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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 783 Documents
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Enhancing Privacy in eGovernment: A Scoping Review of Data Minimization Techniques Gamido, Marlon V.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.7110

Abstract

The protection of personal data collected by e-government services plays an important role in balancing privacy and personalization establishing user trust, operational efficiency, and regulatory compliance. This scoping review investigates data minimization techniques used in personalized e-government services, identifying available techniques, and challenges. A key strategy for enhancing privacy involves limiting data collection and processing to what is only necessary for service delivery, particularly in e-government services. The scoping review, following the PRISMA ScR approach, addresses research questions on the current data minimization techniques in e-government services, their impact on personalization, challenges and barriers to implementation, and the perceived benefits from different stakeholders’ perspectives. From the formulated research questions covering the objectives of this scoping review it identified 2408 documents using relevant search query statements from available academic databases, after conducting screening and eligibility checks, only 20 documents are included in this review. From the documents, only proportional logic and game theory data minimization technique is used in e-governance systems. The impacts of data minimization techniques to personalization, the barriers and challenges in the implementation of data minimization, and the perceived benefits from the major stakeholders of the e-government systems were identified from the covered documents. This review has provided insights as to the extent of studies which include aspects of data minimization application in various egovernment systems. Findings provide direction to future research, policy formulation, and practice, emphasizing gaps and guiding future studies to a more comprehensive understanding of balancing privacy and personalization through data minimization in e-government services.
Remote Sensing for Forensic Investigations: A Review of Techniques and Applications in Clandestine Grave Detection Moreno-Malagón, Sebastián; Garcés-Gómez, Yeison Alberto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.6375

Abstract

The location of clandestine graves is a critical challenge in forensic investigations. This review evaluates the appli-cation of remote sensing technologies to address this challenge. A comprehensive literature search was conducted across scientific databases (Web of Science, Scopus, IEEE Xplore, Google Scholar, PubMed Central, ScienceDirect), using keywords related to remote sensing, forensic science, and burial detection. Peer-reviewed articles and books focusing on remote sensing applications in forensic contexts, especially clandestine grave detection, were included. Data on methods, location, target, spectral indices, and key findings were extracted. A significant increase in pub-lications in this field was observed, particularly since 2018. Techniques included multispectral and hyperspectral imaging (satellite and UAV), LiDAR, GPR, ERT, and thermal imaging. Spectral indices (NDVI, GNDVI, VARI) were used to analyze vegetation stress. Success varied with burial depth, soil type, vegetation cover, and time since burial. Geophysical methods provided valuable subsurface information, but effectiveness decreased over time. Remote sensing offers powerful tools for forensic investigations, enabling non-invasive assessment and improved detection of clandestine graves. A multidisciplinary approach, combining multiple remote sensing techniques with geophysical methods, is crucial. Further research is needed to optimize techniques for diverse environments, improve detection of older burials, and develop standardized methodologies
Strengthening Cybersecurity: DDoS Attack Detection with Deep Learning and Innovative Hybrid Methods Chávez Campoverde, Josías; Chávez Campoverde, Misael; Chávez Campoverde, Daniel; Chávez Campoverde, Naomi; Chávez Díaz, Jorge
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.7268

Abstract

Distributed Denial-of-Service (DDoS) attacks continue to disrupt the availability of online services, motivating the development of robust and scalable detection mechanisms. This work proposes a hybrid CNN–LSTM detection framework evaluated in a controlled, sandboxed testbed for traffic generation and monitoring. The framework is implemented under a supervised learning setting and is positioned to incorporate semi-supervised and transfer learning strategies to address label scarcity and potential distribution shift in future extensions. Using a dataset of 6,000 labeled traffic logs and an 80/10/10 train/validation/test split, the proposed model achieves 98.67% accuracy, 98.01% precision, 96.73% recall, and 97.37% F1-score, outperforming Random Forest (96.42%) and a standalone LSTM (97.10%). Overall, the hybrid design supports improved detection robustness and can serve as a practical component within layered DDoS defense strategies (e.g., filtering and elastic scaling) in operational environments.

Page 79 of 79 | Total Record : 783

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