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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 49 Documents
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Search results for , issue "Vol 40, No 3: December 2025" : 49 Documents clear
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.
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.
Enhancing sales volume using machine learning algorithms Elsayed Aboutabl, Amal; Mahmoud Moawad, Ola; Mohamed Abd-Elwahab, 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.pp1618-1629

Abstract

In today's highly competitive business landscape, companies face a significant challenge in making accurate decisions based on vast amounts of historical data. Reliance on human data analysis often leads to biases and errors, hindering the ability to extract effective insights for sales forecasting. To address this challenge, this research presents an advanced model that integrates 14 machine learning (ML) regression algorithms, including XGBRegressor and LGBMRegressor, to provide accurate sales predictions using a comprehensive global store dataset. The results demonstrate that XGBRegressor and LGBMRegressor achieved the highest test accuracy (92%) and the lowest error rates, proving their ability to handle complex prediction tasks efficiently. This high accuracy in sales forecasting enables companies to make more effective strategic decisions, such as optimizing inventory management, allocating resources optimally, and exploring new growth opportunities. Consequently, the use of these advanced algorithms directly contributes to increasing sales volume and achieving a sustainable competitive advantage.
A TOT: tri-optimized-tariff based strategic residential load management with greedy optimization in IEEE33-bus system: a case study with renewable energy penetration Goswami, Kuheli; Kumar Sil, Arindam
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.pp1199-1211

Abstract

The efficiency of a load management system in terms of its energy performance index (EPI) depends on its capacity to enhance the reliability, resilience, and cost effectiveness of the existing system. Artificial intelligence (AI) is crucial in this shift from classical to AI-based power system planning, optimizing renewable energy (RE) and reducing gridstress. On the other hand, proper placement of resources is essential to achieve benefits and reduce transmission losses. Utility sectors of different states has revealed that in certain areas amongst different type of loads, domestic loads accounts for a substantial proportion of energy consumption. Therefore, the present work deals with optimum load scheduling, integration of RE, energy storage (ES) and proposed tri-optimized-tariff (TOT) for prosumers. We have found that the weighted-K-nearest-neighbor (KNN) method excels in selecting features for household appliances and ES scheduling. The composite greedy optimization (CGO) technique outperforms existing methods in optimization. These results demonstrate the efficiency and real-world potential of our model. We have conducted a case study and developed an AI-based strategic-residential-load-managementsystem (SRLMS), which we have tested on the IEEE33 bus system, showing cost effectiveness and improved EPI for prosumers. This work encourages the development of a harmonious relationship between utility-sectors and prosumers.
Fuzzy medical expert system for prediction of prostate cancer Wantoro, Agus; Rusliyawati, Rusliyawati; Sutyarso, Sutyarso; Hadibrata, Exsa
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.pp1466-1477

Abstract

We developed the fuzzy medical expert system (F-MES) based on fuzzy inference system (FIS) Mamdani using a different approach to prostate cancer risk (PCR) prediction. The difference in our research is that we modify the membership function on the input variable according to medical standards. We used the same input variables as the previous study, namely age, prostate-specific antigen (PSA), prostate volume (PV), and percentage (%) free PSA (%FPSA). The data on the input variable is used as input into F-MES and displays the output in the form of a percentage (%) of PCR. If the PCR is >50%, then the patient is advised to undergo a biopsy test. We conducted an analysis with the doctor to create a simple domain and rule base of 24 rules. Our number of rules is lower than previous studies of 80 and 240, but our prediction results are better the F-MES evaluation used the same 56 patients, that the F-MES we developed had an accuracy of 857%. This score is better than previous studies of 75% and 76%. Our F-MES is simple but effective and can be used as a supporting tool in decision-making in medical diagnosis.
A hybrid DWT-DCT-SVD watermarking scheme using arnold transform Huynh, Van-Thanh; Nguyen, Thai-Son; Vo, Thanh-C
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.pp1659-1668

Abstract

In telemedicine, medical images and electronic patient records (EPRs) are frequently transmitted and stored, making them vulnerable to tampering and theft. To ensure data security and copyright protection, this paper proposes a hybrid watermarking scheme based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD). The method uses a two-level DWT to decompose the image, applies DCT to selected sub-bands, and embeds two watermarks. The first is a logo used for ownership verification, and the second is an EPR encrypted with the Arnold transform for privacy protection. SVD is then used to enhance robustness. Experimental results show that the proposed scheme achieves better image quality and stronger resistance to common attacks compared with existing watermarking methods.
Low complexity blind selective mapping in orthogonal frequency division multiplexing: utilizing linear combination Abdul Wahab, Aeizaal Azman; Muhammad Adnan, Nur Qamarina; Mohamad Hamzah, Firdaus
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.pp1698-1706

Abstract

Orthogonal frequency division multiplexing (OFDM) is a cornerstone in wireless communications for its spectral efficiency and robustness against multipath fading. However, its deployment is constrained by the high peakto-average power ratio (PAPR), which demands complex power amplifiers and increases system costs. Selective mapping (SLM) is a popular distortion less method for PAPR reduction but suffers from high computational complexity and data rate losses due to side information (SI) transmission. This paper proposes a low-complexity, blind SLM method utilizing linear combination, which reduces computational complexity by generating alternative candidate signals without additional inverse fast fourier transform (IFFT) operations. A maximum likelihood estimation (MLE)-based blind receiver recovers transmitted signals without SI, preserving data rate integrity. The proposed method achieves comparable PAPR and bit error rate (BER) performance to conventional SLM (C-SLM) while significantly reducing computational operations. Simulations demonstrate the efficiency of the method across various configurations, making it a strong candidate for next-generation communication systems like 5G and beyond.
FEM-based analysis of the relationship between track insulation conductivity and stray current in DC traction systems Aussawamaykin, Apiwat; Pao-la-or, Padej
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.pp1212-1220

Abstract

This research investigates the influence of track insulation conductivity on stray current in direct current (DC) traction systems, which is a significant issue in railway operations due to its potential to cause electrochemical corrosion. Utilizing the finite element method (FEM), a simplified geometric model of a DC tram traction system was analyzed under varying conditions of track insulation conductivity. The study examined three levels of insulation conductivity, represented by fastener resistances of 1,000 Ω, 3,000 Ω, and 6,000 Ω, to understand their impact on stray current density. Results revealed that increased insulation resistance leads to reduced stray current density, demonstrating the critical role of track insulation in mitigating stray currents. The study further highlights that the depth of soil beneath the track also significantly affects stray current distribution. These findings provide insights into improving track design and maintenance for better protection against the negative effects of stray current in DC traction systems.

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