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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 804 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.

Page 81 of 81 | Total Record : 804

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