulya University Febrianto , Agung; Prasetiya Mulya University Alfiansyah
We demonstrate a practical approach for large-scale object three-dimensional (3D) reconstruction with freely available frameworks, COLMAP and Nerfstudio. We performed the reconstruction of a temple named Candi Siwa, located at Prambanan Site, which is situated between Central Java and Yogyakarta Province, Indonesia. We utilized COLMAP and Nerfstudio as platforms for 3D reconstruction from images captured by an everyday smartphone. In the 3D model construction process, COLMAP generates a dense point cloud, whereas Nerfstudio generates a scene from source images. We selected 96 images of Candi Siwa to perform reconstruction using COLMAP. As a result, a 3D model for the temple with a clear structure and color was observed. A scene rendered in MP4 format was also generated using Nerfstudio. Additionally, we performed the 3D reconstruction from 150 images taken by the public and found them insufficient for constructing the object. This occurred despite the number of images being larger than those used in the previous reconstruction. The results indicate that the success of a crowdsourcing project for reconstructing a large-scale object should consider not only the number of images but also the variation in point of view and the completeness of the whole structure.
Volume: 24
Issue: 1
Page: 249-257
Publish at: 2026-02-01

Advanced microwave imaging and artificial neural networks for early detection and localization of breast tumors

10.12928/telkomnika.v24i1.27126
Abdelfettah; University of Mustapha Stambouli Miraoui , Lotfi; School of Applied Sciences Tlemcen Merad , Djalal; LARATIC Laboratory at National Institute of Telecommunications and Information Technology and Communication (ENSTTIC) Ziani-Kerarti
This study investigates the detection and localization of breast tumors based on dielectric property differences between cancerous and normal tissues. A microwave imaging technique integrated with artificial neural networks (ANNs) is proposed as a noninvasive alternative to conventional screening methods such as mammography and magnetic resonance imaging (MRI). A breast model with a 2.5 mm spherical tumor was designed using CST Microwave Studio. Simulation results show that the ANN achieves a detection rate close to 100%, providing negative outputs for tumor-free cases and positive outputs for cases with tumors. Additionally, ANN outputs strongly correlate with the actual tumor positions in the simulated environment. These findings suggest that microwave imaging combined with ANNs offers a cost effective, radiation-free, and patient-friendly solution for the early detection and localization of breast cancer, with promising potential for clinical translation.
Volume: 24
Issue: 1
Page: 240-248
Publish at: 2026-02-01

Optimized IMC with GWO algorithm and variable switching function for voltage regulation of SEPIC converter

10.12928/telkomnika.v24i1.27330
Reza; Shahrood University of Technology Fazeli , Mohammad; Shahrood University of Technology Haddad Zarif , Mahmoud; Islamic Azad University Zadehbagheri , Tole; Universitas Ahmad Dahlan Embedded System and Power Electronics Research Group Sutikno
With the growing application of single-ended primary-inductor converter (SEPIC) converters in power electronic systems, precise output voltage regulation under uncertainties and nonlinear conditions remains a significant challenge. Although internal model control (IMC) effectively addresses issues arising from unstable zeros and fixed time delays in non-minimum phase systems, its performance can degrade under large transient errors or sudden disturbances, leading to control signal saturation and instability. In this study, a modified IMC scheme is proposed, which integrates a variable switching function into the control structure. This addition enhances the robustness of the system by dynamically adapting the control effort to mitigate abrupt changes in the control signal and stabilize the output voltage. Furthermore, it prevents controller saturation during large-signal deviations, thereby improving transient response and maintaining system stability. The design parameters of the controller are optimized using the gray wolf algorithm to achieve an optimal balance between voltage overshoot, settling time, and closed-loop stability. Simulation results under various operating conditions confirm the superior performance of the proposed control method compared to conventional IMC.
Volume: 24
Issue: 1
Page: 258-270
Publish at: 2026-02-01

Enhancing reflective elements of intelligent reflective surfaces in 6G communications using artificial intelligence

10.12928/telkomnika.v24i1.27307
Jehan Kadhim Shareef; University of Thi-Qar Al-Safi , Abbas Thajeel Rhaif; University of Thi-Qar Alsahlanee
The dynamic landscape of 6G communication networks necessitates innovative strategies to address energy inefficiency and signal degradation in densely populated regions with limited line-of-sight (LOS) coverage. A novel technology known as an intelligent reflecting surface (IRS) has emerged; it can dynamically modify the characteristics of electromagnetic waves to enhance signal propagation. Unfortunately, current IRS models frequently neglect the balance between energy efficiency (EE) and the quantity of reflective elements (N) in Rayleigh fading scenarios. This study introduces an algorithm called dynamic-static particle swarm optimization (DS-PSO) aimed at improving EE and decreasing the quantity of reflective components in the performance optimization of IRS. The research assesses the proposed model in comparison to single-input single-output (SISO) systems, conventional IRS models, and IRS models from prior studies within a realistic urban framework. The optimized IRS, which only uses seven reflective elements, has a peak EE of 366 Mbit/Joule. This is a big improvement over IRS models from earlier research, as shown by the numbers. The findings indicate that artificial intelligence (AI)-driven optimization can enhance IRS technology for sustainable and efficient 6G networks.
Volume: 24
Issue: 1
Page: 22-33
Publish at: 2026-02-01

A decoupling-based multivariable H∞ controller for PMSM speed and current regulation

10.12928/telkomnika.v24i1.27515
Farid; Higher School of Applied Sciences of Tlemcen Oudjama , Mohammed; University of Tlemcen Messirdi , Mokhtar; University Centre of Maghnia Bourdim , Abelmadjid; University of Tlemcen Boumédiène
High precision speed regulation of the permanent magnet synchronous motor (PMSM) is a critical challenge in modern industrial applications, including electric vehicles and traction systems. This task is significantly affected by external disturbances, such as variable load torque, as well as physical phenomena often neglected in analytical models, such as magnetic circuit saturation or thermal variations in electrical parameters. In this context, conventional control methods often fail to ensure both dynamic performance and robustness. This paper proposes a multivariable H∞ control strategy based on field-oriented control (FOC) and d/q decoupling to design a robust and high-performance controller. The diagonal multiple-input multiple-output (MIMO) model, linking the direct-axis voltage𝑣𝑑to the current 𝑖𝑑and the quadrature-axis voltage 𝑣𝑞to the rotational speed 𝜔𝑟, is derived directly from the decoupling principles of FOC, without relying on linearization around an operating point or modeling of parametric uncertainties. The H∞ controller is synthesized using the standard configuration, with carefully selected weighting functions to ensure dynamic performance, closed-loop stability, and effective disturbance rejection. Numerical simulations demonstrate that the proposed controller achieves accurate speed reference tracking, fine current regulation, and fast load disturbance rejection, confirming its effectiveness and robustness. This approach provides an advanced alternative to conventional control methods by fully exploiting the multivariable structure of the system.
Volume: 24
Issue: 1
Page: 293-301
Publish at: 2026-02-01

Improved disturbance rejection of induction motor drives using PI–VGSTASM control and torque disturbance estimation

10.12928/telkomnika.v24i1.27459
Ngoc; Industrial University of Ho Chi Minh City Thuy Pham , Duc; Industrial University of Ho Chi Minh City Thuan Le , Thanh; Industrial University of Ho Chi Minh City Tinh Pham
Induction motor (IM) drives often suffer performance degradation under load variations and parameter uncertainties when using conventional proportional–integral (PI)- based field-oriented control (FOC). To address these issues, this study proposes a composite control framework combining a PI regulator in the speed loop with a Lyapunov-based variable-gain super twisting algorithm (VGSTA) for the inner current loops to enhance robustness against disturbances and parameter variations. In addition, a load torque observer is developed to estimate unknown disturbances in real time and generate an equivalent compensation current, thereby improving disturbance rejection. Unlike existing approaches, the proposed strategy achieves a balance between simplicity, robustness, and smooth control by integrating classical PI control with higher-order sliding mode techniques and adaptive observer dynamics. Furthermore, the controller and observer gains are optimized using particle swarm optimization (PSO) to improve convergence and reduce overshoot under uncertain conditions. Simulation results demonstrate accurate speed regulation, effective chattering reduction, and reliable operation under load and parameter variations. Due to its low computational complexity and high robustness, the proposed method is well suited for industrial drive systems and electric mobility applications.
Volume: 24
Issue: 1
Page: 329-342
Publish at: 2026-02-01

Comparison methods in a decision support system for determining JavaScript frameworks

10.12928/telkomnika.v24i1.27241
Rofif Aghna; Sunan Kalijaga State Islamic University Yogyakarta Fakhri Diya , Agus; Sunan Kalijaga State Islamic University Yogyakarta Mulyanto
The selection of an appropriate JavaScript framework in web-based software development often leads to errors when the chosen framework is incompatible with the design. The ability to make decisions quickly, accurately, and precisely is therefore a key factor in successful software design. Addressing this need, the present study analyzes the accuracy of the analytical hierarchy process-weight product (AHP-WP), analytical hierarchy process-technique for order preference by similarity to ideal solution (AHP TOPSIS), and analytical hierarchy process-simple multi-attribute rating technique (AHP-SMART) methods in determining the most suitable JavaScript framework according to the International Organization for Standardization (ISO) 9126 classification. To evaluate accuracy, the mean absolute percentage error (MAPE) was applied as a cost function to measure the error percentage of each method. The analysis was conducted on ten popular JavaScript frameworks selected based on their popularity and usage trends. The evaluation considered six quality criteria: functionality, reliability, usability, efficiency, maintainability, and portability. The results show the ranking of each alternative for all methods. Accuracy measurement using MAPE revealed that the AHP-WP method produced the smallest error percentage (37.77645%), compared to AHP-TOPSIS (47.12566%) and AHP-SMART (46.4041%). Accordingly, the AHP-WP method is recommended for decision support system (DSS) development.
Volume: 24
Issue: 1
Page: 95-110
Publish at: 2026-02-01

Distorted born iterative method reconstruction in high-noise environments using KNN-based machine learning denoising

10.12928/telkomnika.v24i1.27401
Nguyen Quang; Vietnam Academy of Science and Technology Huy , Nguyen Truong; Vietnam Academy of Science and Technology Thang
Ultrasound tomography reconstruction using the distorted born iterative method (DBIM) is sensitive to measurement noise, which degrades image fidelity and slows convergence. We propose integrating a k-nearest neighbors (KNN) denoising step within each DBIM iteration to suppress noise adaptively while preserving structural edges. Simulations with a circular cylindrical target and transmit/receive geometry (12×12) were conducted at signal-to-noise ratio (SNR) levels of 6 dB, 3 dB, and 1 dB. Compared with conventional DBIM employing Tikhonov regularization, the KNN-filtered DBIM reduces normalized reconstruction error by up to 57.2% at 1 dB and shows faster error decay over successive iterations. The method is training-free, computationally lightweight, and preserves fine structural details. These properties make KNN-filtered DBIM attractive for noisy or resource-constrained imaging environments. Future work will validate the approach on experimental data and explore adaptive K selection.
Volume: 24
Issue: 1
Page: 206-218
Publish at: 2026-02-01

Explainable hybrid models for cardiovascular disease detection and mortality prediction

10.11591/ijai.v15.i1.pp191-212
Ali Al-Ataby , Hussain Attia
The impact of cardiovascular diseases (CVDs) is devastating, with 20.5 million deaths annually. Early detection and prediction tools exist, but current approaches struggle to balance predictive performance with clinical interpretability. In this work, a two-stage machine learning (ML) framework is proposed for heart disease detection and mortality prediction in heart failure patients. Logistic regression (LR), random forest (RF), and gradient boosting (GB) models were trained using the publicly available heart failure datasets, and their performance was compared, then a stacked ensemble approach was employed to enhance prediction accuracy. Model interpretability was achieved through Shapley additive explanations (SHAP), which provide global feature rankings and specific patient attributes, supporting explainable artificial intelligence (XAI) in clinical practice. The GB model achieved the highest performance in the first stage with a receiver operating characteristic area under the curve (ROC AUC) of 96% and an accuracy of 89% on internal testing, while external validation confirmed strong generalization (ROC AUC of 94%). In the second stage, stacked ensemble model was employed and achieved marginal improvements. Two interactive web applications were developed to enable real-time predictions with SHAP visualizations. The results demonstrate that combining high-performance ML models with interpretable outputs can significantly improve trust in real-world healthcare environments.
Volume: 15
Issue: 1
Page: 191-212
Publish at: 2026-02-01

Comparison between ensemble and linear methods for website phishing detection

10.11591/ijai.v15.i1.pp681-694
Saba Hussein Rashid , Saba Alaa Abdulwahhab , Farah Amer Abdulaziz
In the current digitalized world, the notion of cybersecurity has become crucial in everyday life, and the issue of privacy takes the leading role in the technological agenda of the global community. One such social engineering attack that is currently prevalent is phishing, which is a common technique used by cybercriminals to intercept sensitive data. Despite the presence of certain limitations which can restrict its usefulness, machine learning (ML) has evolved into an interesting approach to identify phishing attacks. Cloud ML is an effective solution that uses cloud computing solutions to create, train, and deploy models that provide a faster and more accurate result as well as support large datasets. This paper compares the ensemble method of Amazon SageMaker’s AutoML tool, AutoGluon, with the linear method of SageMaker’s linear learner algorithm for website phishing detection. Key factors examined include training techniques, training time, batch transform time, endpoint prediction time, and model accuracy. The results demonstrate that while AutoGluon outperforms linear learner in terms of accuracy and prediction speed, linear learner is faster in training and batch transform processes.
Volume: 15
Issue: 1
Page: 681-694
Publish at: 2026-02-01
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