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28,593 Article Results

Household electric monitoring IoT system

10.11591/ijeecs.v40.i1.pp85-92
Joemar Corpuz , Kristine Joy S. Dela Cruz , Joan B. Palomar , Jackielyn Tamayo , Hohn Lois C. Bongao , Mark Joseph B. Enojas , Jane E. Morgado
In dense areas in the Philippines, there are recorded cases of power theft or known to be illegally tapping power lines from another household which results to complaints because of increased electricity bills. To address the power theft problems, this work uses internet of things system for household electric monitoring and control. A transmitter and receiver set up is designed to monitor the energy consumption at both ends. When there is discrepancy with the meter reading, an alert system sends notification that there is an illegal wiretapping. The load is monitored through electric meters and the powers measured are compared. These data are being sent wirelessly through a GSM module. The meter readings for both the transmitter and receiver can be viewed in a mobile phone through a web app developed. A minimum of 3W difference between the transmitter and the receiver will mean a discrepancy and notifies illegal wiretapping. Illegal connections are cutoff when an incident of tapping occurs. Based on the results of the test, the household electricity monitoring system through internet of things (IoT) is found to be 100% reliable in detecting and cutting off illegal connections. Additionally, the system is able to compute the monthly power consumption.
Volume: 40
Issue: 1
Page: 85-92
Publish at: 2025-10-01

Distributed formation control with obstacle and collision avoidance for humanoid robot

10.11591/ijeecs.v40.i1.pp108-117
Faisal Wahab , Bambang Riyanto Trilaksnono
Formation control has become a popular research topic in recent years. A common challenge in formation control is ensuring that robots can avoid obstacles and maintain a safe distance from one another to prevent collisions while forming a formation. In this research, a distributed formation control approach for a multi-robot system (MRS) with obstacle and collision avoidance is presented. The distributed formation control architecture is based on a consensus algorithm and consists of four layers: consensus tracking, consensus-based formation control, behavior, and physical robot layers. The system was implemented and evaluated through both simulations and experiments. Humanoid robots were used as the platform for these implementations. The result of the simulations and experiments show that the distributed formation control system successfully guided the robots into desired formation while also avoiding obstacles and preventing collisions with other robots.
Volume: 40
Issue: 1
Page: 108-117
Publish at: 2025-10-01

Alzheimer’s disease stage prediction using a novel transfer learning-Alzheimer’s network architecture

10.11591/ijeecs.v40.i1.pp518-529
Pothala Ramya , Chappa Ramesh , Odugu Srinivasa Rao
The root cause of Alzheimer’s disease (AD) is unknown except for a very tiny number of family instances caused by a genetic mutation. A thorough examination of particular brain disorders’ tissues is necessary to correctly identify the circumstances using scans of magnetic resonance imaging (MRI), and specific non-brain tissues, like the neck, skin, muscle, and fat, make further investigation challenging and can be seen in MRI scans. This work aims to use the FSL-BET skull stripping tool to remove non-brain tissues and extract the significant region of the brain- deep learning (DL) techniques rather than machine learning (ML) models helpful in classification and predictions. The most frequent issue with DL models is which needs a lot of training data, causes to problems with class imbalance. To avoid imbalance issues, we used data augmentation to ensure that the samples were distributed equally among the classes. A novel transfer learning Alzheimer’s disease network (TL-AzNet) based visual geometry group-19 (VGG19) technique was developed in this study. Conducted a comparison study using the base and suggested models, comparing over data with oversampling versus non-oversampling. The novel model predicted AD with a 95% accuracy rate.
Volume: 40
Issue: 1
Page: 518-529
Publish at: 2025-10-01

Implementation of a secure system for calculating and supervising the energy consumption of electrical equipment

10.11591/ijeecs.v40.i1.pp127-136
Jarmouni Ezzitouni , Ahmed Mouhsen , Mohamed Lamhamdi , Ennajih Elmehdi , En-Naoui Ilias , Bousbaa Mohamed
With the advent of smart grids and the growing challenges associated with the production and consumption of electrical energy, it is crucial to deploy reliable systems to monitor production and consumption, as well as to improve energy efficiency. To ensure optimal decision-making in energy management and control systems, it is essential to have both efficient measurement systems for data collection and acquisition and secure information exchange. These elements are fundamental to ensuring the smooth operation of energy systems and enabling precise supervision of energy flows, thus contributing to more efficient use of available electrical resources. This article focuses on the implementation of a complete electrical energy calculation and management system for energy consumers. To achieve this, devices such as integrated digital control units and current and voltage sensors are used. The system architecture guarantees precise measurement and calculation of electrical energy and other important parameters, such as power factor in the case of inductive and capacitive loads, which have an effect on reactive energy. The data collected is stored in a secure database.
Volume: 40
Issue: 1
Page: 127-136
Publish at: 2025-10-01

Optimizing distance vector-hop localization in wireless sensor networks using the grasshopper optimization algorithm

10.11591/ijeecs.v40.i1.pp461-479
Janani Selvaraj , Hymlin Rose Sasijohn Gloryrajabai , Sivarathinabala Mariappan , Backia Abinaya Antony Samy , Sudhakar Kalairishi
In scenarios involving mobile sensors within distributed sensor systems, such as those often encountered in wireless sensor networks (WSNs) or the internet of things (IoT), the ability to ascertain the origin of sensor data holds significant importance. Range-free Monte Carlo Localization methods offer an energy-efficient solution that eliminates the need for extra hardware, as they solely rely on the radio hardware already present on sensor nodes. But there are certain disadvantages when implemented, as it occupies more amount of power and some inaccuracies might happen in accessing the data from the sensor node. In this paper, we suggest the grasshopper optimization algorithm (GOA) strategy, which incorporates the distance-vector hop (DVHop) and three-anchor methods. It displays its usefulness in terms of both overall localization accuracy and resistance to hostile attacks or malfunctioning nodes. Nonetheless, the incorporation of dead reckoning based on motion sensor data significantly enhances the precision of location estimates and bolsters the network's robustness against both faulty components and malicious agents.
Volume: 40
Issue: 1
Page: 461-479
Publish at: 2025-10-01

Analysis and evaluation about the dimmable light affect positioning-based MISO visible light communication

10.11591/ijeecs.v40.i1.pp181-188
Trang Nguyen , Dat Vuong
Visible light communication (VLC) is a new on-trend communication technology which offers high-speed data rate, great deployment potential in indoor enviroment. In VLC scenario, the positioning based on visible light communication (VLCP) has become one of interesting application of researchers. Most of existing proposed VLCP algorithms focused on mathematical analysis of multi-dimensional perspective based on the received signal strength (RSS) to enhance the accuracy without the consideration of dimming. However, regarding to physical characteristics of VLC devices and requirement of illumination, the light is increasingly dimmable along the time which leads to decrease transmitted optical power of LED as well as RSS received at the photodetector (PD)). Inspired by the above-mentioned constraints, this paper proposed the mathematical model to analyses the effect of dimming capability on the state-of-art RSS based positioning algorithms. Evaluation of the proposed model based on the metrics of RSS and position error (PE) is conducted on Matlab.
Volume: 40
Issue: 1
Page: 181-188
Publish at: 2025-10-01

Substrate thickness variation on the frequency response of microstrip antenna for mm-wave application

10.12928/telkomnika.v23i5.26731
Bello Abdullahi; Universiti Sains Malaysia Muhammad , Mohd Fadzil; Universiti Sains Malaysia Ain , Mohd Nazri; Universiti Sains Malaysia Mahmud , Mohd Zamir; Universiti Sains Malaysia Pakhuruddin , Ahmadu; Universiti Sains Malaysia Girgiri , Mohamad Faiz Mohamed; Collaborative Microelectronic Design Excellence Center (CEDEC) Omar
Substrate height (Hs) is an important parameter that influences antenna propagation. This research designed a low-profile 28 GHz microstrip antenna on a polyimide substrate with varying Hs using CST Studio software. The simulated results and MINITAB software were used to develop regression model equations, which analyzed the impact of Hs variation on the antenna performance. The proposed models’ equations have indicated an increase in average responses of resonant frequency (Fr), percentage bandwidth (% BW), gain (G), return loss (RL), and efficiency (ƞ) as the Hs decreased. The antenna achieved a BW of 3.87 GHz at Hs 0.525 mm and 5.54 GHz at 0.025 mm, a G of 3.89 dBi at Hs 0.525 mm and 3.91 dBi at Hs 0.025 mm, and an ƞ of 94.19% at Hs 0.525 mm and 98.24% at Hs 0.025 mm. The antenna was fabricated and tested, and the experimental results were validated with the models’ equations. The thinner substrate resulted in an improvement in the antenna performance.
Volume: 23
Issue: 5
Page: 1188-1200
Publish at: 2025-10-01

Realization of Bernstein-Vazirani quantum algorithm in an interactive educational game

10.12928/telkomnika.v23i5.26929
David; Calvin Institute of Technology Gosal , Timothy Rudolf; Calvin Institute of Technology Tan , Yozef; Calvin Institute of Technology Tjandra , Hendrik Santoso; Calvin Institute of Technology Sugiarto
Quantum algorithms are celebrated for their computational superiority over classical counterparts, yet they pose significant learning challenges for non-physics audiences. Among these, the Bernstein-Vazirani (BV) algorithm stands out for its quantum speedup by efficiently identifying a secret binary string. However, the accessibility of such algorithms remains constrained by their inherent technical complexity. To address this educational gap, this paper introduces a gamified, web-based tool that innovatively reinterprets the BV algorithm’s complex mathematical settings through an into engaging scenario of identifying broken lamps. Players assume the role of an investigator, utilizing both classical and quantum solvers to identify faulty lamps with minimal queries. By transforming the BV algorithm into an intuitive gameplay experience, the tool helps reducing technical barriers, making quantum concepts much more comprehensible for educators and students than traditional methods that demand rigorous mathematical understanding. Developed using Qiskit, IBM’s Python package for quantum computation, and deployed via Flask, a popular Python microframework for building web applications, the game effectively simplifies complex quantum algorithms while demonstrating the practical applications of quantum speedup. This contribution advances quantum education by merging technical depth with interactive design, fostering a broader understanding of quantum principles and inspiring new innovations in gamified learning.
Volume: 23
Issue: 5
Page: 1247-1257
Publish at: 2025-10-01

Building change detection via classification in high-resolution aerial imagery

10.11591/ijai.v14.i5.pp4319-4331
Hayder Mosa Merza , Ihab Sbeity , Mohamed Dbouk , Zein Al-Abidin Ibrahim
This research investigates the detection of changes in building structures within high-resolution aerial images of Baghdad, Iraq, over two years, 2007 and 2024. Employing advanced remote sensing techniques and sophisticated image processing algorithms, this study aims to identify and quantify alterations in the urban landscape accurately by addressing the key challenges inherent in the image registration process, as well as the availability associated with change detection (CD) techniques. We examined the data collection strategies, evaluated matching methods, and compared CD approaches. Aerial images were accurately analyzed to detect changes in building footprints, construction activities, and destruction. We developed a comprehensive annotation methodology tailored to the complex urban environment of Baghdad. These findings emphasize the rapidly evolving nature of Baghdad’s urban fabric and the critical need for ongoing monitoring to inform urban planning and management strategies. The results demonstrate the efficacy of utilizing high-resolution aerial imagery with object-based CD techniques for detailed urban analysis. This research advances the existing knowledge by providing a robust framework for urban CD, with implications for enhancing urban planning and policy-making processes. Future research will focus on refining the annotation processes and incorporating additional data sources to enhance the accuracy and comprehensiveness of urban CD methodologies.
Volume: 14
Issue: 5
Page: 4319-4331
Publish at: 2025-10-01

Voltage stability investigation: enabling large-scale renewable energy integration in Tamil Nadu’s grid

10.11591/ijeecs.v40.i1.pp47-56
Chelladurai Chandarahasan , Edwin Sheeba Percis
The integration of renewable energy sources (RES) such as wind and solar, characterized by their inherent intermittency and variability, poses notable challenges to the stability and reliability of power systems. This research addresses these challenges by conducting a detailed load flow, voltage sensitivity and stability analysis at a significant extra high voltage (EHV) pooling station under high-RES penetration. Employing advanced simulation methodologies, the study evaluates the effects of RES integration on voltage profiles and the system’s capacity to sustain equilibrium under steady-state operations. The findings highlight that substantial RES integration induces considerable voltage fluctuations and reactive power disparities. To counter these effects, static var compensators (SVCs) were deployed, demonstrating their efficacy in enhancing voltage stability and providing essential reactive power support. The study confirms the pivotal role of SVCs in alleviating voltage sensitivity to RES fluctuations, thereby promoting a more stable and reliable power supply. This research underscores the importance of strategic reactive power management devices in enabling a seamless transition towards a renewable-dominant energy landscape.
Volume: 40
Issue: 1
Page: 47-56
Publish at: 2025-10-01

Cryptographically secure digital certificates on a distributed ledger

10.11591/ijeecs.v40.i1.pp236-262
Umna Iftikhar , Hafiz Muhammad Attaullah , Inam Ullah Khan , Muhammad Mansoor Alam , Mazliham Mohd Su’ud , Ahthasham Sajid
Verification of a qualification, achievement, quality, or aspect of a person’s background is one of the biggest problems nowadays as we have seen many platforms where students can get fake credentials. Every organization must select professional and academically qualified employees to give quality service. As a result, corporations rely on academic certifications to confirm and measure their prospective employees’ academic qualifications. On the other hand, these employers lack a standardized process for confirming the legitimacy of academic certificates or degrees. Because the present procedures for verifying educational certifications are time-consuming, exhausting, and costly, just a few employers verify certificates for prospective employees. This research examines the issues that are related to the smart verification of someone’s credentials. To make the process of verifying digital credentials quicker, simpler, and more cost-effective, we suggest decentralized architecture. We present the prototype, design, and implementation of the proposed framework.
Volume: 40
Issue: 1
Page: 236-262
Publish at: 2025-10-01

Integrating swarm intelligence with deep learning for enhanced social media sentiment analysis

10.11591/ijeecs.v40.i1.pp280-287
Parminder Singh , Saurabh Dhyani
Understanding user views on social media in the advent of internet content demands sentiment analysis. This study introduces a novel approach called particle swarm-accelerated model (PSAM), that integrates deep learning with long short-term memory (LSTM) with two hyper-parameters and swarm intelligence through particle swarm optimization (PSO). In the sentiment classification of YouTube movie reviews for “Pushpa 2,” the recommended approach classifies opinions as “positive,” “negative,” or “neutral,” with an accuracy score of 95.3%. The process involved utilizing YouTube API to collect user-genearted comments, followed by advanced preprocessing steps such as punctuation removal, stopword filtering, slang normalization, and emoji handling. PSO performs feature selection to boost the efficiency of classification systems. The PSAM model reaches superior outcome results compared to support vector machines (SVM), Naive Bayes, CNN, and random forest classifiers when evaluated based on F1-score and accuracy metrics. The proposed hybrid model demonstrates its ability to boost sentiment analysis in different social media platforms according to research findings.
Volume: 40
Issue: 1
Page: 280-287
Publish at: 2025-10-01

Development of hydraulic servo controller for mechanical testing with optimization of PID tuning methods

10.12928/telkomnika.v23i5.26784
Djoko Wahyu; BRIN Karmiadji , Harris; BRIN Zenal , Dede Lia; Universitas Pancasila Zariatin , Arif; Indonesian Institute of Technology Krisbudiman , Andi Muhdiar; BRIN Kadir , Yudi; BRIN Irawadi , Indra Hardiman; BRIN Mulyowardono , Budi; BRIN Prasetiyo , Nofriyadi; BRIN Nurdam , Tri; BRIN Widodo
This study explores the use of hydraulic servo control (HSC) systems in static and dynamic structural testing, focusing on optimizing proportional, integral, derivative (PID) controller tuning. The HSC system comprises three main components: hydraulic, control, and measurement systems. To achieve optimal performance, the research begins with preparing setpoint displacement/force data and developing mathematical models for the cylinder actuator and servo valve, incorporating sensors like load cells and linear variable differential transducers (LVDTs). A closed-loop transfer function is used to predict outputs that align closely with setpoint values. Three PID tuning methods—Ziegler-Nichols, Cohen-Coon, and adaptive control—are evaluated. Simulation results show all methods yield satisfactory performance with evaluation errors below 1.5%. Implementation tests further confirm effectiveness, with root mean square deviation (RMSD) values under 1%, indicating high precision. Despite promising results, the study acknowledges limitations due to restricted datasets and test conditions. Future research should address broader dynamic load variations, nonlinearities such as fluid leakage and hysteresis, and integrate intelligent optimization techniques like machine learning to enhance robustness and adaptability. This work contributes to improving the reliability and accuracy of HSC systems in structural testing, paving the way for smarter, more responsive control strategies in engineering applications.
Volume: 23
Issue: 5
Page: 1404-1414
Publish at: 2025-10-01

DDoS attack detection using optimal scrutiny boosted graph convolutional and bidirectional long short-term memory

10.12928/telkomnika.v23i5.27046
Huda Mohammed; Urmia University Ibadi , Asghar Asgharian; Urmia University Sardroud
The distributed denial of service (DDoS) attack occurs when massive traffic from numerous computers is directed to a server or network, causing crashes and disrupting functionality. Such attacks often shut down websites or applications temporarily and remain among the most critical cybersecurity challenges. Detecting DDoS is difficult and must occur before mitigation. Recently, machine learning and deep learning (ML/DL) have been employed for detection; however, architectural limitations restrict their effectiveness against evolving attack methods. This paper presents a novel framework, scrutiny boosted graph convolutional–bidirectional long short-term memory and vision transformer (SBGC-BiLSTM-ViT), which integrates graph convolutional, BiLSTM, and ViT models with machine learning classifiers such as support vector machine (SVM), Naïve Bayes (NB), random forest (RF), and K-nearest neighbors (KNN). The integration enables autonomous extraction of critical features, enhancing precision in detecting and classifying DDoS attacks. To further boost performance, a Bayesian optimization algorithm (BOA) is applied for hyperparameter tuning of SBGC and ML methods. Evaluation on benchmark datasets UNSW-NB15 and CICDDoS2019 demonstrates that the proposed approach achieves higher accuracy and effectively identifies new DDoS variants, outperforming conventional methods.
Volume: 23
Issue: 5
Page: 1212-1227
Publish at: 2025-10-01

Lexicon-based comparison for suicide sentiment analysis on Twitter (X)

10.12928/telkomnika.v23i5.25711
Munawar; Esa Unggul University Munawar , Dwi; Universitas Esa Unggul Sartika , Fathinatul; Esa Unggul University Husnah
Suicidal individuals frequently share their desires on social media. As a result, it was determined that a learning machine for early detection of suicide issues on social media was required. This study aims to examine Twitter (X) users’ suicide-related sentiment expressions. The results of searching X for the keywords ‘suicide’, ‘wish to die’, and ‘want to commit suicide’ for 4 months yielded 5,535 tweets. Following the cleaning process, 2,425 tweets were collected. The findings of labeling with the lexicon-based valence aware dictionary and sentiment reasoner (VADER) and Indonesia sentiment (INSET) lexicon, which psychologists confirmed, revealed that VADER was more accurate (92.1%) than INSET (81.6%). Sentiment research reveals negative (86.4%), positive (11.1%), and neutral (2.5%) sentiment. Support vector machine (SVM), K-nearest neighbor (KNN), and Naïve Bayes modeling results show accuracy above 86%, with SVM having the best accuracy (87.65%). Because of its great accuracy, this model can be used to identify and analyze suspicious behavior relating to suicide on X. Further research is still required, despite the excellent identification of early indicators of suicide ideation from social media posts.
Volume: 23
Issue: 5
Page: 1314-1322
Publish at: 2025-10-01
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