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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 75 Documents
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Search results for , issue "Vol 14, No 2: April 2025" : 75 Documents clear
Machine learning-based and synthetic aperture radar time-series data for rice classification over Sentinel-1 imagery Nardkulpat, Attawut; Boonpook, Wuttichai; Sitthi, Asamaporn; Tan, Yumin
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.7833

Abstract

Rice extraction is critical in remote sensing, especially in Suphan Buri province, Thailand, using Sentinel-1 synthetic aperture radar (SAR) time-series data and advanced machine learning algorithms. Given the challenges of varied terrains and diverse crop types, the research employs different polarization modes (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV+VH) to enhance classification accuracy. The study evaluates the performance of three machine learning algorithms: random forest, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The results demonstrate that combined VV+VH polarization outperforms VV and VH alone, providing better accuracy due to its ability to capture more detailed object features. LightGBM emerged as the most effective among the algorithms, particularly when dealing with large datasets. After hyperparameter tuning (n_estimators: 820, max_depth: 10, and learning_rate: 0.01), LightGBM achieved the highest accuracy. The rice class showed exceptional precision, recall, and F1-score, surpassing other land-use classes (agriculture/forest and urban areas). However, these classes still pose challenges, highlighting the need for future studies to integrate multi-sensor data and explore more sophisticated machine-learning models. This research offers a promising approach to enhancing rice monitoring and management in diverse agricultural landscapes, contributing to more accurate and efficient farming practices.
Explainable deep learning for diagnosing acute lymphocytic leukemia using blood smear images Theodorus Syaron Darmawan, Sion; Husna Shabrina, Nabila
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9073

Abstract

Acute lymphocytic leukemia (ALL) is a rapidly progressing blood cancer that affects the lymphocytes. The diagnosis of ALL typically entails the examination of blood smears under a microscope, processes that are both time-consuming and susceptible to errors. Deep learning (DL) approaches have shown significant promise in automating the classification of ALL from microscopic images. However, the lack of transparency in these models hinders their widespread adoption in clinical settings. This study addresses this challenge by employing fine-tuned EfficientNetV2B3, a DL model, in conjunction with local interpretable model-agnostic explanations (LIME), a technique for explainable artificial intelligence (XAI) technique, to classify microscopic images of ALL. The C-NMC 2019 dataset, which has been augmented to ensure class balance, was utilized for training and evaluation. The proposed approach achieved impressive results, with an average recall, F1-score, and accuracy of 0.9795 and precision of 0.9796. The use of LIME effectively highlights relevant areas for prediction, accurately corresponding to the cell characteristics. The integration of DL and XAI techniques enhances the interpretability of ALL classification models, potentially increasing their trustworthiness and adoption in clinical practice. This study aims to further the development of diagnostic tools that are both precise and transparent for ALL.
Optimized colon cancer classification via feature selection and machine learning Haddou Bouazza, Sara; Haddou Bouazza, Jihad
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9270

Abstract

The increasing dimensionality of gene expression data poses significant challenges in cancer classification, particularly in colon cancer. This study presents a novel filtering approach (FA) and a gene classifier (GC) to enhance gene selection and classification accuracy. Utilizing a dataset of 62 samples, our methods integrate statistical measures and machine learning classifiers, achieving classification accuracies of 96% and 97%, respectively. The FA effectively filters out noise and redundancy, allowing for accurate predictions with a minimal subset of genes, while the GC leverages multiple classifiers for optimal performance. These findings underscore the importance of robust feature selection in improving cancer diagnostics and suggest potential applications in personalized medicine. By addressing the limitations of existing methodologies, our work lays the groundwork for future research in cancer genomics, emphasizing the need for adaptive strategies to handle complex datasets.
Eagle strategy-based crow search algorithm for UCP: integration of pumped storage units in smart grid environment Rizki, Adil; Habachi, Rachid; Tahiry, Karim; Echchatbi, Abdelwahed
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.4037

Abstract

This paper proposes a hybrid eagle strategy with crow search algorithm (ES-CSA) as local optimizer to solve the unit commitment problem (UCP) in power systems. The algorithm aims to minimize total operational costs while considering pumped storage units as spinning reserves. The proposed methodology combines the exploration capability of ES with CSA's local search efficiency to determine optimal generator scheduling and power dispatch. The approach is validated using standard test cases from the literature, demonstrating improved convergence and cost reduction compared to existing methods. Results confirm the effectiveness of integrating pumped storage units in reducing overall system costs while maintaining reliable operation.
Performance evaluation of feature extraction to improve the classification of PTM in C-glycosylation using XGBoost Damayanti, Damayanti; Rosyking Lumbanraja, Favorisen; Junaidi, Akmal; Sutyarso, Sutyarso; Nugroho Susanto, Gregorius; Hendrastuty, Nirwana
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8466

Abstract

Protein function is regulated by an important mechanism known as post-translational modification (PTM). Covalent and enzymatic protein modifications are added during protein biosynthesis, and such alterations significantly influence the regulation of gene activity and the functionality of proteins. Glycosylation, one type of PTM, involves adding sugar groups to a protein's structure. Numerous illnesses, such as diabetes, cancer, and the flu, have been linked to glycosylation. Therefore, it is critical to predict the presence of glycosylation, whether it occurs or not. Currently, predicting glycosylation sites is still done manually using biological methods, which require repeated experiments and a significant amount of time. To address these challenges, it is essential to rapidly develop computational data models using machine learning methods. In this study, the extreme gradient boosting (XGBoost) method is implemented, and C-glycosylation data is obtained from the publicly accessible UniProt website. The objective is to enhance the accuracy of C-glycosylation prediction using the XGBoost method. Feature extraction is performed using amino acid index (AAindex), composition, transition, and distribution (CTD), solvent AccessiBiLitiEs (SABLE), hydrophobicity, and pseudo amino acid composition (PseAAC) to improve accuracy. The minimum redundancy maximum relevance (MRMR) method is applied for feature selection. The findings of the study demonstrate that the PTM C-glycosylation prediction achieved 100%.
Comparative simulation of phishing attacks on a critical information infrastructure organization: an empirical study Sirawongphatsara, Patsita; Pornpongtechavanich, Phisit; Phanthuna, Nattapong; Daengsi, Therdpong
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8020

Abstract

Nowadays, cybersecurity is crucial. Therefore, cybersecurity awareness should be a concern for businesses, particularly critical infrastructure organizations. The results of this study, using simulated phishing attacks, indicate that in the first attempt, workers of a Thai railway firm received a phony email purporting to inform recipients of a special deal from a reputable retailer of information technology (IT) equipment. The findings showed that 10.9% of the 735 workers fell for the scam. This demonstrates a good level of awareness regarding cyber dangers. The workers who were duped by the initial attack received awareness training. Next, a second attempt was carried out. This time, the strategy was for the workers to change their passwords through an email notification from the fake IT staff. According to the findings, 1.4% of the workers fell victim to both attacks (different email content), and a further 8.0% of the workers who did not fall victim to the first attack were deceived. Furthermore, after the statistical analysis, it was confirmed that there is a difference in the relationship between the workers and the two phishing attack simulations using different content. As a result, this study has demonstrated that different types of content can affect levels of awareness.
Improving nutrient prediction models with polynomial and ratio features and mRMR selection Indriani, Fatma; Budiman, Irwan; Kartini, Dwi; Handayani, Lilies
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9189

Abstract

Due to limited space and regulations, food labels often lack information on micronutrients, i.e., vitamins and minerals. Accurately predicting missing these micronutrient data is essential yet challenging. This study explores the feasibility of using machine learning to predict these missing nutrients based on a limited reported nutrient (protein and carbs). Using the Tabel Komposisi Pangan Indonesia (TKPI) dataset, we evaluated the performance of 12 diverse classifiers to predict binary classes ("low" or "high") for 13 target micronutrients. Random forest emerged as the best performing classifier with an average accuracy of 0.7421 across all target nutrients. Additionally, we introduced feature engineering techniques by incorporating polynomial and ratio features to enhance model performance. Minimum redundancy maximum relevance (mRMR) feature selection was then applied to identify the most informative features. This approach boosted the average accuracy of the random forest classifier to 0.7591. These findings highlight the efficacy of feature engineering and selection in enhancing nutrient prediction models, demonstrating the potential to improve consumer knowledge about unknown nutrients in food.
SentimentLP: unveiling advanced sentiment analysis through Leptotila optimization-based gradient boosting machines Merlin Durairaj John Louis, Anitha; Kumar Dhanasekaran, Vimal
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8959

Abstract

Sentiment analysis is pivotal in extracting insights from textual data, enabling organizations to understand customer opinions, market trends, and brand perception. This study introduces a novel approach, SentimentLP, which integrates Leptotila optimization (LPO) with gradient boosting machines (GBM) for sentiment analysis tasks. The proposed framework leverages LPO’s dynamic optimization capabilities to enhance GBM models’ performance in sentiment classification. Through iterative refinement and adaptive learning, SentimentLP optimizes feature extraction, model training, and ensemble learning processes, improving sentiment analysis accuracy and efficiency. Results from various evaluation metrics, including precision, recall, classification accuracy, and F-measure, demonstrate the effectiveness of SentimentLP in accurately capturing sentiment expressions in text data. Additionally, the fusion of LPO with GBM ensures scalability, adaptability, and interpretability of sentiment analysis models, making SentimentLP a valuable tool for extracting actionable insights from textual data across diverse domains and applications.
The implementation of the K-nearest neighbor algorithm to detect the KRSRI robot obstacles Hamonangan Nasution, Tigor; Muhammad Prihandoyo, Arza; Seniman, Seniman
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8225

Abstract

The Indonesian SAR robot contest (KRSRI) is a development of the fire extinguisher robot contest (KRPAI); initially, the robot at KRPAI only put out fires. Still, at KRSRI, the robot was asked to prioritize the SAR function. The robot had to overcome obstacles in this contest to complete it. Based on this, an obstacle detection system for the robot was designed using machine learning with the K-nearest neighbor algorithm and gray level co-occurrence matrix feature extraction. Later, the robot is expected to be able to carry out accurate obstacle detection to prioritize efficiency so that no more time is consumed due to the robot incorrectly detecting an obstacle. The results of the tests that have been carried out show that the detection accuracy based on the test dataset is 80% for rising barriers, 100% for debris obstacles, and 90% for step obstacles, and an error value of 20% for increasing obstacles is obtained, 0% for debris obstacles, and 10% for stair obstacles.
Advancing palm oil fruit ripeness classification using transfer learning in deep neural networks Kurniawan, Rudi; Samsuryadi, Samsuryadi; Susilawati Mohamad, Fatma; Oktafia Lingga Wijaya, Harma; Santoso, Budi
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8651

Abstract

The palm oil industry is a significant component of Indonesia’s economy, driven by increasing global demand across various industries. Manual identification of palm oil fruit ripeness is often subjective and labor-intensive, creating a need for a faster and more accurate solution. This study proposes the use of deep learning models based on transfer learning to enhance the classification of palm oil fruit ripeness. Our research evaluates several models, finding that ResNet152V2 achieves the highest performance with superior accuracy and the lowest validation loss. DenseNet201, MobileNet, and InceptionV3 also deliver strong results, each demonstrating an accuracy above 0.99 and a validation loss below 0.04. Cross-validation confirms that ResNet152V2, DenseNet201, and MobileNet maintain high and consistent performance across different folds, showcasing their stability and reliability. This approach provides a promising alternative to manual methods, offering a more efficient and precise means for determining palm oil fruit ripeness, which could significantly benefit the industry by streamlining quality control processes.

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