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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
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Q-learning based active monitoring with weighted least connection round robin load balancing principle for serverless computing Balan, Yashwanth; Paul Rajan, Arokia
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3171-3179

Abstract

Serverless computing is considered one of the most promising technologies for real-time applications, with function as a service (FaaS) managing service requests in serverless computing. Load balancing played a vital role in assigning tasks in serverless computing for customers; user requests were controlled by load balancing algorithms and managed using machine learning techniques to deliver results and performance metrics within specified time limits. All serverless computing applications aimed to achieve optimal performance based on the most effective load balancing techniques, which directed requests to the appropriate servers in a timely manner. This research focused on developing a novel Q-learning based active monitoring with least connection round robin load balancing principle (Q-LAMWLR LB) for serverless computing to address the aforementioned challenge. Also, aimed to intelligently assign requests to serverless computing based on the number of requests arriving at the load balancer and how intelligently they could be directed to the appropriate server. This work utilized standard techniques to calculate the average response time for each scheduling algorithm and develop a novel intelligent load-balancing technique in serverless computing. Required experiment were conducted and the results are giving the improvement as compared to other load balancing principles. The further research in this area also identified and presented.
An intelligent approach to design big data on e-commerce in cloud computing environment Syed, salma; Sundari, Nadimpalli Usha Deepa; Dogiparti, Satish Babu; Rani, Duggimpudi Mary Sharmila; Yenireddy, Ankireddy; Kumar, Narayana Srinivas; Sunkara, Rajeev; Narasimha Raju, Buddaraju Naga Venkata
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3439-3448

Abstract

Web resources extract useful knowledge by the process of web mining. Web server maintains the log files for analyzing them from behavior of customer and improves business as the challenging task for E-commerce companies. The processing and computing of big data was increased day by day by the demand of computer system’s ability. The emphasis on data was increased gradually by the rapid development of information technology. Various businesses are exploring effective data analysis methods, and this system proposes an intelligent approach to designing big data for e-commerce in a cloud computing environment. This paper aims to develop and implement the relevancy vector (RV) algorithm, an innovative page ranking algorithm based on Hadoop distributed file system (HDFS) map reduce. The research provides customers with a robust meta search tool that makes it easy for them to understand personalized search requirements and make purchases based on their preferences. The intelligent meta search system adverse events (IMSS-AE) tool and the RV page ranking algorithm were shown to be efficient and effective by a thorough experimental evaluation in terms of reduced response time, enhanced page freshness, high personalized relevance, and high hit rates.
Development of watershed algorithm for identification of diabetic retinopathy based on fundus images Surmayanti, Surmayanti; Sumijan, Sumijan; Bukhori, Saiful
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2845-2856

Abstract

Diabetic retinopathy (DR) is a serious complication of diabetes that can lead to blindness if not detected early. This research presents a novel method for the identification of DR using fundus images, employing the Watershed Algorithm for accurate image segmentation and the gray level co-occurrence matrix (GLCM) for texture feature extraction. The image processing pipeline involves several stages, including grayscale conversion, noise reduction through Gaussian and median filters, and Otsu's thresholding to isolate key features such as retinal lesions. The watershed algorithm is applied to delineate the boundaries of abnormal regions, while the GLCM method extracts texture features like contrast, correlation, energy, and homogeneity, which are essential for diagnosing retinal abnormalities. The proposed approach demonstrates a high accuracy rate of 92%, successfully identifying abnormalities in 46 out of 50 fundus images. The method shows significant potential for enhancing early detection of DR, providing accurate segmentation and texture analysis, making it a valuable tool for medical professionals in diagnosing retinal diseases.
Comparative analysis of YOLOv8 techniques: OpenCV and coordinate attention weighting for distance perception in blind navigation systems Utami, Ema; Syahrudin, Erwin; Hartanto, Anggit Dwi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3267-3278

Abstract

Blindness is a very important issue to consider in research aimed at assisting vision. This condition requires further study to provide solutions for the blind. This study evaluates and compares the effectiveness of the you only look once v8 (YOLOv8) model integrated with OpenCV and the coordinate attention weighting (CAW) technique for distance estimation in a blind navigation system. Initially, YOLOv8 integrated with OpenCV produced less than optimal results, prompting further improvement efforts to surpass the performance of CAW. The goal is to enhance the accuracy and efficiency of distance perception without the need for additional sensors. The materials used include a variety of datasets annotated with distance information to train and evaluate the model. The methods employed include integrating YOLOv8 with OpenCV for baseline comparison and applying CAW to improve distance perception through enhanced feature attention. The results show that YOLOv8+OpenCV Improved achieves the lowest mean squared error (MSE) across the entire distance range: 0-1 m (0.44), 1-2 m (0.50), 2-3 m (0.58), 3-4 m (0.64), and 4-5 m (0.71). YOLOv8+CAW also outperforms YOLOv8+OpenCV original, demonstrating a notable enhancement in accuracy. The model achieves a detection accuracy of 95.7%, showcasing the effectiveness of computer vision techniques in supporting blind navigation systems, offering precise distance estimation capabilities and reducing the reliance on external sensors. The implications include improved real-time performance and accessibility for the blind, paving the way for more efficient and reliable navigation assistance technologies.
Implementing brain tumor detection using various machine learning techniques Puspita, Rani; Rahayu, Cindy
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3309-3318

Abstract

The brain is a very complex organ of the human body. One of the brain diseases is a tumor. Brain tumors are caused by uncontrolled cell growth. Early recognition, classification and analysis of brain tumors is very important to find out whether there is a tumor in a person's brain so it is important for us to do this in order to treat the tumor thoroughly. Machine learning (ML) techniques that have the highest accuracy in detecting the health sector are extreme gradient boosting (XGBoost), logistic regression, random forest, k-nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). In this research, data collection and exploration were carried out, data training using six methods, and evaluation using a confusion matrix. After conducting the experiment, it was obtained that random forest had the highest accuracy, namely 98.41%. Where XGBoost obtained an accuracy of 98.14%, logistic regression obtained an accuracy of 97.34%, KNN and naive Bayes of 97.34%, and SVM of 97.88%.
Notice of Retraction Cuckoo search algorithm approach for optimal placement and sizing of distribution generation in radial distribution networks Ojo, Kayode; Fanifosi, Seyi; Ayokunle, Awelewa; Samuel, Isaac
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2681-2696

Abstract

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ijece@iaesjournal.com.-----------------------------------------------------------------------Radial distribution networks (RDNs) often experience power loss due to improper distribution generation (DG) allocation. Strategic DG placement can reduce power loss, minimize costs, and improve voltage profiles and stability. This research optimizes DG placement and sizing in RDNs using the cuckoo search algorithm (CSA). The objective function considers losses across all network branches, and CSA identifies optimal DG locations and sizes. Tested on IEEE 33-bus, IEEE 69-bus, and Nigeria's Imalefalafia 32-bus RDN, the Cuckoo Search technique results in optimal DG locations at buses 6, 50, and 18 with corresponding sizes of 2.4576, 1.852, and 2.718 MW, respectively. Voltage improvements are 0.9509, 0.9817, and 0.9821 p.u, while total active and reactive power losses for IEEE 33-bus are reduced by 49.03% and 45.00%, and for IEEE 69-bus by 63.67% and 61.14%. The CSA approach significantly enhances voltage profiles and reduces power losses in these networks.
Generation of business process modeling notation diagrams from textual functional requirements in Indonesian Sholiq, Sholiq; Yaqin, Muhammad Ainul; Subriadi, Apol Pribadi; Setiawan, Bambang
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2938-2950

Abstract

This study proposes a method for converting textual functional requirements in Indonesian to business process modeling notation (BPMN) diagrams. has not been found in previous studies. The use of BPMN diagrams to present software functional requirements has the advantage of being better in terms of presenting sequential activities than using use case diagrams. On the other hand, the requirements obtained from clients in the requirements collection session are more in the form of user stories, namely text in natural language. The method used in this study is to integrate natural language processing and a set of mapping rules and BPMN diagram generation rules. The proposed method is tested with 15 functional requirement cases from three applications, namely mini hospital software, employee cooperatives, and stores. Then, the results are compared with diagrams made by experts for the same cases. The test results show an accurate level of the proposed method of 94.4%.
Enhancing linear quadratic regulator and proportional-integral linear quadratic regulator controllers for photovoltaic systems Assabaa, Mohamed; Bouchahed, Adel; Draidi, Abdellah; Mehimmedetsi, Boudjema; Belhani, Ahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2713-2725

Abstract

This article introduces the linear quadratic regulator (LQR) control and the hybrid linear quadratic regulator proportional-integral (LQR-PI) control, both applied to a photovoltaic system coupled with a DC-DC boost converter. The converter outputs direct current electrical energy to power direct loads. Two robust control correctors, based on the LQR and LQR-PI methods, are designed to enhance the static and dynamic performance of the PV DC-DC boost system. These controllers aim to minimize oscillations and overshoots while ensuring stability across varying solar conditions, thereby optimizing operation around the maximum power point. The disturb and observe maximum power point tracking (MPPT) technique, integrated with the LQR and LQR-PI controllers, ensures system functionality under disturbances. The novelty of this work lies in the development of a MATLAB control block diagram capable of regulating the reference voltage provided by the perturb and observe (P&O) MPPT algorithm. MATLAB simulations demonstrate the robustness and high performance of the LQR and LQR-PI controllers, validating the efficacy of this boost converter control strategy.
Classification of customer complaints on social media for e-commerce in Indonesia Aditama, Achmad Rizki; Wicaksono, Alfan Farizki
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2977-2985

Abstract

The e-commerce industry in Indonesia has experienced rapid growth, especially during the COVID-19 pandemic, which accelerated the shift to online platforms. The market is expected to grow by 105.5% from 2025 to 2030 due to increased internet and smartphone use. As e-commerce expands, companies must improve how they handle customer complaints to build trust and loyalty. Social media is a crucial channel for customer interactions, but it also includes non-complaint messages like positive comments, general questions, and spams that need to be filtered out. This research proposes a machine learning model to automatically classify social media interactions into complaints and non-complaints, focusing on Indonesian-language content. The modeling process utilized 10,600 data points collected from social media X. The best model, a bidirectional encoder representation from transformers (BERT) based classifier, achieved an F1-score of 98.3%. The McNemar test revealed significant performance differences between several models, with the BERT-based model outperforming others. This demonstrates that it is highly effective in distinguishing between complaints and non-complaints, making it a valuable tool for enhancing customer service in Indonesia's e-commerce sector.
Enhancing tabular data analysis for classification of airline passenger satisfaction using TabNet deep neural network Kaidi, Rachid; Omara, Hicham; Lazaar, Mohamed; Al Achhab, Mohammed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3362-3372

Abstract

In an era of air travel, understanding and enhancing passenger satisfaction are pivotal to the success of airlines and the overall passenger experience. Analyzing airline passenger satisfaction using tabular data can pose various challenges, both when employing classical statistical methods and when leveraging machine learning and deep learning techniques. On the one hand, statistical approaches pose various challenges including limited feature engineering techniques, the assumption of linearity of the data sets and limited predictive power. Then again, the use of machine learning and deep learning techniques may face other challenges such as the problem of overfitting, difficulty in interpreting data, intensive resource requirements, and the generalization problem in deploying machine learning-based methods. This paper presents a novel deep learning approach utilizing TabNet to classify airline passenger satisfaction. Leveraging a comprehensive dataset comprising various passenger-related attributes, our TabNet-based model demonstrates exceptional performance in distinguishing between satisfied and dissatisfied passengers. Our model’s robustness in handling tabular data, underscores its power as a valuable tool for the aviation industry. Comparing out results to recent papers show that out model outperforms these studies in terms of accuracy, precision, recall and area under the curve. The results show that our TabNet Network model outperforms all implemented machine learning models by reaching respectively the following results: 96.47%, 96.41% and 96.24% for accuracy, F1-score and G-mean score.

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