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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 799 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.
Evaluation of Vector Font Rendering and Voice Recognition in Integrated Hearing Support Systems CHUN, KYUNGHAN
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7516

Abstract

This paper focuses on the implementation of core functionalities for a Hearing Support System (HSS) and the validation of its engineering feasibility. The system is designed to address the limitations of conventional hearing aids, specifically their restricted personalized calibration and environmental adaptation. The proposed HSS is a smartphone application-based system characterized by key functions: personalized settings derived from individual audiogram profiles, environment-specific presets, and real-time speech translation with textual display. Regarding the system's auxiliary output, the implementation of a Hangul (Korean) display is presented. A comparative analysis between a low-cost ESP32-based implementation (utilizing bitmap fonts) and a Raspberry Pi-based counterpart (employing vector fonts) empirically validates the necessity of vector fonts for enabling font scaling functions, which are crucial for users with low vision. For speech recognition, the study adopts an approach that transforms one-dimensional time-series audio waveforms into two-dimensional 'sound images,' specifically spectrograms, which serve as input for a Convolutional Neural Network (CNN). Conclusively, this research successfully prototyped the core functionalities of the HSS at a Proof of Concept (PoC) level, utilizing tools, thereby confirming its integration feasibility. Nevertheless, several key areas are identified as future tasks for practical deployment: the refinement of preset functionalities, the elimination of dependencies on external APIs, and fundamental enhancements to speech recognition performance through the adoption of deeper CNN architectures.
A Context-Aware Itinerary Recommendation Model Based on CBR with Auto-Revise and Multi-Clustered Data Modeling Faizal, Edi; Hartati, Sri; Musdholifah, Aina
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7000

Abstract

This study proposes an itinerary recommendation model based on Case-Based Reasoning (CBR), enhanced with an auto-revise mechanism and multi-cluster modeling using the DBSCAN algorithm. The model is developed from four primary data sources: historical travel cases, visit statistics, social media reviews, and contextual data. The auto-revise mechanism is activated when case similarity falls below 0.95, allowing solution adjustments based on six feature subsets: spatial, categorical, attraction, destination type, popularity, and visitor segmentation. Evaluation was conducted through 5-fold cross-validation and new-case testing, yielding F1-scores of 92.60% and 90.29%, respectively, while ranking performance remained consistently high across both evaluation scenarios. The model also demonstrated improvements in recommendation quality metrics, including novelty, diversity, and serendipity, alongside a reduction in average response latency from 25.53 ms to 20.09 ms. These results indicate that the proposed integrative CBR auto-revise approach, supported by contextual data and multi-cluster structuring, provides an adaptive and efficient itinerary recommendation framework suitable for real-time decision-support scenarios.
Design and Optimization of EMC Filtering Strategies for DC-DC Converters in Electric Vehicles Applications Lghazi, Soufiane; M'barki, Zakaria; Mejdoub, Youssef; Senhaji Rhazi, Kaoutar; Ait Salih, Ali
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7394

Abstract

The rapid electrification of vehicles intensifies electromagnetic interference (EMI) challenges in DC–DC converters, particularly isolated topologies used for high-voltage to low-voltage energy transfer. High-frequency switching generates common-mode (CM) and differential-mode (DM) conducted noise that threatens compliance with stringent CISPR 25 Class 5 standards. This paper proposes the design, modeling, and evaluation of a compact electromagnetic compatibility (EMC) filter capable of simultaneously suppressing CM and DM emissions in an isolated DC–DC converter for electric vehicle applications. The proposed passive filter combines a CM choke with Y-capacitors, a DM π-filter using X-capacitors and series inductors, and an RC damping branch to avoid resonances. The converter and filter were modeled in LTspice, and conducted emission spectra were evaluated using a Line Impedance Stabilization Network (LISN) with Fast Fourier Transform (FFT) analysis. Simulation results demonstrate that conducted emissions are reduced by about 40 dBµV, ensuring full compliance with CISPR 25 Class 5 limits. The proposed solution offers a cost-effective and practical approach to improve EMC margins and reliability in automotive DC–DC converters. The results presented in this study are based on circuit-level simulations, and experimental validation will be addressed in future work.
Cardiovascular Disease Risk Classification Using Machine Learning with Weighted Feature Fusion and Explainable AI on Bangladeshi Clinical and Lifestyle Data Asif, Tasnimul Intazam; Ray, Bishwaprotap; Hossain, Md. Alomgir; Imran, Faisal; Barua, Prime Biswajit; Anisha, Nishat Salsabil; Minhaj, Ariful Haque; Roy, Amit
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7421

Abstract

Article history: Cardiovascular disease (CVD) is one of the top causes of death across the world, and there is a need to develop early risk prediction models that can be accurate and interpreted. This study introduces a weighted feature fusion (WFF) model of machine learning to integrate clinical, lifestyle, and engineered features into an integrated machine learning model to improve the classification of CVD risk and the interpretability of the model. Several classifiers, such as the Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), Bagging, and Decision Tree, were trained and tested based on fusion-based methods. The experimental findings indicated that the highest classification accuracy of the model, at 91%, is obtained by the Random Forest model. Moreover, the model was better in terms of discrimination, as ROC-AUC scores were over 0.980447 in all categories of CVD risk. Explainable AI algorithms, such as SHAP and LIME, were used to provide transparency, which, when combined with feature fusion, leads to a significant improvement in accuracy, reliability, and interpretability of CVD risk prediction models that can lead to the development of data-driven healthcare decision support systems of trust
INTEGRATING EVS, PV, AND ESS IN COMMERCIAL PARKING LOTS: A COOPERATIVE NASH GAME FOR TRANSACTIVE ENERGY Mohamed, Youmna Elsayed; Hamouda, Mohamed; El-Dessouki, Maher; EL-Shimy, Mohamed
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7252

Abstract

Electric vehicles (EVs) are widely recognized as a key solution for reducing emissions in the transportation sector. Their growing adoption, combined with flexible charging schedules, presents a valuable opportunity to enhance grid operations. Unlike traditional approaches, a transactive energy (TE) model provides a more balanced framework, creating mutual benefits for both the grid and EV owners while ensuring that owners retain autonomy to decide how and when their vehicles are charged. In this work, we present a comprehensive TE management framework designed to optimize energy exchange among EVs, photovoltaic (PV) systems, battery energy storage systems (BESS), and the utility grid, addressing the limitations of conventional centralized energy markets. A novel EV parking lot model is proposed, enabling peer-to-peer (P2P) transactions powered exclusively by renewables and the grid, supporting both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations to enhance energy utilization. The proposed model enables peer-to-peer (P2P) electricity transactions within a decentralized architecture. To capture the strategic behavior of self-interested energy agents, a game-theoretic approach based on Nash equilibrium is formulated, enabling coordinated decision-making under competitive conditions. The model is implemented using a non-linear programming formulation in GAMS and tested over a 24-hour operational cycle. Comparative analysis between a baseline scenario and the Nash-based model reveals significant improvements in energy utilization, cost-effectiveness, and overall system reliability. The results demonstrate that the proposed cooperative game-theoretic framework not only enhances economic performance but also promotes grid stability and equitable resource allocation, positioning it as a viable solution for future decentralized energy systems.
An AHP-Modified TOPSIS and Pareto Model for Employee Turnover Intention Analysis Al Abid, Faisal Bin; Bakri, Aryati Binti; Chowdhury, Shefayatuj Johara; Uddin, Jia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.6506

Abstract

Employee turnover intention (TOI) is a significant challenge that affects an organization financially, particularly in the context of Indonesian academic sector, where turnover rate is notably high. This study uses a primary Indonesian academic dataset and proposes a novel framework for Indonesian academic turnover intention (TOI) encompassing Analytic hierarchy process (AHP), Modified TOPSIS combined with Pareto principle and compares the proposed frame- work with existing framework of entropy-based weighted method, traditional TOPSIS and interval scaling for categorizing academic employees according to productivity. The AHP procedure encompasses hybrid logarithmic linear normalization integrating linear as well as logarithmic normalization, consequently ensuring consistency and robustness for categorization of TOI. The proposed framework integrates Euclidean, Manhattan, Chebyshev distance for resolving the issues of traditional TOPSIS for ranking alternatives. The modified TOPSIS incorporates Information Gain, Recursive feature elimination (RFE) and Select K-best for finding Indonesian academic TOI. Random forest was implemented as the baseline classifier model for both the proposed and existing scheme. Experimental results revealed that proposed approach achieved higher predictive accuracy in contrast to the existing approach for categorizing employees into enthusiastic, behavioral and distressed. Therefore, this study establishes a robust approach for employee categorization outperforming the existing approach.
Intelligent Interconnection of Parallel LCC-HVDC Links and AC Grids for Transient Stability Enhancement under Faults Conditions Bakdi, Moussa; Taleb, Rachid; Toualbia, Asma; Mellah, Hacene; MEHEDI, Fayçal; BOUYAKOUB, Ismail; MERIEM BENZIANE, Madjid
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7150

Abstract

The intelligent interconnection of AC grids across diverse regions, incorporating renewable energy sources and complex, a nonlinear grid configurations, presents significant challenges to power system stability. To mitigate instability and suppress power oscillations during short-circuit faults in AC Grids, this work focuses on leveraging high-performance power electronic converters, specifically Line Commutated Converters (LCCs) based on High-Voltage Direct Current (HVDC) in parallel links, to facilitate efficient power exchange and enhance grid integration and stability. This work proposes an advanced control framework for intelligent interconnection systems; a dual-layer control strategy is introduced, combining a Conventional Power System Stabilizer (CPSS) for local damping of synchronous generator oscillations and a Power Oscillation Damping (POD) controller for global mitigation of inter-area and wide-area oscillations. This integrated approach is established as a leading control methodology for LCC-HVDC systems, enabling robust synchronous interconnections between hybrid AC/DC grids. The paper further examines key challenges in designing and implementing the LCC-HVDC-based POD controller, addressing dynamic performance and system-wide coordination. Dynamic simulations are conducted using the Power System Toolbox (PST) in MATLAB, leveraging its user-friendly interface and computational efficiency. The results demonstrate superior dynamic response, with rapid oscillation damping and enhanced steady-state performance, validating the proposed controller's efficacy in improving transient stability.

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