Articles

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

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

30,895 Article Results

Indirect adaptive neural network control for constant power conversion in wave energy system

10.11591/ijece.v16i4.pp2120-2133
Jesus de la Cruz-Alejo , Hugo Beatriz Cuellar , J. Antonio Lobato Cadena , Edwin Christian Becerra-Alvarez
The conversion of ocean wave energy into electrical energy occurs near beaches and is important for the design and implementation of wave energy conversion (WEC) systems. However, its generation depends on environmental conditions, which complicates the design and control of the devices. This work presents an approach to indirect adaptive control based on artificial neural networks to detect wave conditions for the proper functioning of WEC structures. The method involves generating a constant output voltage using a voltage boost converter and a direct current-alternating current (DC-AC) converter. Maintaining a constant output power despite variations in wave conditions to generate a voltage of 24 V with a current of 2 A is the primary proposal for the control design. The mechanical design integrates a rack and pinion system and a pulley transmission that connects a floating device to an electric generator. The implementation of control is carried out on an Arduino platform. The control system was implemented on an Arduino platform, occupying 48% of the available memory, with a convergence time of 4.29 ms, a mean squared error (MSE) of 0.13715, and a root mean squared error (RMSE) of 0.37034. These low values indicate that the proposed control system has greater accuracy. The experimental results validate the proposed control system, which reduces energy conversion errors and achieves greater efficiency.
Volume: 16
Issue: 4
Page: 2120-2133
Publish at: 2026-08-01

Beyond adoption: measuring the success of mandatory information systems through an integrated ECM and ISSM

10.11591/ijece.v16i4.pp2031-2041
Muhammad Rosyid Ridlo , Muhammad Fachri Shandika Iman , Reny Yuliati
The successful implementation of mandatory organizational information systems depends not only on system adoption but also on user satisfaction. However, most post-adoption evaluation studies have focused on voluntary systems, leaving mandatory public sector deployments substantially underexplored. This study evaluates the determinants of employee satisfaction with the Coretax Administration System, a nationwide integrated tax platform implemented by the Directorate General of Taxes in Indonesia. To provide a comprehensive explanation of post-adoption evaluation, this research integrates the Expectation Confirmation Model (ECM) and the Information System Success Model (ISSM), examining how system quality, information quality, and service quality influence confirmation and perceived usefulness, which in turn determine user satisfaction. Using a quantitative approach, data were collected from 292 employees actively using the system and analyzed through Partial Least Square Structural Equation Modeling (PLS-SEM). The results demonstrate that the integrated model exhibits strong predictive power, explaining 76.4% of the variance in user satisfaction. System quality emerged as the most influential determinant, significantly affecting confirmation and perceived usefulness, which subsequently drives satisfaction. Meanwhile, information quality and service quality showed selective effects, indicating that technical reliability plays a more critical role than supportive features in a mandatory environment. The findings offer actionable guidance for policymakers and IS architects engaged in large-scale compulsory digital transformation initiatives in the public sector.
Volume: 16
Issue: 4
Page: 2031-2041
Publish at: 2026-08-01

Real-time anomaly detection system using best performed machine learning model

10.12928/telkomnika.v24i4.27561
Victor; North-West University Mathebula , Bukohwo; North-West University Michael Esiefarienrhe
Effective anomaly detection is critical for protecting organizational networks against increasingly sophisticated cyber threats. However, most machine learning-based intrusion detection models are developed and validated using public benchmark datasets, which may not reflect the operational characteristics, traffic behavior, and threat patterns of real institutional networks. In the case of Umalusi, there is currently no anomaly detection model customized and validated using Umalusi-specific network traffic, creating a practical gap in deployable cybersecurity capability. This study proposes a hybrid machine learning framework tailored to support accurate, efficient, and operationally relevant anomaly detection. Using knowledge discovery in databases (KDD) process, network traffic data were collected and pre-processed through normalization, label encoding, missing value treatment, and dimensionality reduction using principal component analysis (PCA). The 16 hybrid models integrating unsupervised anomaly detection with supervised classification were implemented and comparatively evaluated. Experimental findings indicate that the density-based spatial clustering of applications databasescan (DBSCAN) + random forest (RF) model achieved 99.92% accuracy while maintaining a low false positive (FP) cost, making it suitable for a security operations centre (SOC). In addition, a Flask-based web application was developed to enable real-time deployment by sniffing live network traffic, executing inference, and persisting results in an SQLite database.
Volume: 24
Issue: 4
Page: 1204-1215
Publish at: 2026-08-01

Intelligent routing-based attack detection in Internet of Things networks using artificial intelligence

10.11591/ijece.v16i4.pp2169-2181
Huda Saloom Sultan , Asseel Jabbar Almahdi , Murteza Hanoon Tuama , Athar Hussein Mohammed
The fast-growing Internet of Things (IoT) networks have posed considerable security risks because of decentralized network designs, dynamic topologies, and inadequate computation capabilities. Current intrusion detection strategies are primarily traffic-based, but without paying attention to routing-layer dynamics, which are paramount in multi-hop IoT systems. To overcome this drawback, this paper suggests a smart routing-conscious attack detection model which combines routing-layer monitoring with methods of artificial intelligence to improve the security of IoT networks. The suggested framework constantly compares routing metrics, such as packet loss, change in hop count, end to end delay and energy consumption to detect malicious routing behavior in real time. Two types of artificial neural networks, feedforward neural network (FFNN) and convolutional neural network (CNN) are used to categorize routing activities as normal or malicious. The experimentation on simulation was carried out by using NS-2 in a dynamic multi-hop IoT environment where routing-based DoS attacks were implemented. The experimental results reveal that CNN model had a higher detection accuracy of 85.76% with lower execution time of 17 s compared to the FFNN model which had an accuracy of 82.76% and an execution time of 18 s. Moreover, the suggested framework enhanced reliability of routing by minimizing the packet loss and communication delay and having low routing overhead. These results support the hypothesis that routing-aware intelligence can be used to enhance AI-based intrusion detection to create an adaptive, routing-aware, and resource-efficient security solution to decentralized networks of IoT devices.
Volume: 16
Issue: 4
Page: 2169-2181
Publish at: 2026-08-01

Predictive safety helmet for miners using internet of things and artificial intelligence

10.12928/telkomnika.v24i4.27421
Vijayalakshmi; Thiagarajar College of Engineering Murugesan , Irudhaya Ronisha Innasi; Thiagarajar College of Engineering John Benedict , Janani; Thiagarajar College of Engineering Vigneswaran , Pooja; Thiagarajar College of Engineering Senthamarai Kannan
Mining is still responsible for many deaths since mines have dangerous environmental conditions including mine collapses, gas emissions, and high temperatures. However, traditional helmets do not provide adequate protection; besides, they cannot analyze miners’ health as well as the environmental hazards. In order to solve this issue, this work presents a predictive safety helmet equipped with several sensors and means of communication. Specifically, the helmet comprises a micro-electro mechanical systems (MEMS) accelerometer for vibration monitoring, a gas sensor for detecting the presence of harmful gases, a heartbeat sensor for assessing workers’ well-being, and a temperature sensor for monitoring the environmental parameters. Additionally, the device is provided with a global positioning system (GPS) module for location determination and a global system for mobile (GSM) module for transmitting alert notifications in case of emergency situations. The collected data is analyzed on an internet of things (IoT)-based system; any signs of danger cause alerts to be sent immediately.
Volume: 24
Issue: 4
Page: 1177-1186
Publish at: 2026-08-01

Methods of finding the maximum common transitive subgraph: experimental comparison

10.12928/telkomnika.v24i4.27683
Oleg; Volgograd State Technical University Sychev , Anton; Volgograd State Technical University Chupinin
The problem of finding a maximum common subgraph (MCS) in a graph has broad applications in practical domains. However, certain scenarios require subgraphs with special properties, such as transitivity, that must be kept during building the subgraph. We formally define the concept of a transitive subgraph, investigate its properties. We study four different algorithms for finding the max imum common transitive subgraph (MCTS), compiled a list of tests aim at com paring graphs after making various changes and evaluated their accuracy and efficiency on a set of test cases. Benchmarking on 64 tests ranks the algorithms by scalability and accuracy: branch matching is the most scalable (> 1000 ver tices) and accurate (F1: 0.9907). MCS tree search is viable for graphs of up to ∼ 250 vertices (F1: 0.9752). Backtracking is limited to < 30 vertices (ac curacy: 0.5625), and brute-force is only feasible for graphs with ≤ 10 vertices, despite its high accuracy (0.9375). We discuss the advantages and disadvantages of each method, the test cases where each method demonstrates a non-optimal MCTS,identify the classes on which the methods work correctly and found that the branch matching method based on the longest common subsequence (LCS) algorithm performed the best.
Volume: 24
Issue: 4
Page: 1187-1196
Publish at: 2026-08-01

From data to intelligence: foundations of learning systems, representation, and computational perception

10.11591/ijece.v16i4.pp1669-1676
Tole Sutikno
The rapid evolution of intelligent systems has shifted the focus of electrical and computer engineering from isolated data processing toward integrated models of machine cognition. This editorial introduces a foundational perspective on machine intelligence systems, emphasizing the transformation from raw data to meaningful intelligence through learning systems, representation mechanisms, and computational perception. In contemporary AI-driven environments, intelligence is no longer defined solely by algorithmic performance, but by the ability to construct structured representations of the world and interpret complex multimodal signals. Learning systems, particularly those grounded in machine learning and deep learning paradigms, serve as the core mechanism enabling this transformation. Representation learning provides the bridge between unstructured data and abstract knowledge, while computational perception enables machines to interpret visual, auditory, and sensor-based information in real time. Together, these components form the foundational architecture of intelligent systems that underpin emerging applications in engineering, automation, and cyber-physical environments. This editorial sets the stage for understanding intelligence as an emergent computational construct, highlighting its role as the first phase in the broader cognitive intelligence systems continuum that progresses toward adaptive, autonomous, and socio-cognitive systems in future research directions.
Volume: 16
Issue: 4
Page: 1669-1676
Publish at: 2026-08-01

ACLiMA: an IoT-based autonomous flood monitoring and mitigation system with database-driven threshold control

10.11591/ijece.v16i4.pp1867-1875
Hendi Santoso , Rizqan Khairan Munandar , Apriansyah Apriansyah , Andi Ihwan , Putri Yuli Utami
Urban flooding remains a critical challenge in densely populated and low-lying areas, where delayed response and limited monitoring infrastructure significantly increase flood risks. Existing flood monitoring systems are typically limited to passive observation or fixed-threshold alerting without integrated autonomous mitigation and flexible configuration. This study proposes autonomous control logic for IoT-based monitoring and actuation (ACLiMA), an IoT-based autonomous flood monitoring and mitigation system using a database-driven threshold control approach to enable real-time monitoring and immediate response. The system integrates ultrasonic water-level sensing, centralized database management, web-based visualization, and autonomous pump actuation within a unified architecture. Flood conditions are classified into four operational states—SAFE, CAUTION, DANGEROUS, and FLOOD—based on configurable threshold values stored in the database, allowing dynamic adjustment without firmware modification. Experimental results demonstrate stable system integration with deterministic control behaviour and low response latency between sensing and actuation, enabling timely pump activation during critical conditions. The system also provides multi-temporal visualization for monitoring and analysis, while the database-driven configuration enhances flexibility, scalability, and ease of deployment across different environments. Overall, the proposed system offers a low-cost, modular, and autonomous solution for real-time flood mitigation, contributing to the transition from passive monitoring toward active mitigation in smart city and resource-constrained urban applications.
Volume: 16
Issue: 4
Page: 1867-1875
Publish at: 2026-08-01

Aquaponic greenhouse agriculture integrated with multi-modal sensors and LED-grow-light IoT-based

10.11591/ijece.v16i4.pp2254-2264
Pujianti Wahyuningsih , Muhammad Risal , Nining Haerani , Abdul Jalil
This study aims to develop a smart greenhouse aquaponic farming system that integrates aquaculture and hydroponic cultivation based on the Internet of Things (IoT). The proposed integration method employs multi-modal sensors and LED-grow-lights as supporting technologies to enable remote monitoring and control of aquaponic farming conditions through the Blynk IoT platform. The multi-modal sensors utilized in this research include DHT11 for monitoring air temperature and humidity, light dependent resistor (LDR) and infrared (IR) sensors for measuring sunlight intensity and LED-grow-lights levels, a soil moisture sensor for measuring hydroponic water volume, DS18B20 for monitoring aquaponic water temperature, a total dissolved solids (TDS) sensor for nutrient concentration, and pH-4502C for measuring water acidity. The LED-grow-lights functions as an artificial light source to replace sunlight under unfavorable weather conditions. In this study, a Raspberry Pi was implemented as the central data processing unit, while the Blynk IoT platform was employed to transmit aquaponic greenhouse data to the farmer’s smartphone. The experimental results demonstrate that the integration of multi-modal sensors enables effective monitoring of IoT-based aquaponic farming conditions with an accuracy level of up to 94% compared with other product of sensors, a monitoring and control delay ranging transmits the data from the embedded devices to smartphone farmer between 5 and 9 seconds, and reliable replacement of sunlight by the LED-grow-lights during adverse weather conditions.
Volume: 16
Issue: 4
Page: 2254-2264
Publish at: 2026-08-01

Design and manufacture of a self-balancing system for two-wheeled vehicle models using a reaction wheel

10.11591/ijece.v16i4.pp1853-1866
Indrawanto Indrawanto , Yuzar Arigi , Vani Virdyawan
Motorbikes are a popular mode of transportation in Indonesia and are agile in maneuvering on roads with heavy traffic. The increasing use of motorbikes has triggered many accidents. This paper discusses the design, manufacture, and control of a self-balancing system for a two-wheeled vehicle model to improve driving safety. The self-balancing system designed uses a reaction wheel. The system architecture consists of a microcontroller board, a DC motor, a gyroscope, a reaction wheel, and a two-wheel vehicle model. The dimensions of the reaction wheel are optimized between the mass and the moment of inertia to make it possible to self-balance the model from a certain initial angle. The controller is designed based on the state space model with a feedback linear-quadratic regulator controller. The matrix weighting values are selected using Bryson’s rules method. Experimental results show that the self-balancing system can work well for the two-wheel vehicle model.
Volume: 16
Issue: 4
Page: 1853-1866
Publish at: 2026-08-01

Integrating principal component analysis in spatial-spectral fusion models for hyperspectral image segmentation

10.11591/ijece.v16i4.pp2074-2086
Alexander Calvin , Laksmita Rahadianti
Hyperspectral imaging (HSI) from unmanned aerial vehicles (UAVs) provides rich spatial-spectral data, but its high dimensionality presents significant computational challenges for semantic segmentation. While state-of-the-art models like the transformer-based HSI-TransUnet are often employed, they introduce massive computational overhead. This study adapts a lightweight, dual-tunnel deep convolutional neural network (DCNN) framework for land-use segmentation on hyperspectral images by integrating PCA-based spatial reduction in the spatial branch, and benchmarks it on the UAV-HSI-Crop dataset against HSI- TransUnet. For further analysis, an ablation study compares principal component analysis (PCA) and local similarity projection (LSP) as spatial feature ex- tractors. The results demonstrate a significant performance and efficiency advantage. Our proposed PCA-based model (271.1K parameters) obtained a Kappa (κ) of 0.8582, overall accuracy (OA) of 0.8800, and average accuracy (AA) of 0.4918, outperforming the LSP-based model by 0.65% in κ, 0.51% in OA, and 2.16% in AA and the HSI-TransUnet baseline by 2.35% in κ, 1.95% in OA, and 8.10% in AA. On our experimental setup, this result was achieved with a 152.7-fold reduction in model size, a 14.2-fold decrease in training time, and a 4.6-fold speedup in inference relative to the reported HSI-TransUnet baseline. These findings show that the PCA-based dual-tunnel DCNN provides a favor- able trade-off between class-balanced accuracy and computational efficiency for this HSI segmentation task.
Volume: 16
Issue: 4
Page: 2074-2086
Publish at: 2026-08-01

Bone strength analyzer and monitoring device for lower limb external fixation

10.11591/ijece.v16i4.pp1778-1791
Devin Babu , Waheb A. Jabbar , Muhammad Hisyam Rosle , Noorazliza Sulaiman , Mohd Amir Shahlan Mohd Aspar , Abdul Nasir
The procedure for external fixator removal in lower limb fractures is typically based on radiographic data, subjects’ patients to ionising radiation, and provides minimal real-time information about the healing process. This project suggests a sensor-based Internet-of-Things apparatus, which will measure bone strength during the recuperating process based on load cells, HX711 amplifiers, and a Wemos ESP8266 microcontroller. The system provides real-time feedback in the form of LED indicators and a buzzer, whereas remote monitoring is supported by the Blynk dashboard. Measurement accuracy of over 90% was carried out as per the experimental validation conducted under the simulation of various loads (3-9 kg) and with clear stage indicators of critical, partial, and full recovery. The device offers continuous monitoring and objective bone healing evaluation with no radiation in comparison to conventional imaging. The limitation of the study is the limited range of loads in prototype testing, which could affect accuracy in particular situations. However, the results represent the possible clinical relevance of introducing real-time biomechanical surveillance into the process of fracture treatment, hence contributing to safer rehabilitation and more reasonable decisions related to the fixator removal.
Volume: 16
Issue: 4
Page: 1778-1791
Publish at: 2026-08-01

Miniaturized patch antenna for the S-band communication subsystem of the 3U University CubeSat

10.11591/ijece.v16i4.pp1913-1926
Nabil El Hassainate , Loubna Berrich , Nabil Benjelloun , Ahmed Oulad Said , Zouhair Guennoun
This paper introduces a miniaturized patch antenna for the reception module of the 3U University CubeSat in the S-band communications subsystem. In order to reduce the physical characteristics of the antenna (dimensions, mass) and achieve circular polarization (CP), as well as increasing its performances, two techniques are used: the first consists of introducing semicircle truncation on both sides of the square patch, and the second consists of modifying the ground plane with networks of symmetrical slots along the main axes (x,y). The fabricated antenna prototype has overall dimensions of 55×55×3.27 mm and a total mass of 20.59 g. The developed antenna spans the uplink band (2.025 to 2.110 GHz) for payload and telemetry operations. The designed antenna achieves a reflection coefficient below minus 10 dB across the target frequency band, along with a minus 3 dB axial ratio bandwidth that is well appropriate to space communication links. The comparisons of the prototype results to the simulation results using CST and HFSS provide close agreement of around 90%.
Volume: 16
Issue: 4
Page: 1913-1926
Publish at: 2026-08-01

A hybrid retrieval augmented generation framework for automated educational document understanding and intelligent response generation

10.11591/ijece.v16i4.pp1964-1975
Basavesh D. , Jayashree Nagaraj
New students often struggle when short articles clash with thick textbooks. Still, even though large language models offer some teaching support, standard online setups lack focused accuracy - sometimes making things up - and risk user data control. Here comes an idea: build a tightly tested, self- contained system that aligns learning materials automatically without needing the internet, keeping information private by design. One look at two setups shows how they handle local reasoning differently. Instead of using both encoder and decoder parts, one system skips the encoder entirely. That simpler design grabs full context through ChromaDB without shrinking the data first. Meanwhile, the older type crunches input down, losing meaning along the way. Even though it runs fast - just under a second - errors pop up often, four out of five responses drifting off course. On the flip side, the new method builds correct code nearly every time, adds clear explanations tied to lesson goals, yet takes more than fourteen seconds to reply. Slower? Yes. More accurate? Clearly. What stands out is how compressed models running locally can still catch up in understanding classroom content. Another key point emerges: building tutors powered by artificial intelligence (AI) becomes safer when data never leaves the device and outside services are not needed at all.
Volume: 16
Issue: 4
Page: 1964-1975
Publish at: 2026-08-01

Automated prediction of the mode of birth delivery using geometric features of uterine contraction segments

10.11591/ijece.v16i4.pp1885-1898
Rubana Hoque Chowdhury , Roma Sultana , Quazi Delwar Hossain , Mohiuddin Ahmad
Pregnancy is a unique, complex process and it's hard to predict the mode of birth delivery due to the lack resources. This study aims to develop a clinical decision support system to predict birth delivery mode according to three categories: spontaneous vaginal delivery, induced vaginal delivery, and cesarean section. This methodology entails the automated extraction of uterine contraction segments from the electrohysterogram (EHG) signal based on the zero-crossing rate. Each segment is processed through a discrete Fourier transform to obtain the Fourier coefficients. Geometric features, including area, perimeter, circularity, variance, and bending energy were extracted from the boundary shape of these complex coefficients using the convex hull method. We found the women who experience spontaneous deliveries exhibit higher feature values and a lower circularity value compared to those who undergo cesarean sections or induced births. Based on these extracted parameters, the random forest (RF) model yielded promising results: reaching an accuracy above 90% in the classification between caesarean and spontaneous deliveries and spontaneous and induced vaginal deliveries and somewhat lower, around 70% between induced and cesarean. To conclude, the utilization of all proposed EHG parameters through machine learning can enhance obstetricians' ability to predict the mode of birth delivery.
Volume: 16
Issue: 4
Page: 1885-1898
Publish at: 2026-08-01
1 2 3
...
2059 2060
Show 9 of 2060

Discover Our Library

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

Explore Now
Library 3D Ilustration