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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
Securing patient data and access control in electronic health records with Ethereum blockchain Kumarswamy, Shruthi; Athikatte Sampigerayappa, Poornima
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.v14i4.9524

Abstract

Blockchain technology has become an essential tool for enhancing reliability and security across several industries, including the healthcare sector. In this work, we propose and implement an Ethereum-based blockchain framework to decentralize electronic health records (EHRs) at Tumakuru Siddaganga Hospital. The system establishes an append-only chain of transaction blocks that guarantees the confidentiality, auditability, and integrity of patient health records. By design, only authorized healthcare professionals can access patient data, and even then, only with the patient’s explicit consent—ensuring a privacy-preserving access model. Our approach demonstrated a 40% reduction in data access delays and eliminated unauthorized access attempts through smart contract-based access control. The decentralized nature of the framework reduces reliance on centralized databases, significantly lowering the risk of data tampering and breaches. Additionally, the implemented consensus protocol ensures that only verified transactions are recorded, maintaining consistency across distributed nodes. Compared to traditional systems, our blockchain-based solution improved the traceability of health data access events by 100%, ensuring transparency and accountability. These findings validate that blockchain technology can substantially enhance data sharing, integrity, and patient control in modern healthcare systems.
Image encryption algorithm based on a new one-dimensional chaotic map’s generator Htiti, Mohamed; Akharraz, Ismail; Ahaitouf, Abdelaziz
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.v14i4.9500

Abstract

Encryption plays a crucial role in protecting sensitive data, including communications, financial transactions, and personal information, from cyber threats. One significant area of encryption is image encryption, which ensures the privacy of visual content, such as in secure image transmission, cloud storage, and medical image processing. Recent advancements in image encryption leverage chaotic maps based on chaos theory, generating unpredictable patterns ideal for securing images. This paper presents a novel chaotic map generator that enhances the dynamics of existing chaotic maps. Based on this generator, we propose a new encryption scheme that operates on the entire input image, obscuring the relationship between the original and encrypted images while spreading pixel changes across the entire encrypted image in one step. The scheme also produces an encrypted image of a different size, making it more efficient and resilient to attacks. While some steps of the proposed system are symmetric, others are asymmetric, ensuring a higher level of security. Based on the obtained results, this approach significantly enhances both security and performance in image encryption.
Modification of grey-level co-occurrence matrix for epileptic electroencephalogram signal classification Setiawan Beu, Donny; Rizal, Achmad; Ziani, Said; Triwiyanto, Triwiyanto
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.v14i4.9105

Abstract

Texture analysis is a fundamental approach in image processing for identifying specific patterns or structures. One widely used method is the grey-level co-occurrence matrix (GLCM), which computes the frequency of pixel value pairs at certain distances and angles. This study adapts the GLCM method for 1D electroencephalogram (EEG) signal analysis, focusing on extracting features such as contrast, energy, homogeneity, correlation, and entropy. EEG signals are normalized to the range 0–255, and the extracted features are classified using a support vector machine (SVM). Experimental results show that combining features across multiple distances (d=1 to 20) achieves classification accuracy of 78.8% for five classes (Z/O/N/F/S), 94.0% for three classes (O/F/S), and 94.3% for another three-class group (Z/N/F). The method shows strong performance for short to medium distances and fewer class combinations. However, performance declines when dealing with more complex class sets and longer distances, where texture features become less effective. The drop in accuracy for Z/O/N/F/S beyond d=5 underscores the challenges of maintaining feature robustness at extended distances. Despite this, GLCM remains a promising approach for EEG signal classification. Future work should focus on optimizing distance parameters and feature combinations to further enhance classification performance.
A multicriteria comparison of end-to-end and cascade speech-to-text translation models Labied, Maria; Belangour, Abdessamad; Banane, Mouad
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.v14i4.9241

Abstract

This paper presents a thorough examination of two prominent speech-to-text translation (STT) models: the end-to-end (E2E) model and the cascade model. STT is a critical technology in today’s multilingual society, facilitating communication across language barriers. The study focuses on comparing these models using a multicriteria approach to evaluate their effectiveness in translating speech to text. The E2E model represents a unified architecture that directly translates speech into text, while the cascade model involves separate modules for speech recognition and machine translation (MT). Both models have distinct advantages and challenges, which are explored in detail. Through a multicriteria comparison, this research assesses various performance metrics and criteria to determine the strengths and weaknesses of each model. The weighted sum method is employed to assign weights to evaluation criteria, providing a systematic evaluation framework. The findings have implications for researchers and developers in STT. By understanding the comparative performance of E2E and cascade models, researchers can make informed decisions regarding model selection based on criteria such as accuracy, speed, robustness, and resource requirements. This research advances the understanding of speech translation technologies and provides a foundation for future studies to refine evaluation methodologies, explore hybrid models, and enhance translation quality.
Soybean leaf disease detection and classification using deep learning approach Adimas, Ayenew Kassie; Mekonen, Mareye Zeleke; Assegie, Tsehay Admassu; Singh, Hemant Kumar; Mazumdar, Indu; Gupta, Shashi Kant; Salau, Ayodeji Olalekan; Tin, Ting Tin
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.v14i4.8585

Abstract

In Ethiopia, where soybeans are mainly involved, manual observation has traditionally been relied upon for detecting soybean leaf diseases. However, the manual process is susceptible to numerous issues such as labor-intensiveness, inconsistency, and subjectivity. While previous studies have explored automated classification for soybean leaf disease detection, they primarily focused on binary classification, overlooking the complexity and diversity of soybean leaf diseases, which hinders effective management strategies. This study introduces deep learning algorithms and computer vision for automated soybean leaf disease identification and classification in soybean leaves. By comparing pre-trained convolutional neural network (CNN) models (VGG16, VGG19, and ResNet50V2), a dataset of 3078 soybean leaf images was curated, representing various diseases. Image preprocessing techniques augmented the dataset to 6,958 images, enhancing the model's accuracy and generalization performance. VGG16 demonstrated outstanding performance with a test accuracy of 99.35%, highlighting its promising performance and generalization potential.
A rigorous examination of electromyography forearm muscle response in grasping and swinging scenarios Mohd Bukhari Wan Daud, Wan; Osman Tokhi, Mohammad; Sudirman, Rubita; Juzaila Abd Latif, Mohd; Abas, Norafizah; Sutikno, Tole
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.v14i4.8233

Abstract

This study examines the use of electromyography (EMG) in analyzing forearm muscle responses in hand grasping force with swinging motions. We start by establishing the basics of hand grasping force and swinging motions, laying the groundwork for subsequent discussions. The paper critically assesses various EMG techniques, highlighting how they reveal muscle activity during hand grasping in dynamic situations. We explore how swinging motions affect hand grasping force biomechanics, emphasizing the role of EMG in capturing dynamic muscle activity. A thorough examination of methodologies used in EMG studies provides insights into current practices and emerging trends. Practical applications across fields like rehabilitation and robotics underscore the relevance of this research. The study concludes by addressing current challenges and suggesting future research directions. This synthesis provides a straightforward resource for researchers, practitioners, and technologists seeking a deeper understanding of EMG indices in hand-grasping force analysis with swinging action.
Task scheduling algorithm using grey wolf optimization technique in cloud computing environment Khaleelahmed, Shaik; Selvaraj, Sivakumar; Mohite, Rajendra B.; Bangare, Manoj L.; Bangare, Pushpa M.; Kulkarni, Shriram S.; Ajibade, Samuel-Soma M.; Raghuvanshi, Abhishek
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.v14i4.7695

Abstract

Scheduling refers to the process of allocating cloud resources to several users according to a schedule that has been established in advance. It is not possible to get acceptable performance in settings that are distributed without proper planning for simultaneous processes. When developing productive schedules in the cloud, it is necessary for work scheduling to take a variety of constraints and goals into consideration.When dealing with activities that have performance optimization limits, resource allocation is a very important aspect to consider. When it comes to cloud computing, the only way to achieve great performance, high profits, high scalability, efficient provisioning, and cost savings is with an exceptional task scheduling system. This article presents a grey wolf optimization (GWO) based framework for efficient task scheduling in cloud computing environment. The proposed algorithm is compared with particle swarm optimization (PSO) and flower pollination algorithm (FPA) and GWO is performing task scheduling in less execution time and cost in comparison with PSO and FPA techniques. Execution time taken by GWO to finish 200 task in 120.2 ms. It is less than the time taken by PSO and FPA algorithm to finish same number of tasks.
Comparative analysis of word embedding features to improve the performance of deep learning models on social media data Jasmir, Jasmir; Alam Jusia, Pareza; Arvita, Yulia; Gunardi, Gunardi
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.v14i4.9200

Abstract

In this study, we apply various deep learning methods incorporating word embedding features to evaluate their impact on improving classification performance in sentiment analysis. The methods employed include conditional random field (CRF), bidirectional long short term memory (BLSTM), and convolutional neural network (CNN). Our experiments utilize social media data from restaurant review. By testing different iterations of these deep learning techniques with various word embedding features, we found that the BLSTM algorithm achieved the highest accuracy of 80.00% before integrating word embedding features. After incorporating word embeddings, the BLSTM with the word2vec feature achieved an accuracy of 87.00%. Notably, the CNN showed a significant improvement with the FastText feature. Considering all evaluation metrics—accuracy, precision, recall, and F1-score—the BLSTM algorithm consistently demonstrated the best performance across different word embeddings.
COMATS: a cuckoo-mimicking data anonymization scheme for preserving sensitive preferences in transaction data Gunawan, Dedi; Sulistyo Nugroho, Yusuf; Yasin Al Irsyadi, Fatah; Priyawati, Diah; Nur Rohmah, Arini; Sukoco, Bambang; Islam, Syful
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.v14i4.9113

Abstract

Sharing customer transaction data is becoming more perceived in e-commerce and retail industries. Even though the act derives benefits for companies, it may end up in certain privacy threats, such as sensitive personal preferences disclosure. Therefore, the data owner should take measures to minimize the threats. Data anonymization is one of the solutions that has been suggested to address the issue. However, there are still underlying problems, specifically in diminishing the amount of information loss and item loss, as well as maintaining data properties of the anonymized dataset. This paper proposes a unique data anonymization scheme called COMATS. It adopts the brood parasitism behavior of cuckoo birds in laying their eggs into host nests. The scheme incorporates item insertion technique and item suppression technique. The robustness of the proposed scheme lies in its strategy for selecting suppressed items and determining the inserted items. To ensure its efficacy, the proposed method is evaluated in several experiments. The experimental results suggest that the COMATS can guarantee privacy protection by reducing the probability of a successful attack. Additionally, it can also reduce the number of item losses and preserve better data utility in comparison to existing data anonymization schemes.
Multiwalled carbon nanotube/chitosan composite on quartz crystal microbalance for formaldehyde detection Mahadi, Aisyah Syafiqah; Razib, Mohd Asyraf Mohd; Ralib, Aliza Aini Md; Ahmad, Farah; Yusof, Marmeezee Mohd.
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.v14i4.8948

Abstract

This study introduces multi-walled carbon nanotubes (MWCNT)/chitosan (CS) composite as a potential new sensing material for quartz crystal microbalance (QCM) formaldehyde sensors. This sensing material selectively binds target molecules, causing a measurable frequency shift proportional to the added mass. CS, MWCNT, and the MWCNT/Cs composite samples were prepared for comparison via sonification, crosslinking and dispersion methods. The morphology character was studied using Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and field emission scanning electron microscopy (FE-SEM). Next the samples were drop cast on the QCM working electrode. An adsorption test was conducted to study the static and dynamic response for the formaldehyde detection. The frequency shift of the formaldehyde adsorption for the CS, MWCNT-COOH, and MWCNT/CS-based sensors were 114.98 Hz, 108.23 Hz, and 196.63 Hz respectively. The calculated sensitivity of 23.48 Hz/ppm and regression line R2 at 0.95076 were recorded shows that the MWCNT/CS can be a promising sensing layer to detect formaldehyde vapour.

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