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 20 Documents
 1   2 
Search results for , issue "Vol 13, No 4: December 2025" : 20 Documents clear
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.
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 2 of 2 | Total Record : 20

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue 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