cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tole Sutikno
Contact Email
ijece@iaesjournal.com
Phone
-
Journal Mail Official
ijece@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
 622   623   624   625   626 
Combination of rough set and cosine similarity approaches in student graduation prediction Go, Ratna Yulika; Asianto, Tinuk Andriyanti; Setiowati, Dewi; Meilisa, Ranny; Munthe, Christine Cecylia; Kusumawardhana, R. Hendra
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp6001-6011

Abstract

Higher education institutions must deliver high-quality education that produces graduates who are knowledgeable, skilled, creative, and competitive. In this system, students are a vital asset, and their timely graduation rate is an important factor to consider. In the department of computer science, a challenge arises in distinguishing between students who graduate on time and those who do not. With a low on-time graduation rate of just 1.90% out of 158 graduates, this issue could negatively affect the institution's accreditation evaluation. This research employs the Case-Based Reasoning method, enhanced with an indexing process using rough sets and a prediction process utilizing cosine similarity. The testing, conducted using k-fold validation with 60%, 70%, and 80% of the data, produced average accuracy rates of 64.2%, 66.3%, and 65.6%, respectively. The test results indicate that the highest average accuracy of 66.3% was achieved with 70% of the cases.
Fine-tuning pre-trained deep learning models for crop prediction using soil conditions in smart agriculture Pawaskar, Praveen; H K, Yogish; B, Pakruddin; Yogish, Deepa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5667-5678

Abstract

Agriculture is the backbone of the Indian economy, with soil quality playing a crucial role in crop productivity. Farmers often struggle to select the appropriate crop based on soil type, leading to significant losses in yield and productivity. To address this challenge, deep learning techniques provide an efficient solution for automated soil classification. In this study, a dataset of 781 original soil images, including clay soil, alluvial soil, red soil, and black soil, was collected from Kaggle and augmented to 3,702 images to enhance model training. Several deep learning models were employed for soil classification, including pretrained architectures and a proposed model, SoilNet. Experimental results demonstrated that DenseNet201 achieved 100% validation accuracy, ResNet50V2 98%, VGG16 99%, MobileNetV2 99%, and the proposed SoilNet model 97%. The proposed approach outperformed existing work by surpassing 95% accuracy. Additionally, model performance was evaluated using precision, recall, and F1-score, ensuring a comprehensive analysis of classification effectiveness. These findings highlight the potential of deep learning in improving soil classification accuracy, aiding farmers in making informed crop selection decisions.
Computational modelling under uncertainty: statistical mean approach to optimize fuzzy multi-objective linear programming problem with trapezoidal numbers Shrivastava, Arti; Saxena, Bharti; Bhardwaj, Ramakant; Ghosh, Aditya; Narayan, Satyendra
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5708-5716

Abstract

This study presents a comprehensive approach to solving fuzzy multi-objective linear programming problems (FMOLPP) under uncertainty using trapezoidal fuzzy numbers. The authors propose a novel integration of Yager’s ranking method, the Big-M optimization technique, and Chandra Sen’s statistical mean methods to effectively convert fuzzy objectives into crisp values and optimize them. The methodology allows for managing multiple fuzzy objectives by ranking and aggregating them using various statistical means such as arithmetic, geometric, quadratic, harmonic, and Heronian averages. The model is implemented using TORA software and demonstrated through a detailed numerical example. The results validate the robustness and practicality of the proposed approach, showcasing consistent optimal solutions across all statistical methods. This research significantly enhances decision-making processes in uncertain environments by offering a structured, computationally efficient solution strategy for complex real-world optimization problems.
Intelligent control for distributed smart grid: comprehensive system integrating wave, fuel cell, and photovoltaic power generation B S, Manohar; Banakara, Basavaraja
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5119-5129

Abstract

The intermittent supply from renewable energy sources reckons integration of different renewable sources that can provide robust and uninterrupted energy supply to the grid. This paper applies an intelligent control method to such hybrid power generation involving a wave generator, fuel cell, and solar power generator integrated into the distribution power grid. A common DC link that supplies the voltage source converter (VSC) is powered by the output from the hybridized wave, fuel cell and photovoltaic (PV) output. Wave generator uses the rectifier DC-DC converter, PV uses a maximum power point tracking (MPPT)-controlled DC-DC converter and fuel cell uses a DC-DC converter. All DC sources converge at the DC link, connecting to an inverter featuring another voltage source controller for controlled AC voltage. In instances of power unavailability from renewable resources, the fuel cell seamlessly provides power. The inverter controls the integration of power from these sources to the grid and maintains stable DC link voltage due to the dynamic nature of the DQ controller. MATLAB-based simulation is developed for the proposed controller and a comparison between both proportional integral and adaptive neuro-fuzzy inference system (ANFIS) controller in the DC link voltage regulation loop is observed. An ANFIS controller is employed as an alternative to the proportional integral (PI) controller and found that the ANFIS controller outperformed the PI controller in voltage regulation at the DC link.
Power loss reduction and stability enhancement of power system through transmission network reconfiguration Akor, Titus Terwase; Madueme, Theophilu Chukwudolue; Ohanu, Chibuike Peter; Sutikno, Tole
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp6012-6026

Abstract

The power network faces several challenges as electricity usage rises and the frequency of partial and total grid disruptions is of great concern. This paper addresses the problem of voltage instability and high-power losses in transmission network, which threatens the stability of the power grid. The MATLAB R2023a/MATPOWER 5.0 is used to develop a model and analyze using the Newton-Raphson load flow method. The analysis reveals a marginal voltage violation at Bus 13 (below 0.95 p.u.). To enhance stability and efficiency, the network was reconfigured using a hybrid whale algorithm and particle swarm optimization (WAPSO) approach, incorporating new transmission lines (5-8 and 13-14) to improve connectivity and reduce congestion. The reconfiguration reduced active power losses by 29.5% (from 36.013 to 25.371 MW) and reactive power losses by 29.8% (from 301.30 to 211.59 MVAr). The system demonstrated first swing stability, with rotor angles remaining below π/2 (1.5669 rad maximum deviation) and fault clearance within the critical clearing time (0.2 s). Optimized exciter gains and a damping coefficient of 1.5 p.u. ensured effective oscillation suppression and stable generator voltages at 1.05 p.u. The hybrid WAPSO approach proved effective in optimizing voltage and rotor angle stability, enabling the network to meet a 24.086 p.u. load demand while enhancing overall grid reliability.
Stability analysis and robust control of cyber-physical systems: integrating Jacobian linearization, Lyapunov methods, and linear quadratic regulator control via LMI techniques Boutssaid, Rachid; Aboulkassim, Abdeljabar; Kririm, Said; Arjdal, El Hanafi; Moumani, Youssef
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5276-5285

Abstract

Stability issues in cyber-physical systems (CPS) arise from the challenging effects of nonlinear dynamics relation to multi-input, multi-output systems. This research proposed a robust control framework that combines Jacobian linearization, Lyapunov stability analysis, and linear quadratic regulator (LQR) control via linear matrix inequalities (LMIs). The robust methodology does the following: it applies linearization on the dynamics of the CPS; it establishes the stability of the system using Lyapunov functions and LMIs; and it designs an LQR controller. The proposed framework was validated through a comparison between the behavior of a linearized and nonlinear model. The autonomous vehicle application showed: a settling time of 20 seconds; an overshoot of 3.8187%; and a steady-state error of 2.688×10⁻⁷. The proposed framework is robustly demonstrated and has applications to areas in automation and smart infrastructure. Future work includes optimizing the design of weighting matrices and developing adaptive control features.
Citizens’ electronic satisfaction factors in electronic government services: an empirical study from Kuwait Alshehab, Abdullah; Alfayly, Ali; Alazemi, Naser
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5690-5698

Abstract

This study investigates the dimensions of service quality provided by Kuwait’s “Sahel” electronic government (e-government) application and their impact on user satisfaction among citizens and residents. Adopting a quantitative methodology based on the modified electronic government quality (e-GovQual) model, data were collected from 1,064 respondents over four weeks, assessing user experiences across usability, reliability, responsiveness, security, and efficiency dimensions. Results indicate moderate overall satisfaction, with particularly high ratings for transparency and ease of use, yet notable concerns regarding trust and data security. Satisfaction with reliability and technical support was moderate, signaling areas for improvement. The study recommends enhancing the user interface for intuitive navigation, improving real-time data synchronization between governmental entities, providing efficient technical support, and strengthening security measures to build user trust. These recommendations are crucial for advancing Kuwait’s e-government effectiveness. Future research should explore causal relationships among service quality dimensions and incorporate technical assessments by information and communication technology (ICT) experts to further enhance user satisfaction.
Explainable fault diagnosis using discrete grey wolf optimization algorithm for photovoltaic system Hassina, Slimani; Ouahiba, Chouhal; Yassine, Beddiaf; Rafik, Mahdaoui; Hichem, Haouassi; Roumaissa, Hamdi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5286-5296

Abstract

The present article introduces the discrete grey wolf optimization algorithm (DGWOA), a novel variant of the grey wolf optimizer (GWO). DGWOA integrates discrete optimization techniques with explainable artificial intelligence (XAI) methodologies. This approach aims to overcome limitations associated with traditional fault diagnosis methods, such as limited accuracy in identifying complex patterns and low interpretability. Furthermore, it mitigates early convergence problems commonly encountered in optimization algorithms and enhances adaptability to discrete classification challenges. The DGWOA algorithm is designed to generate interpretable classification rules for fault detection through a stochastic search strategy. The explainability provided by the model not only enhances decision-making transparency but also improves diagnostic efficiency and predictive accuracy. The proposed algorithm was evaluated using a photovoltaic system dataset and benchmarked against established rule-based classifiers. DGWOA consistently achieved a classification accuracy of 99.48% and a precision of 100%, demonstrating its effectiveness in enhancing fault detection. Moreover, the interpretability of the generated classification rules contributes to the generation of outcomes that are both actionable and comprehensible to decision-makers.
Integrity verification of medical images in internet of medical things for smart cities using data hiding scheme Devi, Kilari Jyothsna; Daniel, Ravuri; Prasad, Bode; Ishak, Mohamad Khairi; Sudarsa, Dorababu; Kiran, Pasam Prudhvi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5770-5781

Abstract

As technology has advanced, the internet of medical things (IoMT) has become incredibly useful. It is used to transmit a wide variety of medical images. Sensitive patient data may be altered during transmission or subject to illegal access. To overcome all of these challenges and preserve the integrity of medical images while transmission over IoMT, a blind region-based data concealing approach called medical image watermarking (MIW) is suggested. The region of interest (ROI) and region of non-interest (RONI) are the two sections that make up the medical image. The aim of the suggested MIW technique is to prevent transmission-related manipulation of medical image ROI. To provide high imperceptibility and resilience, confined integrity verification and recovery bits (CIVRB) bits are embedded in the RONI using hybrid integer wavelet transform–singular value decomposition (IWT-SVD). According to the experimental results, the suggested system is highly imperceptible (average peak signal-to-noise ratio (PSNR)=56dB), robust (average NC=0.99), and exhibits integrity verification accuracy of over 98% against a variety of image processing attacks. In terms of several watermarking properties, the proposed technique performs over state-of-the-art schemes. This method offers a dependable framework for protecting medical images in real-time IoMT applications and is suitable for smart healthcare environments.
Design and experimental validation of a microstrip Vivaldi antenna-based system for breast tumor detection Qanoune, Samiya; Ammor, Hassan; Er-Reguig, Zakaria; Guennoun, Zouhair
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5497-5505

Abstract

Breast cancer remains one of the leading causes of death among women worldwide, highlighting the critical need for accurate, non-invasive, and cost-effective diagnostic solutions. In light of this, microwave imaging has surfaced as a promising alternative to conventional diagnostic methods. This approach leverages its capability to differentiate between healthy and cancerous tissues by examining their dielectric properties. This study presents the design, implementation, and experimental assessment of a Vivaldi antenna-based system aimed at breast cancer detection. The antenna is designed to operate within the ultra-wideband frequency range, which facilitates high-resolution imaging and effective deep tissue penetration. Data collected from tissue-mimicking phantoms reveals the system’s proficiency in identifying anomalies, showcasing a significant contrast between malignant and normal tissue regions. We analyze various performance metrics, including signal reflection, penetration depth, and imaging resolution to substantiate the system's efficacy. The results underline the significant potential of Vivaldi antennas in improving early- stage breast cancer detection, thus contributing to advancements in microwave imaging technology.

Page 624 of 631 | Total Record : 6301

 622   623   624   625   626 

Filter by Year

2011 2026


Reset
Filter By Issues
All Issue Vol 16, No 1: February 2026 Vol 15, No 6: December 2025 Vol 15, No 5: October 2025 Vol 15, No 4: August 2025 Vol 15, No 3: June 2025 Vol 15, No 2: April 2025 Vol 15, No 1: February 2025 Vol 14, No 6: December 2024 Vol 14, No 5: October 2024 Vol 14, No 4: August 2024 Vol 14, No 3: June 2024 Vol 14, No 2: April 2024 Vol 14, No 1: February 2024 Vol 13, No 6: December 2023 Vol 13, No 5: October 2023 Vol 13, No 4: August 2023 Vol 13, No 3: June 2023 Vol 13, No 2: April 2023 Vol 13, No 1: February 2023 Vol 12, No 6: December 2022 Vol 12, No 5: October 2022 Vol 12, No 4: August 2022 Vol 12, No 3: June 2022 Vol 12, No 2: April 2022 Vol 12, No 1: February 2022 Vol 11, No 6: December 2021 Vol 11, No 5: October 2021 Vol 11, No 4: August 2021 Vol 11, No 3: June 2021 Vol 11, No 2: April 2021 Vol 11, No 1: February 2021 Vol 10, No 6: December 2020 Vol 10, No 5: October 2020 Vol 10, No 4: August 2020 Vol 10, No 3: June 2020 Vol 10, No 2: April 2020 Vol 10, No 1: February 2020 Vol 9, No 6: December 2019 Vol 9, No 5: October 2019 Vol 9, No 4: August 2019 Vol 9, No 3: June 2019 Vol 9, No 2: April 2019 Vol 9, No 1: February 2019 Vol 8, No 6: December 2018 Vol 8, No 5: October 2018 Vol 8, No 4: August 2018 Vol 8, No 3: June 2018 Vol 8, No 2: April 2018 Vol 8, No 1: February 2018 Vol 7, No 6: December 2017 Vol 7, No 5: October 2017 Vol 7, No 4: August 2017 Vol 7, No 3: June 2017 Vol 7, No 2: April 2017 Vol 7, No 1: February 2017 Vol 6, No 6: December 2016 Vol 6, No 5: October 2016 Vol 6, No 4: August 2016 Vol 6, No 3: June 2016 Vol 6, No 2: April 2016 Vol 6, No 1: February 2016 Vol 5, No 6: December 2015 Vol 5, No 5: October 2015 Vol 5, No 4: August 2015 Vol 5, No 3: June 2015 Vol 5, No 2: April 2015 Vol 5, No 1: February 2015 Vol 4, No 6: December 2014 Vol 4, No 5: October 2014 Vol 4, No 4: August 2014 Vol 4, No 3: June 2014 Vol 4, No 2: April 2014 Vol 4, No 1: February 2014 Vol 3, No 6: December 2013 Vol 3, No 5: October 2013 Vol 3, No 4: August 2013 Vol 3, No 3: June 2013 Vol 3, No 2: April 2013 Vol 3, No 1: February 2013 Vol 2, No 6: December 2012 Vol 2, No 5: October 2012 Vol 2, No 4: August 2012 Vol 2, No 3: June 2012 Vol 2, No 2: April 2012 Vol 2, No 1: February 2012 Vol 1, No 2: December 2011 Vol 1, No 1: September 2011 More Issue