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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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Optimizing Energy Management in Hybrid Systems: A Case Study on PV, Battery, and Hydrogen Electrolysis Rada, Rawan A; Anwar, Naveed; Hussein, Aziza I.; M. Aly, Rabab Hamed
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.v13i4.7173

Abstract

Energy-intensive facilities face significant challenges in managing energy costs while ensuring reliable power for critical operations. This paper explores the integration of renewable energy and hydrogen technologies in a hybrid system, aimed at reducing grid dependency, minimizing energy costs, and contributing to environmental sustainability. A hybrid energy system comprising solar photovoltaic (PV) generation, battery storage, hydrogen production via electrolysis, and a proton exchange membrane (PEM) fuel cell was developed and simulated using MATLAB Simulink. The system was controlled by an intelligent energy management system (EMS) based on fuzzy logic, which dynamically prioritized energy sources to ensure operational autonomy. A hospital in Jeddah is used as a case study to demonstrate the application of this hybrid system. Simulation results showed that the hybrid system could generate up to 3,017,359 kWh annually, reducing the cost of energy (COE) from $0.19/kWh to $0.11/kWh. The system alleviated grid load by 3,000,000 kWh/year and reduced CO₂ emissions by 1.5 million kg annually. The PV array demonstrated a maximum power point tracking (MPPT) efficiency of 93.6%, and the PEM fuel cell achieved an efficiency of 65%. The fuzzy logic EMS effectively optimized energy flow, ensuring reliable power supply without frequent reliance on grid power. These findings highlight the potential of hybrid renewable energy systems for enhancing energy resilience and sustainability in energy-intensive facilities.
Waste Classification Using NasNet-Mobile: A Multi-Stage Deep Learning Approach for Environmental Sustainability Yoong, Hui Ching; Jong, Siat Ling; Tay, Kim Gaik
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.v13i4.7198

Abstract

Improper waste management remains a significant global challenge, resulting in severe environmental and health impacts. Existing classification systems were designed and studied on large deep learning models, which are computationally expensive and not well-suited for embedded systems. To overcome this challenge, this study introduces a lightweight NasNet Mobile architecture that was trained using a three-stage learning framework. The framework employs transfer learning, fine-tuning, and hyperparameter optimisation to improve the model’s performance and generalisation capabilities progressively. To validate the proposed approach, experimental evaluations were conducted on TrashNet and Garbage Classification datasets. The model achieved an accuracy of 91.25% on the TrashNet dataset and 94.85% on the Garbage Classification dataset using the optimal hyperparameter set obtained through the random search technique. These results indicate that the proposed strategy effectively adapts to varying data distributions and outperforms popular Convolutional Neural Network (CNN) architectures, such as VGG-16, ResNet, AlexNet, etc. Therefore, the proposed model provides a reliable foundation for developing scalable and efficient waste classification systems for environmental applications. This study contributes to a practical deep learning approach that improves classification performance while maintaining low resource requirements for sustainable waste management.
VENTRICULAR TACHYCARDIA PREDICTION THROUGH DEEP LEARNING: ENHANCING CARDIAC MONITORING Ravindran, Rekha; Bharathi, R.; Philip, Khil Mathew; Bhavana, J.; Venkatesh, N.; Kusumanchi, T. P. S. Kumar; B, Jegajothi
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.v13i4.6603

Abstract

Ventricular Tachycardia (VT) is a life threatening arrhythmia that needs to be detected early and correctly to avoid cardiac arrest. In this paper, the authors hypothesise a hybrid deep learning model based on WaveNet, Swin Transformer, and MISH activation function to make powerful predictions of VTs on the basis of ECG signals in the MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB). The preprocessing pipeline will consist of wavelet-based denoising, min-max normalization and HRV feature. WaveNet has the ability to capture short-term temporal variations whilst the Swin Transformer considers global relations via hierarchical attention. The suggested approach has excellent performance over baseline models, having accuracy, precision, recall, and F1-score of 97.57, 96.89, 97.42, and 97.15, respectively. The improved capability of the model to detect VT with a low number of false negative results shows that the model could be used in realtime cardiac monitoring and clinical decision support. The next steps to be considered in the future research will be the model optimization of wearable devices and testing on multi-center ECG data.
Optimization of Cement Distribution Route Based on Hybrid Genetic-Firefly Algorithm (GAFA) Heryati, Agustina; Stiawan, Deris; Setiawan, Heri; Rini, Dian Palupi; Budiarto, Rahmat
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.v13i4.6404

Abstract

This study focuses on optimizing the cement distribution route to improve efficiency, reduce costs, and minimize environmental impacts. A hybrid Genetic-Firefly Algorithm (GAFA) approach, integrating the Genetic Algorithm (GA) and the Firefly Algorithm (FA), is developed to solve the complex problem of determining the optimal distribution route to ensure timely, efficient, and sustainable delivery. The Data from PT Semen Baturaja includes three factory locations and 128 distributor points. Various parameter configurations are tested, including population size, mutation probability, total execution time, average execution time, standard deviation of execution time, best factory, and best distance to provide their impact on algorithm performance. The empirical results show that the optimal configuration produces the lowest total distance of 205.14 kilometers and high executiontime efficiency. The best route covers 128 strategic distribution points in the Sumatra region. These results prove the effectiveness of the hybrid GAFA algorithm in optimizing cement distribution routes, contributing significantly to operational efficiency and transportation cost savings. Thus, this approach offers a practical, efficient solution for optimizing cement distribution routes in the manufacturing industry.
Conversational Assistant with Large Language Model Agent for Natural Interaction in Home Automation Environments Yauri, Ricardo; Espino, Rafael
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.v13i4.7028

Abstract

Digital transformation has driven the development of smart home automation systems for healthcare and remote assistance, which can integrate large language models, foundational models, and Retrieval-Augmented Generation techniques to design conversational assistants accessible to people with reduced mobility. In these cases, it is necessary to overcome usability barriers to control home devices because traditional home automation interfaces are not adapted to their specific needs. Previous research has identified systems such as voice control, gestures, and conversational agents based on generative artificial intelligence to facilitate assisted interaction in the home through home automation systems. This article describes a conversational agent, aimed at people with reduced mobility, to facilitate natural interaction with home automation environments, using technologies such as OpenAI, Langchain, and LlamaIndex. The results demonstrate the successful deployment of a web-based telecare platform based on conversational agents capable of retrieving technical documents through embedding, generating SQL queries, and MQTT topics for integration into real-world monitoring and assistance environments for people with reduced mobility.
Sliding Mode Based Nonlinear Control of a Three-Level NPC-Type DC-DC Bidirectional Converter for HV Applications Ngoune, Jean-Paul; Etouke, Paul Owoundi; Ekemb, Gabriel; Cham, Julius D.; Boum, Alexandre T.
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.v13i4.6895

Abstract

This paper proposes the design and a comparative study of three sliding modebased controllers—sliding mode control (SMC), fuzzy logic sliding mode control (Fuzzy-SMC), and ANFIS sliding mode control (Anfis-SMC)— applied to a newly designed DC-DC isolated bidirectional converter featuring a three-level neutral point clamped (3L-NPC) topology. Utilizing single phase shift (SPS) modulation enabled by a simplified analytical model, the controllers demonstrate strong robustness against load disturbances and input voltage variations, while ensuring accurate reference voltage tracking in both Boost and Buck modes. Comparative analysis shows that all three controllers outperform the traditional PI controller across most performance metrics. Voltage balancing is effectively maintained through an auxiliary inductive circuit. The investigated control schemes are validated via MATLABSimulink simulations, confirming their suitability for the efficient control of the studied converter.
Enhanced Dung Beetle Optimization Algorithm Combined Harris Hawks Optimizer and Nelder Mead Simplex Method for Solar-Connected Smart-grid Application Vy, Huynh Tuyet; Anh, Ho Pham Huy
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.v13i4.5939

Abstract

The Dung Beetle Optimization algorithm (DBO) is a swarm-based intelligence algorithm with competitive performance against other popular optimization algorithms. However, its process is often fallen in local optimum, insufficient accuracy, and slow convergence speed due to a lack of combination or collaboration between search agents. This research proposes an advanced DBO approach combining the Harris Hawks Optimizer (HHO) and Nelder Mead method to improve slow convergence speed, insufficient accuracy, and premature convergence. The Nelder Mead method is used in the subpopulation of ball-rolling to reduce the probability of falling in local optimum, along with “seven kills” strategy of HHO method that is combined in the former iterations of the DBO algorithm to enhance its global search capacity and convergence speed. The performance of the proposed enhanced dung beetle optimization (EDBO) algorithm is evaluated via 30 CEC-2017 benchmark functions and compared with several representative meta-heuristic algorithms, including the original DBO and HHO, as well as three recently proposed methods: RUN, SMA, and AO. The result shows that EDBO consistently achieves superior performance over most of the C-test functions in terms of solution quality and robustness. Additionally, when applied to the optimization of the operating cost of a solar-connected residential power system, the proposed EDBO attains the best or highly competitive global optimum compared with the competing algorithms.
Robust Stereo Matching for Driver Assistance Systems Under Adverse Driving Conditions Nguyen, Vinh Dinh; Tran, Nhan Huu
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/.v13i4.6686

Abstract

Deep stereo networks perform effectively when both training and testing data come from the same domain. However, their accuracy tends to drop significantly in efficiency-focused target scenarios due to domain shifts between training and testing datasets. These shifts often arise from differences in factors such as color, lighting, contrast, and texture. Additionally, the architecture of deep networks generally results in processing times that are unsuitable for real-time applications. To address these issues, this paper proposes a lightweight and robust stereo matching approach tailored for diverse driving environments. It leverages attention mechanisms for feature extraction and uses evolutionary algorithms for optimizing parameters. The method outperforms existing deep learning and traditional stereo matching techniques in terms of both processing speed and the percentage of bad pixels, as demonstrated on three challenging outdoor datasets: KITTI, HCI, and Driving Stereo. These results indicate that the proposed solution is highly effective for real-world applications where both precision and flexibility are essential.
Secure Medical Image Transmission via CNN-Derived Keys and Chaotic DNA Encoding Bentouila, Sara; Faraoun, Kamel Mohamed
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.v13i4.7505

Abstract

This paper presents a secure and efficient hybrid encryption framework designed for medical image protection. The method combines a fine-tuned MobileNetV3 network for content-adaptive key generation with the nonlinear dynamics of a Lu chaotic system and a DNA-based Cipher Feedback (CFB) diffusion stage. The proposed approach eliminates the arbitrary selection of chaotic maps commonly found in existing methods by dynamically adapting to image content. Experimental tests conducted on brain MRI images demonstrate strong security and robustness, achieving an entropy of 7.9998, NPCR of 99.58%, UACI of 29.93%, PSNR of 7.43 dB, and an average encryption time of 0.24 s. These results confirm excellent randomness, high key sensitivity, and real-time processing capability. The proposed model outperforms recent chaotic and hybrid schemes, making it suitable for secure medical image transmission and telemedicine applications.
Design of Compact, Low-cost Electrochemical Electronic Reader (Potentiostat) for Chemical Compound Analysis Harimurti, Suksmandhira; Gamalyn, Irene Maria; Johan, Tengku Abdurrazak; Septian, M. Rivaldi Ali; Anshori, Isa; Estananto, Estananto
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.v13i4.7027

Abstract

The development of low-cost electrochemical electronic reader (potentiostat) has been vastly growing recently since it could serve as a rapid chemical compound detection. Nevertheless, the realization of a low-cost potentiostat, having a compact size and providing multi-feature, is challenging. Here, a compact, low-cost potentiostat, supporting multi electrochemical methods was demonstrated. The total dimension was (10.8 x 4.6 x 3.5) cm3 with the weight of only 75.6 g. By using 16-bit analog-to-digital converter (ADC) and 12-bit digital-to-analog converter (DAC) components, the potentiostat facilitated a current input range of ±580 µA with a resolution of 23.5 nA and a voltage sweep range of ±1.5 V with a resolution of 800 µV. For the electrochemical measurement, it supported cyclic voltammetry (CV) and differential pulse voltammetry (DPV) methods. Furthermore, in comparison with a commercial potentiostat (Sensit Smart), the potentiostat showcased a good accuracy performance. In detail, the average relative accuracy of the CV method test was 91% and 95% for the anodic (Ianodic) and cathodic peak currents (Icathodic), respectively. For DPV method, the lowest relative accuracy of the peak current (Ipeak) was still 83%. These results demonstrated that our potentiostat could promisingly be utilized for chemical compound detection in low-cost setting or rural areas.

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