cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
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 21 Documents
 1   2   3 
Search results for , issue "Vol 14, No 1: March 2026" : 21 Documents clear
Evaluation of Vector Font Rendering and Voice Recognition in Integrated Hearing Support Systems Chun, K.
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.v14i1.7516

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
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
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
Publisher : IAES Indonesian Section

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

Abstract

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, 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% obtained by the Random Forest model. Moreover, the model was better in terms of discrimination as ROCAUC scores were over 0.980447in all categories of CVD risk. Explainable AI algorithms, such as SHAP and LIME were used to provide transparency, 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 Elsayed, Youmna; Hamouda, Mohamed; El-Dessouki, M. A.; EL-Shimy, M.
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.v14i1.7252

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
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; Benziane, Madjid Meriem
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.v14i1.7150

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 (WAO). 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.
A Low-Complexity Algorithm for Estimation of The P80 Rock Granulometry Indicator Based on Digital Image Processing Aybar, José; Tasayco, Pool; Kemper, Guillermo
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.v14i1.7180

Abstract

This work presents an algorithm that automatically measures rock size distribution during primary crushing in mining, continuously monitoring without interrupting production or causing issues in subsequent crushing stages due to oversized rocks. Existing commercial solutions demand significant investment and infrastructure, making them unfeasible for small mining operations. As a result, these companies typically halt production and send samples to a laboratory to determine the P80 granulometric factor. Most current research focuses on edge detection and rock segmentation, often overlooking the estimation of granulometric parameters. The proposed algorithm uses low-complexity image processing techniques that can run on a small-board computer. Images are captured by a camera positioned above a conveyor belt within an image acquisition enclosure and processed using watershed segmentation and morphological operations like erosion and dilation. The P80 value is estimated through the Rosin-Rammler linearization model. Results show 89% accuracy compared to laboratory measurements and a 91.3% success rate.
Benchmarking Linear vs. Non-Linear Predictive Models for Energy Demand Forecasting in Electrical Motor-Driven Compressors Nazir, Humam; Izzuddin, Tarmizi Ahmad; Zulkafli, Nur Izyan; Bin Sulaima, Mohamad Fani; Jali, Mohd Hafiz; Hashim, Haslenda; Jayiddin, Nur Saleha; Md Lasin, Azmi; Iskandar, M Tarmidzi
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.v14i1.7777

Abstract

In the LNG regasification plants, among the most energy-demanding components are the electrical motor driven compressors. Artificial Neural Networks (ANNs) and Extreme Gradient Boosting (XGBoost) feature superior flexibility in modelling nonlinear and dynamic compressor behaviour, unlike the conventional methods such as Multiple Linear Regression (MLR), which is limited by its assumption of linearity. This study focuses on data-driven techniques for compressor power consumption prediction, in which MLR, ANN and XGBOOST are compared. Here, real operational data collected from two Boil-off Gas (BOG) and two Regasification Terminal Export Compressors (RGTEC) were considered to develop and evaluate the models. The findings suggest that non-linear machine learning models provide superior predictive performance in comparison to those of the traditional linear approach. The highest prediction accuracy for most compressors is achieved using the ANN model, with a n R2 values of 94.6%, 99.64% and 99.64% for BOG-A, BOG-C and RGTEC-A, respectively. Meanwhile, the best performance was found for XGBOOST for RGTEC-B, with a R2 value of 94.17% and a significantly lower RMSE value. Contrary to that, MLR produced lower accuracy in several cases, particularly when subjected to complex operating conditions, in which linear assumptions were inadequate to capture system dynamics. In addition, this research features one of the first direct comparisons between linear and nonlinear models applied to real-time compressor data, unlike past research that depends on simulations or single-method analysis. It emphasizes the practical advantages of ANN and XGBoost for data-driven energy forecasting and operational optimizations in the gas industry.
Quartz Crystal Microbalance (QCM) Sensor Array with Varying PMMA Coatings for Coffee Roasting Aroma Monitoring Muttaqin, Adharul; Sakti, Setyawan Purnomo; Naba, Agus; Mudjirahardjo, Panca
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.v14i1.7760

Abstract

This study investigates how polymethyl methacrylate (PMMA) coating concentration (3–15 wt%) tunes the response of an eight-channel Quartz Crystal Microbalance (QCM) sensor array to real coffee roasting volatiles at 200–240 °C. One channel was left uncoated as a reference, while seven channels were coated with different PMMA concentrations to introduce controlled response diversity. Baseline-corrected frequency shifts were processed into qualitative features describing response magnitude, kinetics, and early recovery, and principal component analysis (PCA) was used to visualize multichannel pattern structure across repeated roasts. Consistent temperature-dependent response patterns were observed, while run-to-run variability increased at higher temperatures. The first two principal components captured ~75% of the total variance (PC1 dominated by integrated response magnitude and PC2 reflecting kinetic variability). Because chamber humidity increased during roasting, a supplementary robustness check was performed using recorded RH; temperature-dependent structure remained after accounting for humidity effects. Overall, discrimination arises from the collective multichannel response, suggesting potential applicability of PMMA-coated QCM arrays for qualitative coffee roasting monitoring, pending further validation with larger datasets and complementary analytical methods.

Page 1 of 3 | Total Record : 21

 1   2   3 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol 14, No 1: March 2026 Vol 13, No 4: December 2025 Vol 13, No 3: September 2025 Vol 13, No 2: June 2025 Vol 13, No 1: March 2025 Vol 12, No 4: December 2024 Vol 12, No 3: September 2024 Vol 12, No 2: June 2024 Vol 12, No 1: March 2024 Vol 11, No 4: December 2023 Vol 11, No 3: September 2023 Vol 11, No 2: June 2023 Vol 11, No 1: March 2023 Vol 10, No 4: December 2022 Vol 10, No 3: September 2022 Vol 10, No 2: June 2022 Vol 10, No 1: March 2022 Vol 9, No 4: December 2021 Vol 9, No 3: September 2021 Vol 9, No 2: June 2021 Vol 9, No 1: March 2021 Vol 8, No 4: December 2020 Vol 8, No 3: September 2020 Vol 8, No 2: June 2020 Vol 8, No 1: March 2020 Vol 7, No 4: December 2019 Vol 7, No 3: September 2019 Vol 7, No 2: June 2019 Vol 7, No 1: March 2019 Vol 6, No 4: December 2018 Vol 6, No 3: September 2018 Vol 6, No 3: September 2018 Vol 6, No 2: June 2018 Vol 6, No 1: March 2018 Vol 5, No 4: December 2017 Vol 5, No 3: September 2017 Vol 5, No 2: June 2017 Vol 5, No 1: March 2017 Vol 4, No 4: December 2016 Vol 4, No 3: September 2016 Vol 4, No 2: June 2016 Vol 4, No 1: March 2016 Vol 3, No 4: December 2015 Vol 3, No 3: September 2015 Vol 3, No 2: June 2015 Vol 3, No 1: March 2015 Vol 2, No 4: December 2014 Vol 2, No 3: September 2014 Vol 2, No 3: September 2014 Vol 2, No 2: June 2014 Vol 2, No 1: March 2014 Vol 1, No 4: December 2013 Vol 1, No 3: September 2013 Vol 1, No 2: June 2013 Vol 1, No 1: March 2013 More Issue