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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.
Arjuna Subject : -
Articles 73 Documents
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Search results for , issue "Vol 14, No 4: August 2025" : 73 Documents clear
Blockchain for future smart grid: a comprehensive survey Shamaseen, Ala’a; Qatawneh, Mohammad; Elshqeirat, Basima
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.v14i4.8956

Abstract

Due to the unique features and characteristics of blockchain technology, its applications have expanded across various sectors, including finance, banking, supply chains, and smart grids (SGs). Blockchain ensures security and trust in transactions without requiring a third party, making it particularly valuable in decentralized systems. This paper explores the integration of blockchain technology into SG systems. It begins with a comprehensive review of conventional and smart power grids, identifying the key challenges modern SGs face, particularly issues related to trust and fraud. An in-depth analysis of blockchain technology follows, highlighting its potential, advantages, and defining characteristics. The study then examines several blockchain-based SG applications and provides a comparative analysis of prior research. The findings of this review illuminate the critical role of blockchain in enhancing SG performance by addressing trust and fraud prevention challenges. Furthermore, this research has significant implications for the energy sector, as it underscores the potential of blockchain to revolutionize SGs through increased security, transparency, and efficiency. By providing a foundation for future studies, this paper aims to guide the development of unified blockchain frameworks that address scalability, privacy, and energy management, paving the way for a more secure and efficient decentralized energy system
An enhanced key schedule mechanism to improve the security strength of the data encryption standard algorithm Zeleke Mekonen, Mareye; Kumar Napa, Komal; Andulalem Ayalew, Amogne; Manivannan, Bommy; Suresh, Tamilarasi; Senthil Murugan, Janakiraman; Admassu Assegie, Tsehay
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.v14i4.9712

Abstract

The rapid growth of internet accessibility requires strong data security measures, mainly for safeguarding sensitive information. Since many threats and attacks steal our private data. Data encryption standard (DES) is one of the cryptographic methods that uses a symmetric key encryption method to resist various types of cryptographic attacks. This work proposes an improved key scheduling algorithm (KSA) to enhance DES security. The modified KSA is evaluated using criteria such as frequency test, hamming weight, and bit difference to measure round key randomness and resilience. Moreover, the avalanche effect is evaluated to assess the diffusion and confusion character of the generated ciphertext. The final result indicates that the enhanced KSA attains better frequency distribution (0.89-1.0), increased hamming weight consistency (97.13%), and high bit transition rates compared to the original DES KSA. These enhancements demonstrate increased randomness and complexity, making the algorithm more resistant to brute-force and other cryptographic attacks. Our proposed work shows enhanced security capabilities, albeit with increased computational requirements, and establishes a foundation for future improvement in symmetric key cryptography.
Optimized convolutional neural network enabled technique for sentiment analysis from social media data Veena, Chinta; Sultanpure, Kavita A.; Meenakshi, Meenakshi; Bangare, Sunil L.; Raskar, Punam Sunil; Sadashiv Kulkarni, Shriram; Arcinas, Myla M.; Rane, Kantilal Pitambar
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.v14i4.7712

Abstract

Sentiment analysis is an area of computational linguistics that studies natural language processing. The most significant subtasks are gathering people's thoughts and organizing them into groups to determine how they feel. The primary purpose of sentiment analysis is to determine whether the individual who created a piece of material has a positive or negative opinion about a subject. It has been claimed that sentiment analysis and social media mining have contributed to the recent success of both private sector and the government. Emotional analysis has applications in practically every aspect of modern life, from individuals to corporations, telecommunications to medical, and economics to politics. This article describes an improved sentiment analysis model based on gray level co-occurrence matrix (GLCM) texture feature extraction and a convolutional neural network (CNN). This model was created using tweets. First, texture characteristics are extracted from the input data set using the GLCM technique. This feature extraction improves categorization accuracy. CNNs are used to classify objects. It outperforms both the support vector machine and the AdaBoost algorithms in terms of accuracy. CNN has achieved an accuracy of 98.5% for sentiment analysis task.
Multi-objective optimization for algorithmic trading in the Vietnamese stock market Nguyen, Trung Duc; Nguyen, Nhat Minh; Tran, Minh
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.v14i4.9288

Abstract

This study aims to optimize algorithmic trading strategies using the relative strength index (RSI) and the moving average convergence divergence (MACD) indicators in the Vietnamese stock market. An automated trading system is constructed to optimize indicator parameters using multi-objective particle swarm optimization (PSO) over three objective functions: total return, win rate, and number of trades. The system employs simultaneous optimization of parameters and signal aggregation for developing the optimal selection strategy. Based on daily Vietnam index data from 2018 to 2024, the results show that the PSO method surpasses the differential evolution (DE) method in both returns and execution time. Additionally, the optimal selection strategy achieves superior performance compared to benchmark strategies. It also demonstrates the ability to adapt to the preferences of traders by selecting appropriate indicators. Traders can use the MACD indicator to seek higher profits, while the RSI indicator is more suitable for minimizing transaction costs in a volatile market.
A lightweight convolutional neural network for rice leaf disease detection integrated in an Android application Hartono, Rudi; Yoeseph, Nanang Maulana; Purnomo, Fendi Aji; Bawono, Sahirul Alim Tri; Purnomo, Agus
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.v14i4.9260

Abstract

More than two-thirds of the world's population rely on rice or wheat as staple foods, which are grown in various Asian countries. Diseases affecting rice leaves can disrupt growth, reduce yields, and cause famine in some areas. Therefore, a quick and accurate recognition method is necessary to minimize losses. This article focuses on eight types of rice leaf diseases using data consisting of approximately 110 images for each disease type, with enhanced image quality to achieve better results. The study applies a convolutional neural network (CNN) model integrated into an Android mobile application, achieving a training accuracy of 86.56% and a validation accuracy of 93.75%. Comparative experiments demonstrate that the model can be effectively implemented in mobile applications for accurately detecting rice leaf diseases, providing a reliable solution for field detection. This method not only helps farmers identify diseases more quickly but also has the potential to reduce crop losses caused by leaf diseases.
Arecanut grading classification based on representational deep neural network with support vector machine K. M., Satheesha; P. R. Nayak, Jithendra; G. S., Rajanna
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.v14i4.9323

Abstract

The grading of arecanuts before their sale is significant for enhancing profitability. The assessment of areca nut quality widely utilizes and respects both producer-level and wholesale dealer-level grading methods. This study proposes an advanced grading framework for white Chali-type arecanuts by developing a standardized image database and utilizing deep learning-based feature extraction. This research presents a novel approach by combining a representational deep neural network (ResNet) for automatic feature extraction with various spectral analysis methods, such as the Fourier transform and wavelet transform, to capture frequency-domain features. The support vector machine (SVM) model classifies these extracted features. The proposed system achieves an accuracy of 97.8%, which is significantly better than existing methods SVM with 72.5%, convolutional neural network (CNN) with 92.9%, AlexNet with 90.6%, and VGG19 with 90.2%. The results show that the proposed hybrid ResNet-SVM method improves accuracy, precision, recall, and F1-score, making it a more reliable and automated way to grade areca nuts. This method thus enhances efficiency, reduces manual effort, and ensures consistent quality assessment.
Analysis of unmanned aerial vehicle airframe materials on circularly polarized antenna radiation characteristics Wahyudi, Wahyudi; Setyadewi, Imas Tri; Sakti, Mohammad Amanta Kumala; Prabowo, Yanuar; Hadiyanti, Donatina Miswati; Rahayu, Novelita; Muzayadah, Nurul Lailatul; Wahyudi, Agus Hendra; Guno, Yomi; Praludi, Teguh; Santosa, Cahya Edi; Sumantyo, Josaphat Tetuko Sri
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.v14i4.8010

Abstract

This paper presents an experimental examination of how unmanned aerial vehicle (UAV) airframe materials affect the electromagnetic characteristics of the airborne circularly polarized (CP) payload antenna. This study specifically investigates the received signal from the circularly polarized synthetic aperture radar (CP-SAR) antenna installed within the fuselage of the lapan surveillance UAV (LSU). In the airborne CP-SAR experiment, broadband CP microstrip subarray antennas were used along with LSU series airframe material composites comprising E-glass EW-185 and Carbon C522 Twill. The composite specimens were prepared to have the same size and thickness to minimize variability in the comparative analysis. The experimental study measures the transmission loss using S-parameters. At 5.3 GHz, the E-glass EW-185 fiber composite exhibits a material attenuation of -1.5 dB and a circular depolarization of 0.32 dB. The E-glass EW-185 fiber composite exhibits a material attenuation of -1.5 dB and a circular depolarization of 0.32 dB. In contrast, the Carbon C522 Twill fiber composite demonstrates a significantly higher material attenuation of -31.24 dB and a circular depolarization of 10.70 dB. Additionally, this paper examines the radiation pattern measurements of the CP-SAR antenna at various frequencies, providing a comprehensive analysis of the materials' impact on antenna performance.
A comparative study of machine learning methods for drug type classification Tejawati, Andi; Suprihanto, Didit; Ery Burhandenny, Aji; Saipul, Saipul; Puspitasari, Novianti; Septiarini, Anindita
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.v14i4.9477

Abstract

Drugs, commonly called narcotics, are dangerous substances that, if consumed excessively, can result in addiction and even death. Drug abuse in Indonesia has reached a concerning stage. In 2017, the National Narcotics Agency detected 46,537 drug-related incidents, including methamphetamine, marijuana, and ecstasy. There are 4 types of substances that can affect drug users, such as hallucinogens, depressants, opioids, and stimulants. A machine learning approach can detect these substances using user symptom data as input. This study uses six different methods in classifying, including decision tree, C.45, K-nearest neighbor (KNN), random forest, and support vector machine (SVM). The dataset comprises 144 data and 21 attributes based on the user's symptoms. The evaluation method in this study uses cross-validation with K-fold values of 5 and 10 and uses three parameters: precision, recall, and accuracy. KNN yields the most optimal results by using K=1 and K-fold 10 in the Euclidean and Minkowski types. The model achieves precision, recall, and accuracy of 91.9%, 91.7%, and 91.67%, respectively.
Optimizing cloud infrastructure efficiency through advanced multimedia data deduplication techniques Mohiuddin, Mohd Hasan; Tamilselvan, Latha
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.v14i4.9292

Abstract

Organizations worldwide commonly utilize cloud infrastructure to manage large volumes of data, making the optimization of storage crucial for enhancing cloud performance. One effective optimization technique is data deduplication, which identifies duplicate objects and ensures that only one copy of unique data is stored in the cloud. While several deduplication schemes currently exist, there is a pressing need to improve efficiency in cloud storage through innovative approaches. In this paper, we propose a new system model designed to facilitate an efficient deduplication process. Our algorithm, called deduplication in cloud infrastructure (DCI), offers a systematic and effective method for handling deduplication challenges related to redundant data storage. DCI focuses on hash generation, metadata comparison, and pointer-based deduplication, providing a comprehensive strategy for optimizing cloud storage resources and minimizing duplication. This ultimately enhances both the efficiency and cost-effectiveness of cloud-based data management. A simulation study using CloudSim and the Hadoop distributed file system (HDFS) simulator demonstrates that the proposed deduplication method is effective. Experimental results show that our algorithm outperforms many existing solutions, achieving the highest deduplication ratio of 6.7 and saving 85.09% of storage space due to its efficient deduplication approach. The proposed system can be used in cloud infrastructures for efficiency.
Development of water quality monitoring system for fish farming B. Papolonias, Juffil; Q. Lavilles, Rabby; I. Miano, Joel
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.v14i4.7673

Abstract

Tilapia fish farming faces growing challenges from climate variability, environmental degradation, and the urgent demand for sustainable food production. However, traditional water quality monitoring methods remain manual and reactive, often resulting in compromised fish health and reduced farm productivity. Addressing this need, this study designed and developed a water quality monitoring system utilizing the internet of things (IoT) and embedded systems to enable real-time, proactive management. Guided by the software development life cycle (SDLC), the methodology focused on planning and analysis, system design and development, and testing and evaluation. The system integrates key water quality sensors, including pH, temperature, dissolved oxygen (DO), and electrical conductivity (EC), identified as critical parameters affecting tilapia health. These sensors were interfaced with Arduino Nano and ESP32 Dev Kit microcontrollers, forming the sensing layer of the system. Sensor data were transmitted to the ThingSpeak IoT platform for real-time visualization and storage. Validation results revealed a low mean absolute percentage error (MAPE), indicating an acceptable sensor performance. User evaluation, based on the technology acceptance model (TAM), indicated that the system was perceived as useful, user-friendly, and valuable for aquaculture management. Overall, the system enables real-time water quality monitoring, supporting a more responsive and sustainable environment for tilapia fish farming.

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