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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 3,126 Documents
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Python-based stock price prediction using backpropagation neural networks: a case study on ANTM Prind Triajeng Pungkasanti; Febrian Wahyu Christanto; Fadhilatut Tasyriqul Hajjas Sabat; Christine Dewi; Eryan Ahmad Firdaus
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9760

Abstract

Accurate stock price prediction is critical for informed investment decisions. Today, stock trading has become a popular option as a source of income among people, due to its potential for rapid gains in a short time, but, due to fluctuating stock prices, it can cause great losses in exchange. This study aims to forecast the closing price using the backpropagation neural network algorithm so that it can be used as a decision support for potential investors and traders in this research, the system was built using the Python programming language, and the stock price data used were shares of the company Aneka Tambang Tbk (ANTM). The results of this research are root mean squared error (RMSE) values, additional labels for prediction results, and graphs for comparison of the original data with the predicted data. Based on the testing result, the best value of RMSE is 3.786, the mean absolute percentage error (MAPE) value is 0.001 which indicates that the prediction results are very close to the actual value.
Power-LSTM for smart greenhouse: a novel deep learning approach to temperature prediction in a Mexican case study Salma Ait Oussous; Dauris Lail Madama; Rachid El Bouayadi; Aouatif Amine
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9438

Abstract

This paper addresses the challenge of predicting internal temperature in green-house environments, a critical aspect of optimizing crop growth and ensuring resource efficiency. While machine learning (ML) techniques have been widely applied to predict greenhouse climates, deep learning (DL) methods offer the po-tential to capture more complex relationships within the data. In this study, we present a comprehensive evaluation of ML and DL models, along with our pro-posed power-long short-term memory (PLSTM) model, to predict the internal temperature of a greenhouse using a database from Mexico. We compared tradi-tional ML models such as linear regression (LR) and extreme gradient boosting (XGBoost) with DL architectures like gated recurrent unit (GRU), artificial neu-ral networks (ANN), hybrid LSTM-ANN and LSTM-RNN architectures. Our proposed PLSTM model outperformed both ML and DL models, achieving the R2 score of 0.9710, and root mean square error (RMSE) equal to 0.1710, high-lighting its superior ability to predict complex time-series data.
CT-based lung cancer detection using spatially localized integral transforms with U-NET classification Abel Belay Wossene; Dawit Assefa; Ayodeji Olalekan Salau; Sepiribo Lucky Braide; Aitizaz Ali; Ting Tin Tin
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9161

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide, emphasizing the need for early, accurate, and scalable detection methods. Low-dose computed tomography (LDCT) has improved early diagnosis, yet challenges like image noise, low contrast, and subtle nodule features often limit reliable interpretation in large-scale screening. This paper proposes a computationally efficient computer-aided detection (CADe) framework that integrates a rotation-invariant, spatially localized integral transform feature extraction with a U-Net-based classifier to enhance lung nodule detection and segmentation. The approach strengthens spatial feature representation while maintaining low computational and memory demands, enabling real-time use in resource-limited clinical settings. Implemented in MATLAB and evaluated on the Cancer Imaging Archive (TCIA) dataset, the system achieved 99.32% classification accuracy, 88.88% specificity, 84.21% precision, 87.3% intersection over union (IoU), and 92.9% dice similarity coefficient (DSC). These results show clear improvements over conventional methods, particularly in rotational robustness and efficiency—key requirements for scalable screening. Although precision and IoU could be further optimized, the framework demonstrates strong potential for clinical adoption. By providing accurate, fast, and robust nodule analysis, this work advances practical high-performance tools for early lung cancer detection, especially in resource-constrained environments, ultimately contributing to better patient survival rates.
Facial expression recognition for emotional state identification using deep convolutional neural network Abdelhakim Gharbi; Abdeljalil Gattal; Issam Bendib
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10998

Abstract

Facial expressions represent one of the most significant forms of non-verbal communication, with psychologists identifying six universal expressions: happiness, sadness, surprise, anger, fear, and disgust. Recognizing these expressions presents considerable challenges due to the subtlety of facial movements and variations across individuals. This paper presents a deep learning-based system for facial expression recognition (FER) that employs convolutional neural networks (CNNs) to classify emotional states. We investigate both a novel CNN architecture developed from scratch and established transfer learning approaches, evaluating their performance on the FER-2013 dataset. Our experimental results demonstrate that the proposed custom CNN architecture achieves 72.93% accuracy when combined with comprehensive data augmentation techniques, outperforming several baseline models. The system shows particular strength in recognizing fundamental emotions while maintaining computational efficiency suitable for real-time applications.
Challenges and opportunities in integrating electric vehicles with distributed renewable generation Mahammad Anwar; Abdul Khadar Asundi; Shabana Sultan Sarmas
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11007

Abstract

Electric vehicles (EVs) and distributed generation (DG) based on renewable energy sources (RES), mainly solar photovoltaics (PV) and wind, are the two main pillars of the current smart grid. EVs are growing in popularity. They promise improved resilience and sustainability through their synergistic combination. This review essay offers a thorough analysis of the opportunities and difficulties present in this pairing. We examine that managing the dual intermittency of renewable energy and EV mobility puts pressure on grid stability and power quality. This study carefully compares the performance, economic feasibility, and grid implications of several integration architectures, ranging from hybrid systems and vehicle-to-grid (V2G) ecosystems to solar and wind-powered charging stations. This work also analyzes the important trade-offs between grid hosting capacity (HC), cost vs reliability, and infrastructure centralization. According to the study, the main socioeconomic and technical obstacles to scalability are antiquated regulations, battery degradation issues, and a lack of standardized interoperability. To turn EVs from a grid burden into a versatile asset for a decarbonized energy future, the study concludes by outlining critical future research topics, highlighting the necessity of AI-driven energy management systems (EMSs), sophisticated vehicle-to-everything (V2X) services, and dynamic HC analysis.
Efficient transformer architecture for sarcasm detection: a study on compression and performance Parul Dubey; Amit Mishra; Aruna Singh; Murtuza Murtuza; Akshita Chanchlani; Pushkar Dubey
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11102

Abstract

This sarcasm detection is a crucial subtask in natural language processing (NLP) particularly for sentiment analysis and conversational AI. Its complexity lies in interpreting context, tone, and intent beyond literal meanings. Traditional models often struggle to capture such nuances, especially in informal and diverse language settings. Moreover, existing approaches lack computational efficiency and fail to adapt well across different domains. This study evaluates three benchmark datasets—News Headlines, Mustard, and Reddit (SARC)—representing structured, scripted, and conversational sarcasm, respectively. Each dataset poses unique linguistic and contextual challenges. The proposed methodology integrates transformer-based models (RoBERTa and DistilBERT) with context summarization using BART and metadata embedding. A comparative analysis is conducted on both linguistic accuracy and computational efficiency. The novelty lies in aligning sarcasm detection performance with architectural optimization for real-time deployment. Evaluation is conducted using accuracy, F1-score, Jaccard coefficient, precision, and recall. Results show that RoBERTa delivers peak performance, while DistilBERT achieves a 1.74× speedup with competitive results, making it suitable for scalable and efficient sarcasm detection.
A multimodal framework for secure digital document fortification using Morse code and biometric watermarking Tresa Maria Josylin; Ganeshayya Shidaganti; Vishwachetan Dasegowda; Anasuya Jadagerimath; Prakash Sheelvanthmath
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10789

Abstract

In an era of escalating cybersecurity threats, traditional authentication methods become more susceptible to attacks like phishing, brute force, and identity theft. With the aim to counter these difficulties, this paper introduces a multi-layered authentication that merges facial recognition, eye-tracking based Morse code verification, biometric verification using convolutional neural network (CNN) and cryptographic watermark with Rubik's encryption. The document fortification system proposed here improves security by integrating biometric authentication, behavioral verification, and encryption-based watermarking to provide both user authentication and document integrity. The authentication process begins with facial recognition, where multi-task cascaded convolutional neural network (MTCNN) detects facial features and FaceNet generates unique embeddings for identity verification. Upon successful authentication, users input a Morse code password via an eye-blinking mechanism, which is decoded and validated. Additionally, fingerprint and iris recognition using CNN models further enhance security. The Rubik’s encryption algorithm secures biometric watermarks within digital documents, preventing tampering. An one-time password (OTP)-based re-authentication mechanism ensures only authorized users can access encrypted files. Experimental results demonstrate the system’s high accuracy and resilience against security threats, making it a robust and scalable authentication framework. This research highlights the potential of multi-factor authentication (MFA) in modern cyber-security, offering a future-ready solution for securing sensitive digital documents such as images and pdf files.
Optimizing the implementation of the Saber post-quantum cryptography scheme with a hybrid architecture Sabyrzhan Atanov; Khuralay Moldamurat; Luigi La Spada; Makhabbat Bakyt; Adil Maidanov
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10103

Abstract

This paper presents a hardware–software hybrid implementation of the Saber key encapsulation mechanism (KEM) on a Terasic DE10-Nano board, which combines an ARM Cortex-A9 processor and an Intel Cyclone V field programmable gate array (FPGA). By offloading computationally intensive polynomial multiplication to a dedicated FPGA module, the hybrid design significantly reduces execution time. Experimental results show that compared to a software-only approach, the hybrid design decreases execution time by 40% for key generation, 35% for encapsulation, and 50% for decapsulation. The consistent performance gains were confirmed across the LightSaber, Saber, and FireSaber parameter sets, demonstrating that CPU-FPGA co-design offers significant efficiency improvements for post quantum cryptography (PQC), especially on platforms with limited resources.
An intelligence framework for threat detection and response in cloud–IoT-assisted enterprise environments Amith Shekhar Chandrashekhar; Sarala D V; Ambuja K; Rajani Kallhalli Channarayappa; Karanam Sunil Kumar
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11046

Abstract

Cloud-internet of things (IoT)-enabled enterprise environments have become an integral part of modern infrastructures, but their increasing interconnectedness makes them vulnerable to sophisticated and rapidly evolving cyber threats. Existing methods for intrusion detection and threat intelligence often suffer from limitations such as high false alarms, low adaptability to new attacks, and computational overhead. To address these challenges, this paper presents an intelligent hybrid framework for threat detection and response in cloud-IoT-enabled enterprises. The proposed system adopts a two-stage architecture: an autoencoder (AE)-based anomaly detector serves as the first security layer to identify deviations from normal traffic behavior, while a convolutional neural network-long short-term memory (CNN-LSTM) model with an attention mechanism serves as the second layer to classify known attack categories with high accuracy. A response mechanism is further integrated to log events, assign severity scores, apply automated protections, and generate real-time alerts, transforming detection into proactive prevention. The system has been evaluated on the benchmark CSE-CIC-IDS2018 dataset, where the anomaly detector achieved an accuracy of 98.4% with a false positive rate of 2%, while the CNN-LSTM-Attention intrusion classifier achieved an accuracy of 99.42%.
Advanced data balancing techniques with machine learning models for acute liver failure prediction Pradnya Borkar; Snehal Bankatrao Shinde; Mayank Jichkar; Mahek Humne; Sagarkumar Badhiye; Tausif Diwan; Nileshchandra Pikle
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10010

Abstract

Amongst various diseases, one of the severe diseases is acute liver failure (ALF) and it is a quick decline in liver health that normally lasts a few days to a few weeks. Machine learning (ML) techniques can play a valuable role in the diagnosis and management of ALF. The proposed study made an effort to remedy the issue of the Kaggle Dataset's class imbalance by carrying out an exhaustive experimental assessment making use of two distinct approaches, namely synthetic minority oversampling technique (SMOTE) and synthetic minority oversampling technique and edited nearest neighbours (SMOTE-ENN). Both SMOTE-balanced and SMOTE-ENN balanced datasets are used to train the support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGBoost), and stacking models. Compared to the SMOTE method, the results demonstrated that the SMOTE-ENN balanced dataset achieved a considerable increase in the accuracy of its predictions. The results showed that the KNN algorithm has attained 99.52\% accuracy, along with a precision of 99.07\%, recall of 99.35\%, and F1 measure of 99.04\%. As a result, we discovered that a data balancing method that is not overly complicated and a supervised ML algorithm could be used to forecast ALF with very high accuracy and excellent potential for utility.

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