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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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Applied differential comparative study of VANET simulators: TrAD protocol study using veins and VNS VANET simulators in both real and standard city maps Sedjelmaci, Amina; Benosman, Hayet; Abdul Rahuman, Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1357-1367

Abstract

This study presents a comprehensive evaluation of vehicular ad-hoc networks (VANETs) by analysing the performance of two leading simulation frameworks: VEINS and VNS. With the increasing demand for efficient vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, understanding the capabilities of data dissemination protocols is crucial for enhancing traffic safety and optimizing route management. We investigate the traffic adaptive data (TrAD) protocol, which dynamically adapts to real-time traffic conditions to ensure reliable communication in high-density vehicular scenarios. Simulations were conducted using OMNeT++ with VEINS and NS-3 with VNS across urban environments in Manhattan and Tlemcen, evaluating TrAD’s effectiveness under diverse traffic conditions. The findings offer valuable insights into the operational strengths of the two simulation frameworks and their implications for advancing vehicular communication systems. This work contributes to the development of robust VANET protocols, supporting innovations in smart and sustainable transportation systems.
Optimal thermo-QoS-aware routing protocol for WBAN communication Bedi, Pardeep; Das, Sanjoy; Goyal, S. B.; Kumar, Manoj; Gupta, Sunil
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp270-282

Abstract

Wireless body area network (WBAN) has emerged as a promising solution to address problems such as population aging, a lack of medical facilities, and different chronic ailments. WBANs have real-time applications, and there is an increasing demand for them. However, due to changing network structure, power supply limitations, and constrained computing capacity, energy constraints, it is difficult task to achieve quality of service (QoS). To mitigate these limitations, the paper proposed an optimal thermo-QoS aware routing protocol (OTQRP) for WBAN communication. The result was investigated in terms of temperature rise, energy consumption and delay. The paper shows better energy efficiency with respect to existing works. Finally, OTQRP feature comparison is also presented with recent research in terms of features such as complexity, latency, and energy economy and observed that OTQRP shows best performance as compared to others.
The acceptance and adoption of technology on government environment: a bibliometric analysis Hildawati, Hildawati; Wallang, Muslimin; Khairri Shariffuddin, Mohd Dino
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1586-1597

Abstract

This study examines technology acceptance and adoption in government, particularly in the context of public service delivery, through a bibliometric analysis conducted using VOSviewer. The analysis aims to identify key research trends, thematic relationships, and emerging patterns in the field of digital governance. Data were retrieved from the Scopus database, covering publications related to the acceptance and adoption of technology in government from 2020 to 2025. The network visualization results indicate that artificial intelligence (AI), digital governance, public transport, e-health, and COVID-19 are among the dominant research themes, reflecting the rapid adoption of technology in transforming public services. The cooccurrence analysis reveals strong linkages among topics such as public health, AI, blockchain, and public trust, underscoring the increasing integration of digital technologies within governance systems. Furthermore, the overlay visualization demonstrates a thematic shift from fundamental studies on acceptance factors—such as trust, security, and digital literacy— toward implementation-oriented strategies, including digital transformation, smart governance, and public service efficiency. The findings suggest that technology adoption in public service continues to expand and diversify; however, significant challenges remain, particularly concerning data security, transparency, and citizen trust. Future research should focus on exploring the application of AI, the use of blockchain for governance, and the integration of internet of things (IoT) in smart city development to support a sustainable, efficient, and citizen-centric digital transformation in the public sector.
Dynamic behavior of induction machines in ATP-EMTP with space harmonics Aller, Jose Manuel; Guevara, Ruben Nicolas; Pulla, Bryam Steven
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp3-17

Abstract

This work develops a space-vector model of a squirrel-cage induction machine that incorporates the effects of spatial harmonics arising from the winding distribution. The modeling approach includes the first, fifth, and seventh spatial harmonics, which are the components with the greatest influence on the machine’s magnetic field. Simulation results highlight the impact of these harmonics on the stator and rotor currents, the electromagnetic torque, and the machine’s speed. To build the model, the voltage behind reactances (VBR) technique is employed, enabling a hybrid strategy that combines circuit-based modeling tools—such as ATP-EMTP—with computational programming in models to complement the solution of the differential equations governing the behavior of the electromechanical system. This methodology effectively transforms the induction machine into a dynamic Thevenin-equivalent circuit for each phase of the converter. ` This study provides a useful framework for evaluating how space harmonics affect the performance and operating characteristics of induction machines. The models were implemented using the ATP-EMTP software and its graphical interface, ATPDraw.
Advancing intelligent, sustainable, and secure engineering systems for future technologies Sutikno, Tole
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp1-2

Abstract

This editorial introduces Volume 41, Number 1, January 2026, of the Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), highlighting pivotal research trajectories expected to influence future progress in electrical engineering and computer science. Instead of covering all aspects of the field, this issue is structured around three strategic macroclusters: intelligent and sustainable engineering systems, AI-driven healthcare and human-centered technologies, and secure, comprehensible, and interconnected intelligent infrastructure. These themes show how artificial intelligence, sustainability, and security are coming together more and more in modern engineering applications. The editorial talks about how important intelligent energy systems, advanced control and hardware solutions, data-driven healthcare innovations, and reliable digital infrastructures are for solving global technological problems. This issue's contributions demonstrate IJEECS's dedication to publishing significant, cross-disciplinary research that bridges theory and practice. This issue of the journal makes it clear that it is a progressive platform that wants to promote smart, long-lasting, and safe technologies for the engineering systems of the future.
Transforming E-governance: the potential of blockchain in the public sector Nuryanti, Linda; Ayuningtyas, Fara; W. Sumunaringrum, Monica D.; Ruswendi, Wenwen; Srimoeljanto, Agoeng; Sutejo, Agus; Susanto, Triyono; Nurmayni, Ratna
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1517-1530

Abstract

Blockchain technology has become a transformative innovation in the digital governance landscape, offering new opportunities to enhance transparency, accountability, and citizen trust. This study offers an extensive bibliometric and thematic examination of international research on blockchain in E-governance from 2019 to 2024. Using data from the Scopus database, the analysis examines publication trends, leading countries, collaboration networks, and the intellectual structure of the field. The findings reveal that research output has grown steadily, dominated by technologically advanced nations such as China, India, and the United Kingdom. The thematic mapping identifies core clusters, including transparency, E-government, and public sector innovation, alongside emerging themes such as artificial intelligence (AI) integration, smart cities, and digital transformation. By integrating bibliometric and thematic analyses, this study offers a comprehensive understanding of how blockchain research evolves within public governance. Despite significant progress, challenges remain, particularly regarding empirical validation, governance frameworks, and regional disparities in adoption. Future research should explore a more complex roadmap for blockchain implementation in government through three interrelated dimensions: technical development, policy and regulatory frameworks, and socio-institutional adaptation. This multidimensional perspective underscores blockchain’s capacity to support secure, inclusive, and data-driven forms of digital governance.
Aspect based multimodal sentiment analysis of product reviews using deep learning techniques Padigapati, Anitha; Praveen Krishna, Anne Venkata
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1707-1719

Abstract

Sentiment analysis plays a crucial role in understanding customer opinions, particularly in product reviews. Traditional approaches primarily focus on textual data; however, with the rise of social media, incorporating multimodal data, including text and emojis, enhances sentiment analysis accuracy. This research introduces a multimodal aspect-based sentiment analysis (MABSA) framework, integrating textual and emoji representations for Samsung M21 product reviews from Flipkart. The methodology involves data preprocessing, aspect extraction, sentiment grouping, and feature extraction using deep learning (DL) techniques. Bidirectional long shortterm memory (Bi-LSTM) networks are employed for classification, leveraging Word2Vec, Emoji2Vec, and bidirectional encoder representations from transformers (BERT) embeddings. Experimental results show that BERT with Bi-LSTM outperforms Word2Vec with Bi-LSTM, achieving 95.6% accuracy in aspect prediction and 96.28% accuracy in sentiment classification. Comparative analysis with existing models highlights the superiority of the MASAT model, effectively integrating implicit aspects, emoticons, and emojis. The study demonstrates the importance of multimodal sentiment analysis for a more comprehensive understanding of user opinions, offering valuable insights for businesses to enhance customer satisfaction.
Intelligent dust monitoring and cleaning optimization on photovoltaic panels Kourtiche, Ali; Belhia, Souaad; Felici-Castell, Santiago; El Amine Said, Mohammed; Bouanani, Rania
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp409-418

Abstract

Dust deposition on photovoltaic (PV) panels is a significant operational issue, often leading to power losses exceeding 15–30% in regions with high airborne particle concentrations. Although numerous studies have investigated either visual detection of dust or analytical estimation of performance loss, most approaches focus on a single task and provide limited practical insight for real-time maintenance. This work introduces a dual-task deep learning framework that simultaneously classifies dust severity and predicts the corresponding power loss from panel images. Five recent architectures vision transformer (ViT), swin transformer, GhostNet, DenseNet, and MobileNetV2 are employed as backbone feature extractors, with extracted embeddings processed by a multi-head multi-layer perceptron (MLP) combining shared representation learning with separate classification and regression outputs. The system is trained and evaluated on a real-world dataset of PV panels, and performance is assessed using accuracy and mean absolute error. DenseNet achieves the highest accuracy (94%) and lowest prediction error, while lightweight convolutional neural network (CNN) backbones demonstrate the best balance between precision and computational efficiency. By integrating hybrid processing and dual predictive capability, the proposed method offers a more comprehensive and deployable solution for automated PV monitoring compared to existing single-output approaches.
GESS-based technical loss estimation for sustainable power networks Masdzarif, Nur Diana Izzani; Anwar Ibrahim, Khairul; Kim Gan, Chin; Hown Tee, Wei
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1187-1198

Abstract

In the pursuit of global environmental sustainability, minimizing technical losses (TL) in power distribution networks has become a key priority for utility providers. Despite numerous advancements, precise loss estimation remains a challenge due to dynamic network conditions, complex configurations, and varying parameters such as load patterns and system topology. This issue is critical, as reducing TL not only enhances distribution efficiency but also contributes to lowering greenhouse gas (GHG) emissions. This study aims to develop and demonstrate a robust method for estimating TL aligned with the global environmental sensing and sustainability (GESS) principles. The proposed approach integrates an advanced loss estimation sequence comprising peak power loss (PPL), load loss factor, and an energy flow model. It is applied to real case studies, enabling assessment of both feeder and transformer losses. Results highlight the impact of key parameters including transformer capacity factor, cable length, load factor (LF), and loss factor on overall losses. Furthermore, the method facilitates quantification of environmental and economic impacts, revealing that both carbon footprint and cost rates are highly sensitive to total energy losses. This work underscores the significance of accurate TL estimation in promoting environmentally and economically sustainable power distribution systems.
Dynamic resource allocation in cloud-radio access network using call detail record analysis Donald Hontinfinde, Régis; Sèmèvo Tognisse, Ida; Sèmèvo Tonou, Marie Mélène; Valeire Hontinfinde, Senan Ida; Abel Konnon, Miton
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1377-1390

Abstract

We propose a solution based on call detail record (CDR) data analysis for cloud-radio access network (C-RAN) network optimization. First, we propose a data traffic prediction model in 3G and 4G networks using artificial intelligence (AI) models (long short-term memory (LSTM) and Bidirectional LTSM (BiLSTM)). Second, we propose a dynamic baseband units (BBU) resource allocation algorithm based on the obtained traffic prediction results to evaluate the rate of BBUs used as well as the average utilization rate of active BBUs in a C-RAN network. We used mean absolute error, root mean square error and mean absolute percentage error to evaluate the prediction model. The results obtained show that the best performance for estimating data traffic in 3G and 4G networks was obtained with the BiLSTM model, and is as follows: 1.143; 1.521; 2.47 percent for 4G, and for 3G, we have 0.2553; 0.3608 and 27.70 percent. Finally, evaluations with the predicted traffic dataset show that our framework provides up to 81% reduction in the number of BBUs used by the normal RAN. Moreover, active BBUs are exploited on average up to 88.34% of their capacity in a C-RAN compared to an average rate of 10.8% in a traditional RAN.

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