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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
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Articles 73 Documents
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Search results for , issue "Vol 14, No 4: August 2025" : 73 Documents clear
Non-fungible token modeling: the enthusiasm of music fans for the digital-collectible revolution Zahra Nurbana, Khadijah; Sudarmilah, Endah
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9269

Abstract

Blockchain technology has become a major focus in data security and reliability. A foundation for innovations such as non-fungible token (NFT), which opens up new opportunities in managing ownership of digital assets. We investigate NFTs in the form of voice, which is digital audio communication. During the COVID-19 pandemic, podcasts have been rampant, creating new business opportunities in digital media such as NFTs, which have explored and evolved in various markets; voice content has gained significant space in sales, promotion, and dissemination/innovation. This research presents a comprehensive analysis of NFTs from 2019 to 2022, focusing on the variable association consisting of the NFT category, the price of each of those NFT categories, NFT editions, and NFT marketplace. We used structural equation modeling (SEM) to clarify the relationship in partial least squares structural equation modeling (PLS-SEM). This study’s findings suggest that music enthusiasts seek NFTs based on the NFT category. Therefore, it is crucial for NFT creators, who are musicians too, to exercise caution when choosing the NFT category that is most popular among music enthusiasts. We suggest that the musicians creating NFTs should consider establishing appealing NFT categories to attract music fans and other collectors.
Enhanced autonomous water garbage collection system using deep learning-based object detection and path planning Dubey, Parul; Bhagat, Titiksha Tulsidas; Kokare, Abhijeet; Dubey, Pushkar; Chaudhari, Poonam Ramesh; Raut, Umesh
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9399

Abstract

Water pollution, particularly from floating debris such as plastics, has become a critical environmental issue, threatening aquatic ecosystems and biodiversity. Autonomous solutions for the detection and removal of waste are increasingly essential for maintaining water cleanliness and mitigating pollution. However, existing systems face limitations in real-time detection, accuracy, and adaptability to diverse aquatic environments. This paper utilizes the water pollution images dataset, comprising almost 300 high-resolution images from lakes, rivers, and coastal areas, representing various types of floating waste under different environmental conditions. In response to these challenges, this paper introduces an autonomous unmanned surface vehicle (USV) system equipped with the enhanced waste detection network (EWD-Net). EWD-Net improves upon traditional single-shot detection algorithms by integrating deeper feature extraction layers and enhancing computational efficiency, resulting in higher accuracy and faster detection. Additionally, the system includes the dynamic path optimization (DPO) module for efficient navigation and obstacle avoidance in complex water environments. The novelty of this system lies in its dual approach, combining advanced detection with optimized path planning, ensuring effective autonomous operation. The results indicate that the proposed model achieves an accuracy of 94.6%, outperforming existing algorithms and providing a robust solution for real-time waste detection and collection.
Multi-source assisted mixed simultaneous wireless information and power transfer for energy efficient routing in IoT networks Nagelli, Prasad; Nagavelli, Ramana
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8276

Abstract

Recently, simultaneous wireless information and power transfer (SWIPT) emerged as the best solution for resource-constrained internet of things (IoT) networks. SWIPT ensures the provision of parallel information and power transfer in the network. Under the SWIPT model, many researchers use two well-established protocols: time switching (TS) and power splitting (PS). TS is better than PS when the signal is weak but inserts an extra delay because energy harvesting (EH) and information decoding (ID) happened two different times. However, PS protocol performs poorly in hard situations with low signal strength even if it conducts EH and ID simultaneously. Hence, this paper proposed a new model called mixed-SWIPT (MSWIPT) which combines TS and PS protocols in an intuitive manner. Further, this work proposes a multi-source EH mechanism in which the receiving node harvests energy from multiple sources which is different from single source, i.e., desired node’s radio frequency (RF) signal. The multiple sources include non-participated Neighbor Node’s RF signal, sink node and co-channel interference and noise. Under the routing, the node selection is formulated as maximum link capacity problems and solved through several constraints. Extensive simulations on proposed model prove the superiority in terms of EH and energy efficiency from the state-of-the-art methods.
Transport of direct current electricity: research and prospects Dzhumamukhambetov, Nasikhan; Yashkov, Vladimir; Kulzhanov, Dyussembek; Konarbayeva, Akmaral; Arstanaliyev, Essengeldi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9463

Abstract

The purpose of this study is to analyse the technical, economic, and environmental aspects of direct current electricity transport and to assess its potential in accordance with modern requirements for sustainable energy and infrastructure. The methods used include analytical method, classification, functional method, statistical method, synthesis. The study revealed that the use of direct current has the potential to increase the capacity of power transmission lines, which is especially important in a dynamically developing industrial sector. It should be noted that direct current networks have no phase shifts and no notions of static and dynamic stability, making them ideal for long-distance power transmission. As a result of the conducted research, it can be concluded that the use of direct current in technologies can increase the efficiency and reliability of energy systems, especially with an increase in consumption and load on the grid. Direct current power transmission technologies meet the requirements of sustainable energy supply, providing economic efficiency and reducing environmental impact.
Solving missing categorical data in questionnaire responses for automated classification Aekwarangkoon, Saifon; Namponwatthanakul, Thanatep; Amonwet, Adisorn; Hemtanon, Siranuch
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8785

Abstract

Handling missing categorical data is critical for maintaining the accuracy and reliability of automatic classification tasks, particularly in mental health screening based on questionnaire responses. This study investigates several imputation methods, including last observation carried forward (LOCF), k-nearest neighbor (KNN) imputation, hot-deck imputation, and multivariate imputation by chained equations (MICE). Results show that KNN imputation achieves the lowest root mean square error (RMSE), indicating the most faithful reconstruction of the original data. However, for classification performance, MICE-imputed datasets produced models that outperformed those generated by other methods and even surpassed models trained on the original incomplete data. Interestingly, we also found that using observed data over multiple iterations of imputation tuning can introduce greater deviation from original missing values, but this process can help form datasets with clearer class boundaries, ultimately improving classification accuracy. These findings emphasize the need to balance data fidelity and model performance when selecting imputation strategies.
Animate prime movers: an un-exploited resource towards achieving United Nations SDG 7-future research requirements Dastgeer, Faizan; Kalam, Akhtar; Naeem, Ayisha; Ahmad, Faraz
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8624

Abstract

Renewable energy is a prominent concept that encompasses various forms, such as solar photovoltaics, wind power, and geothermal energy. Although less familiar, animate energy resources, which include human beings and animals, may also be seen as being explored. While animal-based renewable energy generation may appear novel, different research articles, patents, and a couple of commercially available products have been developed. For the specific case of dairy farms, harnessing this resource can be coupled with appropriate exercise regimens for cows, which may lead to clean energy, animal welfare, and even potential benefits for human health. These efforts align with the sustainable development goals (SDG) of the United Nations, specifically SDG 3 and SDG 7. However, ethical concerns regarding the use of animals for energy production as well as the potential and clean nature of this resource need to be thoroughly investigated before it can be exploited on a larger scale. This research paper aims to identify deficiencies in the current relevant body of knowledge and to present requirements for future research efforts that may help tap into this resource. By exploring the potential of animate energy resources, we may contribute towards sustainable energy production while promoting animal welfare and human health.
Innovative smart showcase design for indoors and eco-friendly hydroponics Exaudi, Kemahyanto; Sembiring, Sarmayanta; Putra Perdana Prasetyo, Aditya; Stiawan, Deris; Fakhrurroja, Hanif; Budiarto, Rahmat
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8353

Abstract

Hydroponics is a unique and fascinating farming technique for producing plants and vegetables. Without having to use a large area of land, people can easily apply the technique to produce fresh and hygienic vegetables. However, the technique cannot be used in apartment environment due to the limited sunlight. Thus, this study introduces an innovative hydroponic system, called as hydroponics smart showcase system that can be implemented indoors, even in the presence of minimal sunlight, and can be monitored online by users. The proposed system consists of a net pot of 4-5 hydroponics cups with a diameter of 50 mm, air temperature and humidity sensors, water level sensors, ultraviolet (UV) lights, indicator displays, and DC fans. Experimental results show that the development of innovative hydroponics using smart showcase has succeeded in stabilizing the air in the showcase according to the specified references. Moreover, UV light intensity settings for photosynthesis can be applied remotely with duration of 24 hours.
Smart wheat agriculture: an in-depth framework for optimized crop agroanalytics utilizing internet of things Kumar Murugesan, Senthil; Janarthanan, Midhunchakkaravarthy
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9328

Abstract

Precision agriculture can be revolutionized by incorporating internet of things (IoT) technologies to maximize crop yield, especially for key crops like wheat. The creation and application of an IoT-enabled monitoring system intended especially for wheat farming is presented in this study. The system provides real-time data on important agronomic characteristics, such as soil health, temperature, humidity, and crop growth stages, by integrating a network of soil moisture sensors, weather stations, and remote sensing devices. With the help of the monitoring system, field conditions can be continuously and remotely observed, giving farmers the ability to make data-driven decisions that improve crop output and resource efficiency. The system can reduce input waste and increase output by optimizing irrigation schedules, adjusting fertilizer applications, and detecting early signs of crop stress or disease through real-time data analysis. Significant gains have been shown in production results, farm sustainability overall, and water usage efficiency in field studies carried out in different wheat-growing locations. According to the research, IoT-based monitoring systems can be extremely helpful in modernizing wheat production by offering useful information that results in more exact and environmentally friendly farming methods.
A novel MPK optimization framework for financial data analysis incorporating complexity and uncertainty management Syah, Rahmad Bayu; Elveny, Marischa; Ananda, Rana Fathinah; Nasution, Mahyuddin Khairuddin Matyuso
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9358

Abstract

In a competitive environment, the ability to scale quickly and successfully is a critical need. This research proposes a new framework using multi-objective complexity prediction model (MPK) for financial data analysis, including complexity and uncertainty management. This model integrates input, uncertainty, and output optimization functions (OOFs) (input optimization function (IOF), uncertainty optimization function (UOF), and OOF) to predict complex output values under dynamic business conditions. Model evaluation is carried out using performance metrics, namely mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R² score. The evaluation results show that this model has an MSE value of 20.112, an RMSE of 2.267, and an MAE of 2.351, reflecting a low prediction error rate and high accuracy. In addition, the R² value of 0.884259 indicates that this model is able to explain around 88.4% of the variability in the output data, indicating its ability to capture complex data patterns. Thus, the proposed MPK model is effective in predicting output values in complex business scenarios and can be applied for strategic decision-making under conditions of uncertainty.
Efficient brain cancer identification using ResNet50 and ResNet50 V2: a comparative study with a primary MRI dataset Mizanur Rahman, Md; Jahan, Israt; Das, Rana; Tasmia Alvi, Syada; Abida Anjum Era, Chowdhury; Asif Khan Akash, Atik; Sohel, Amir; Zaman, Zahura
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9142

Abstract

Primary malignant brain tumors along with central nervous system cause a significant amount of deaths every year, making brain cancer a major worldwide health problem. In South Asian countries, the number of patients suffering from brain cancer is steadily rising. Treatment effectiveness and improved patient outcomes depend on early detection. Using a dataset consisting of 6056 original raw MRI scans, this study evaluates how well convolutional neural networks (CNNs) diagnose brain cancer. We present ResNet50 and ResNet50V2 models assessed for their effectiveness in identifying brain cancers. Transfer learning and fine-tuning were employed to enhance model performance. The models demonstrated strong performance, with 87-99% accuracy rate. ResNet50V2 achieved the highest 99% accuracy. To detect tumor early, this work emphasizes how well the CNN-based machine learning methods help as timely intercession and patient care is necessary. Early prediction with 100% confidence and reliable precision is a critical issue in the modern world. Our goal is to use advanced algorithms to forecast images affected by cancer. Lastly, we will deploy an automated system that will enable us to confidently identify images affected by cancer. Our suggested methodology and its application could significantly impact the field of medical science by combining computer vision and health informatics.

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