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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 825 Documents
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A Novel Linguistic Summarization of Time Series Data Based on Enlarged Hedge Algebra Formalism and Genetic Algorithm Thanh, Tran Xuan; Phong, Pham Dinh; Lan, Pham Thi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.6877

Abstract

The linguistic summarization of time series data (TSD) has been examined extensively because the extracted knowledge represented as summary sentences in natural language is interpretable for all people. The existing extracting methods use manually designed fuzzy partitions of value domains, so the word semantic depends on the subjective opinions of designers. Besides, the number of linguistic words with the fuzzy set-based computational semantics used to describe the TSD, the quantifier, and the summarizer is usually limited to 7±2. That cardinality is not rich enough to describe the special characteristics in a certain period in the TSD. In this paper, enlarge hedge algebra is applied to create a mathematical formalism for automatically designing interpretable and scalable multi-level semantic structures for the corresponding value domains of linguistic variables and these structures can be arbitrarily extended as needed. The objectives of the applied genetic algorithm were also adjusted to improve the optimization goals. The experimental results on the patient admission data have shown that our proposed methods obtain the outstanding results in terms of accuracy, conciseness, and coverage.
Hybrid 3D U-Net Transformer for Precision Glioma Segmentation Bounegta, Ahmed; Khelifi, Mustapha; Beladgham, Mohammed; Ouldammar, Abdellah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.6189

Abstract

Gliomas are one of the most prevalent malignant brain tumors, presenting considerable problems for patient prognosis and therapeutic approaches. Precise segmentation of these tumors is essential for diagnosis, surgical planning, and intraoperative radiologic monitoring (IRM). Defining glioma subregions, including the enhancing tumor (TE), tumor core (TC), and whole tumor (WT), enables targeted therapy and aids in monitoring tumor growth over time. This article presents the 3D U-Net Transformer, a deep learning architecture that integrates convolutional layers with transformer-based self-attention mechanisms. The model efficiently analyzes multimodal MRI scans, utilizing skip connections and attention modules to merge local spatial data with global context, thus improving segmentation performance. The 3D U-Net Transformer, validated on the BraTS 2020 dataset—a benchmark for brain tumor segmentation—surpassed traditional topologies like U-Net and UNet++. The model attained elevated Dice coefficients for TE, TC, and WT areas, accompanied by robust sensitivity and specificity metrics, hence, enhancing its clinical dependability. This sophisticated method enhances surgical procedures and clinical decision-making by providing accurate tumor delineation. The integration of transformer modules within U-Net architectures highlights the possibility of significant advancements in 3D medical imaging and real-time applications. The computer setup for this study comprised a high-performance PC featuring an Intel i7 CPU, 12 GB RAM, x64 architecture, Intel HD Graphics 3000, operating on Kaggle with a P100 GPU, and utilizing Python 3.8, Kaggle, and TensorFlow 2.4 on a 64-bit operating system.
Optimizing K-Means Clustering Parameters for Mapping Smart Contract Transaction Characteristics: A Comparative Analysis of Evaluation Metrics in the IOTA Ecosystem Ubaya, Huda; Stiawan, Deris; Suprapto, Bhakti Yudho; Ekaputra, Rivaldi Febrian; Afifah, Nurul; Ningrum, Septiani Kusuma
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7741

Abstract

Smart contracts are already a major development in digital transaction automation thanks to blockchain technology, but their operational efficiency is still greatly impacted by resource consumption, transaction success rates, and gas cost dynamics. This study aims to optimize the K-Means Clustering algorithm's parameters in order to map the characteristics of smart contract transactions in the IOTA ecosystem and provide thorough insights into the efficiency of gas allocation. Using a massive dataset of 566,303 empirical transactions from the IOTA Tangle, three key metrics the Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index were compared to verify the quality of the clustering. With a Silhouette Coefficient value of 0.9851, Davies-Bouldin Index of 0.4622, and Calinski-Harabasz Index of 741,423.92, quantitative evaluation results demonstrate that the 3- cluster structure performs better than two clusters. These results validate the 3-cluster model's ability to more accurately divide transactions into categories that are efficient, complex, and gas-inefficient. The results of this mapping can serve as the foundation for creating an automated recommendation system for optimizing transaction costs in decentralized networks. This study shows that the Gas Limit and Gas Consumed indicators are crucial predictors of transaction efficiency.
Internet of Things (IOT) Applications for Estrus Detection and Management in Precision Livestock Farming: A Review Ashraf, Arselan; Nisa, Syed Qamrun; Ashraf, Afreen; Gunawan, Teddy Surya; Sophian, Ali
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7359

Abstract

The livestock industry represents a vital sector of the global economy, where reproductive management plays a key role in sustaining productivity and profitability. Estrus detection, a critical component of reproductive efficiency, directly influences breeding success and overall herd performance. Recent advancements in the Internet of Things (IoT) have introduced new opportunities to enhance estrus detection through the integration of sensors, data analytics, and machine learning algorithms. This review explores the potential of IoT-based technologies in livestock estrus detection, focusing on a wide range of approaches including wearable and non-wearable sensors, data collection frameworks, and advanced analytical methods. Commercial IoT-based estrus detection systems are also examined, alongside comparative evaluations of detection performance, advantages, and limitations. Key challenges such as battery life, connectivity, network coverage, data security, privacy, and cost scalability are discussed in detail. Furthermore, the paper highlights future directions, including the integration of IoT with precision livestock farming and the role of emerging technologies in improving animal welfare and production efficiency. Overall, this review provides a comprehensive overview of IoT-based estrus detection, outlining current progress, practical implications, and recommendations for future research and implementation.
Building a brain cancer treatment hospital recommendation system in Vietnam Truong Ho-Viet Phan; Anh Nhat Lam; Bao The Phung
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.7087

Abstract

The purpose of this paper is to suggest an integrated system that combines the classification of medical images and a recommendation model for the choice of the best hospitals for cancer treatment according to the analysis of the brain MRI. For the classification of brain MRI images for tumor detection, we used EfficientNet-B3 as the base model. To evaluate its performance, we contrasted it with two other popular deep learning architectures, namely Vision Transformer (ViT) and ResNet101. Having identified malignancies by image classification, we used the outputs as inputs for a collaborative filtering based recommendation system. This system was constructed utilizing neural network embeddings and hidden vectors to learn the correlations between tumor types and hospital treatment characteristics. Personalized hospital suggestions were generated by calculating the similarity between the embeddings using the dot product. In addition, comparison evaluations with a traditional K-Nearest Neighbors (K-NN) method are performed to evaluate the performance gaps. The classification results revealed that EfficientNet-B3 has better accuracy when compared with ViT and ResNet101 and it is a good choice for medical picture analysis. The neural collaborative filtering model shows superior accuracy and suitability of the hospital selection during the recommendation phase compared to K-NN. The results of this study demonstrate that the combination of deep learning and intelligent recommendation systems may successfully provide excellent diagnosis in healthcare. The proposed framework may improve decision making and may provide individualized recommendations of hospitals for cancer treatment. A case study is offered in Vietnam to recommend hospitals.
Deepfake Detection Based on Deep Learning and Quantum Particle Swarm Optimization Ebeid Ali Ebeid; Ahmed Sobhi; H. A. El Shenbary
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.7203

Abstract

Recently, many fake images and videos can be created and manipulated easily by employing Artificial Intelligence technologies, mainly based on Generative Adversarial Network (GAN). These synthesized media (images or videos) can be used later in unethical ways to create panic among individuals. This type of technology, which synthesizes human images and videos, is called deepfake. This paper proposes a deepfake detection methodology relying on Deep Learning (DL) approach and quantum particle swarm optimization (QPSO). The input image undergoes preprocessing by choosing the Region of Interest (ROI), followed by the application of an image downsampling algorithm to minimize the dimension of the new sub-image and thus decrease processing time costs. After that, the image has been normalized. The optimal subset of dataset has been determined by adapting QPSO during training phase. To perform classification of images and videos, ResNet50 model accomplishes this task perfectly. Experiments indicate that the suggested algorithm demonstrates a significant level of accuracy on ”Real and Fake Face Detection ” and ” Celeb-DF” datasets. The proposed approach achieves accuracy 99.6 % on Celeb-DF and 99.8 % on Real and Fake Face Detection dataset.
Design of a Visual Welding Defect Inspection Robot on The Outer Piping Line System based on The CNN Algorithm Noorman Rinanto; Ari Shinta; Ryan Yudha Adhitya; Mahmoud Elsisi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.7641

Abstract

The investigation focuses on using deep learning techniques to design a testing camera system and identify welding-related issues in outer pipes. The system uses an RGB camera, an ESP32 microcontroller, a DC motor with a rotary encoder, an LED strip, and a small computer that has a graphical user interface. This prototype was made to help with automating the inspection of carbon steel pipe welding, focusing on identifying and grouping different types of defects like porosity, undercut, and too much reinforcement. The results show that the system works in real time, achieving an average of 10.35 frames per second, taking 0.21 seconds to detect something, and having a confidence level of 97.13%. The built-in GUI shows the original image, the edge detection results, and the cropped ROI area, along with motor control and an LED strip. Mechanically, the prototype demonstrates sufficient stability for laboratory research; however, for industrial-scale research purposes, adjustments to materials and dimensions are required. This further develops an automated inspection system based on digital image processing and is designed to be safe, efficient, and compliant with safety standards.
A Real-time Robust English and Bangla License Plate Recognition Using Enhanced GAN and Explainable CNN Jul Jalal Al-Mamur Sayor; Nishat Tasnim Shishir; Mahe Zabin; Suman Saha; Kamruddin Nur; Jia Uddin
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.6823

Abstract

Efficient systems tailored to countries with bilingual license plates remain scarce, and existing methods often struggle to adapt the unique challenges posed by these scenarios. Countries like Bangladesh and India face challenges with the existing techniques. Accurate recognition of bilingual license plates is crucial for traffic management, but conventional methods with limited resources often fail to work effectively with low-quality footage. Thus, this research provides a framework for the resource-efficient solution for bilingual number plates, ESRGAN for resolution enhancement and explainable CNNs with transfer learning for language classification and character recognition. We divide the task into two sub-tasks and train four specialized CNN models for Bangla and English characters and numerals to maximize the system’s performance. The proposed two-tier architecture optimizes computational efficiency by dynamically loading models based on recognition requirements. Overall, using the gradient-based explainability techniques in the proposed framework increases the credibility of decision-making. The proposed models achieved accuracies of 96%, 97.48%, 97.15%, and 97.62% for English license plate recognition, Bangla license plate recognition, Bangla number recognition, and English number recognition, respectively. While maintaining competitive accuracy, the proposed approach outperforms state-of-the-art works in resource efficiency and computational time, which are crucial for edge deployments. Furthermore, the license plate detection and character extraction algorithm proposed in the paper demonstrates excellent accuracy for both black and white-colored plates. This approach ensures consistent performance and efficient resource utilization while showing the ability to work with a variety of traffic conditions, making it suitable for automated vehicle management systems.
Cendrawasih Bird Optimization Algorithm : A nature-inspired metaheuristic Widi Aribowo; Laith Abualigah; Diego Oliva; Abeer Aljohani; Aliyu Sabo
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.7419

Abstract

This study proposes a new metaheuristic algorithm named Cendrawasih Bird Optimization Algorithm (COA), inspired by the natural behavior of Cendrawasih birds—an endemic species of Indonesia. COA mimics the main behavior of the birds, namely exploration and exploitation, to create a balance between global exploration and local exploitation capabilities in solving various optimization problems. This algorithm is mathematically modeled and evaluated using 23 benchmark functions from CEC 2017, and its performance is compared against eight state-of-the-art metaheuristic algorithms. The results of numerical experiments, convergence analysis, and statistical tests show that COA has competitive performance, especially in terms of stability and consistency on unimodal and multimodal functions. Overall, COA is proven to be a reliable, robust, and efficient optimization tool with broad application potential in various engineering and scientific fields.
Integration of Voting-Based Statistical Ensembles and Rule Mining for Anomaly Detection in Microsatellite Power System Rizki Permala; Imas Sukaesih Sitanggang; Hendra Rahmawan; Wahyudi Hasbi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.7561

Abstract

This study aims to detect anomalies, types of anomalies, and identify attributes that contribute to the cause of anomalies in the LAPAN-A2 microsatellite power system. Anomaly detection is cumulative-based, where the first year's dataset is added to the datasets of subsequent years for eight years. Anomaly detection uses a voting-based statistical ensemble (VBSE) approach and a combination of Apriori-Close to find associations and reduce rules. The VBSE obtained an average recall value of 86.49 and an average F1-score of 70.49. The F1-score value of VBSE increased by 4.2-fold compared with IForest (16.95), by 12.6-fold compared with ECOD (5.57), and by 5.6-fold compared with LOF (12.65). VBSE showed an increase in performance as the dataset complexity increased (DS1 → DS8), whereas IForest, LOF, and ECOD tended to decrease. The combination of metrics minSupp. 0.02%, minConf. 0.9, and lift is proven to be effective in capturing rare, reliable anomalies and significant association relationships. A strong correlation was observed between batteries (VBatt1–VBatt3) and a causal relationship between U_UMPB and VBatt. The types of anomalies detected included single, contextual, and correlation anomalies.

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