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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 75 Documents
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Search results for , issue "Vol 15, No 2: April 2026" : 75 Documents clear
Combined analysis of the importance of factors in agricultural process management tasks Abdikerimova, Gulzira; Yessenova, Moldir; Zharkimbekova, Aizhan; Beldeubayeva, Zhanar; Bayegizova, Aigulim; Uzakkyzy, Nurgul; Alimagambetova, Ainagul; Murzabekova, Gulden
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11206

Abstract

The article presents a combined approach for analyzing the significance of factors in the agro-industrial sector using Shapley additive explanations (SHAP), simple combination, and principal component analysis (PCA)+combination methods. The study addresses the pressing need for efficient agricultural resource management under constrained and changing climatic conditions. The proposed methodology evaluates the impact of various factors on key performance indicators such as yield, income, and operating costs. SHAP analysis identified critical determinants, with "Land Area (ha)" contributing significantly to "Market Capacity" (59.5%) and "Sales Revenue" (57.2%), highlighting the importance of production scale. The simple combination method, integrating gradient boosting (GB), mutual information (MI), and recursive feature elimination (RFE) with Lasso, revealed a more balanced factor distribution, assigning 14.5% to "Land Area" and 12.8% and 10.7% to “Seed Use” and “Fertilizer Cost,” respectively. The PCA+combination method emphasized global trends, identifying "Yield per Hectare" (22.5%) and "Field Size" (11.5%) as key contributors to variance. This integrative approach captures localized effects and global interdependencies, offering comprehensive data interpretations. The findings are instrumental in optimizing resource management and strategic planning and enhancing agricultural production efficiency.
Design and analysis of metamaterial-inspired annular ring patch antenna for 5G applications Vidyadharan, Vishu; Sivaramalingam, Sivagnanam; Padma Suresh, Lekshmi Kanthan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11069

Abstract

This work presents an annular ring patch antenna (ARPA) on the basis of metamaterial (MTM) for 5G applications. ARPA has become a popular choice for a range of wireless applications owing to its low profile, miniature size, simplicity in integrating with printed circuit boards (PCBs), and compatibility with contemporary fabrication techniques. Nevertheless, the bandwidth, gain, and efficiency restrictions that the ARPA frequently experiences are crucial for fulfilling the stringent needs of 5G communication systems. To overcome these obstacles, researchers have tuned to materials known as MTMs, which are synthetic materials having special electromagnetic (EM) characteristics absent from natural materials. By including complementary split ring resonators (CSRR) structures into microstrip patch antenna (MPA) the important characteristics like bandwidth, gain, and efficiency are enhanced. An EM simulation software named computer simulation technology (CST) Microwave Studio is employed for the evaluation of the antenna prototype. Rogers RT5880, a commercially accessible substrate material is used to develop the prototype. Comparative analysis is conducted between conventional antennas and ARPA, in which the proposed antenna attains low electrical loss, uniform electrical properties, thermal stability, and dimensional stability with gain of 5.65 dB. The developed work proves that the addition of CSRR structure is the solution to the development of antennas with superior performance characteristics.
Ethylene gas-based fruit expiry predictor: a sustainable solution to fruit wastage Idowu-Bismark, Olabode; Eberchukwu, Anyiam Chinemere; Oshin, Oluwadamilola; Emmanuel, Ibiang; Mary, Tonwe; Adetiba, Emmanuel
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9809

Abstract

Globally, environmental and economic issues are very apparent regarding food wastage. Fruits play a significant role since they can be damaged easily. Ethylene gas is one of the significant gases produced by fruits when they begin to ripen; it tests whether the fruit is ripe or else spoilable. This work presents a sustainable solution for fruit wastage: an ethylene gas-based fruit expiry predictor (FEP). The system is designed and developed around advanced sensing and artificial intelligence (AI) models for real-time monitoring of ethylene levels and temperature in forecasting the spoilage of fruits. The system consists of non-invasive sensors for detecting ethylene and temperature, a data processing microcontroller, and an AI model trained on a large dataset to make accurate predictions regarding the expiry of fruit. The AI model processes the information collected by the sensors and then displays the grade (level of ripeness of the fruit) on a liquid crystal display (LCD) screen. This solution improves fruit management with reduced wastage in line with international sustainability targets. These would enable real-time and highly accurate predictions of fruit spoilage, allowing end-users informed choices that will eventually lead to reducing the carbon footprint from food waste and increasing food security.
Cloud security: an upcoming development in hybrid automated storage protection systems VenkataSubbu, Srividya Bharadwaja; Sasi, Smitha; Pakeer Saheb, Meharunnisa Sakkar Sab; Keshava Rao, Harikeerthan Mysore; Seetharamaiah, Soumya
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10723

Abstract

The need for cloud storage is growing daily, despite its apparent reliability and scalability. Because of the complexity of data security, including privacy and confidentiality, it is difficult to store and manage user data remotely. An untrustworthy insider could be the cloud third party responsible for managing the data security issues occurring within the cloud services. An automated hybrid cryptographic algorithm for cloud data storage is provided by the current research to improve data protection and guarantee information confidentiality without any disruption from cloud third parties. Two encryption methods that are employed to attain high performance and efficiency are enhanced automated concurrent encryption for data blocks (CEDB) and improved automated asynchronous encryption for data streams (AEDS). By converting the fixed S-box in the traditional AES to a modifying S-box using a ternary Galois field (GF) and complementary ternary GF, we present a novel encryption technique in enhanced automated concurrent encryption. Furthermore, the matrix utilized for the AES mix column approach is converted into a dynamic matrix using the ternary GF and complementary ternary GF. This cryptographic approach aims at providing cloud data security, ensuring confidentiality, and integrity for the user’s information even in compromised situations.
Evaluating the effectiveness of digital filtering techniques in electronic stethoscopes: a study of Kalman and Butterworth filters Setioningsih, Endang Dian; Sumber, Sumber; Triwiyanto, Triwiyanto; Abdullayev, Vugar; Amrinsani, Farid; Raharjo, Bima Maulana; Fa’altin, Tetrik
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.8674

Abstract

Heart sound, or phonocardiogram (PCG), signals are often distorted by noise from respiration, movement, and the surrounding environment, which complicates accurate cardiac feature extraction in portable monitoring systems. This study aims to design and evaluate an effective digital filtering method to enhance PCG signal quality obtained from a low-cost acquisition system based on a condenser microphone sensor and an ESP32 microcontroller. The main contribution of this work is the implementation and comparison of two noise-reduction approaches: Butterworth band-pass filters of various orders and the Kalman filter applied to PCG signals acquired from mannequin-based simulations. Data were recorded for 10 seconds at a 1000 Hz sampling rate, processed in MATLAB, and analyzed using the fast Fourier transform (FFT) to determine the optimal frequency ranges. Experimental results demonstrate that the 8th-order Butterworth band-pass filter achieved the highest signal-to-noise ratio (SNR) improvement, averaging 25.659 dB, outperforming other configurations. These findings indicate that an appropriately tuned Butterworth filter provides a simpler yet robust solution for real-time PCG denoising in embedded systems. Future work will integrate the filtering process directly into the ESP32 firmware and evaluate its performance on human subjects to enhance clinical applicability.
Thermal mode modeling using neural network technologies and the finite element method Mussabekov, Nazarbek; Utepbergenov, Irbulat; Kaliyev, Zhanybek; Issayeva, Zhazira; Ybytayeva, Galiya; Ansabekova, Gulbakyt; Karnakova, Gayni; Butabaeva, Karlygash
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11268

Abstract

This study presents the analysis and modeling of the thermal regime of a furnace lining at an industrial copper smelting facility using a combined approach based on neural network (NN) technologies and the finite element method (FEM). Experimental temperature data were collected from a laboratory setup equipped with three thermocouples (TP-2488/1 and TCRosemount 0065), with a sampling frequency of 1 Hz over a total duration of 5 hours, resulting in 18,000 measurement points. The measurement uncertainty of the thermocouples did not exceed ±1.5 °C. These data were used both for model development and for validating the numerical FEM simulations. A feedforward neural network was trained using 70% of the dataset, while 15% and 15% were used for validation and testing, respectively. The prediction error of the neural network remained within 3% with a 95% confidence interval of [2.6%, 3.4%]. The results show that the proposed hybrid approach improves temperature prediction accuracy and reduces static control error by 15% when combined with a proportional-integral controller. The methodology demonstrates significant potential for improving thermal process stability and reducing energy consumption in high-temperature metallurgical systems.
Enhancing urban EV integration: a data-driven hybrid approach to charging station optimization and energy management Hussain, Shaik Mohammed; Swapna, Ganapaneni; Rao, Kambhampati Venkata Govardhan; Kumar, Malligunta Kiran; Teja, Srungaram Ravi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10614

Abstract

Electric vehicles (EVs) are pivotal to sustainable urban mobility, but their large-scale adoption in developing cities depends on efficient charging infrastructure and grid stability. This study proposes a hybrid deep learning framework to optimize EV charging station placement and energy scheduling in Vijayawada, India, projected to host 70,000 EVs by 2028. A convolutional neural network (CNN) is employed to classify charger types (Fast vs. Level 2) based on spatial features such as geospatial coordinates, population density, and traffic volume, while a long short-term memory (LSTM) network forecasts hourly charging demand using synthetic 24-hour sequences. The dataset comprises 108 candidate locations, designed to mirror real usage patterns. Model performance is evaluated using classification accuracy and mean absolute error (MAE). Results indicate that the CNN achieved 92% accuracy in charger type prediction, while the LSTM produced an hourly demand forecast with an MAE of 25 sessions/hour. These outcomes demonstrate the framework’s ability to reduce grid stress by shifting peak loads and strategically placing chargers in high-demand zones. The study provides a scalable and adaptable solution for EV infrastructure planning, enabling resilient grid integration, and supporting sustainable urban energy systems.
Flood mapping using Res-Q and machine learning on imbalanced data Yuliyanti, Siti; Purwayoga, Vega; Rachman, Andi Nur; Gusnadi, Zakwan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10374

Abstract

Flood disaster mapping requires accurate methods to support early warning and mitigation planning. To address common issues such as imbalanced data distribution and limited attribute handling, this study proposes an improved approach. The methodology includes: i) modification of the spatial sort filter skyline method with reverse normalization based on attribute preferences, applied when an attribute has minimal preference to ensure balanced consideration during skyline filtering; ii) data labeling and balancing, where initial flood potential labeling is generated using Res-Q, followed by K-Means clustering to group data into four classes (low, moderate, high, and very high) and SMOTE to further balance the dataset with 558 data points per class; iii) model evaluation using the C5.0 algorithm under three schemes, showing high and consistent accuracy with 89.24% on imbalanced data (Schema 2) and 93.3% on balanced data (Schema 3), while Schema 1 shows overfitting due to extreme imbalance; and iv) the main contribution, integrating reverse normalization with skyline filtering combined with clustering and resampling, enhancing both accuracy and robustness in identifying flood-prone areas. This structured approach highlights methodological improvements, reliable results, and practical contributions for effective flood disaster management.
Bibliometrics on the use of remote sensing and machine learning in crop classification Sánchez-Chavez, Andrea del Pilar; Henao-Cespedes, Vladimir; Garcés-Gómez, Yeison Alberto
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10476

Abstract

This study presents a bibliometric analysis of global research on crop classification using remote sensing and machine learning (ML), a field critical to advancing precision agriculture. A systematic search in Scopus identified 2,122 peer-reviewed articles published between 2014 and 2023. The analysis employed VOSviewer and the Bibliometrix package in R to assess publication trends, citation impact, and keyword co-occurrence networks. Results reveal a marked increase in scientific production after 2017, coinciding with the availability of high-resolution satellite imagery and the adoption of deep learning algorithms, particularly convolutional neural networks (CNNs). China emerged as the leading contributor, followed by the United States and India, reflecting strong investments in agricultural modernization and remote sensing infrastructure. Thematic mapping highlights both traditional research areas, such as vegetation indices and land cover classification, and emerging themes, including AI-supported algorithms and food security. Despite this growth, disparities persist, with most countries contributing fewer than 100 publications, underscoring the need to promote participation in underrepresented regions. Findings demonstrate the field’s rapid evolution, emphasize the integration of AI-driven methods in crop monitoring, and suggest future directions combining remote sensing, ML, and internet of things (IoT) technologies to address global challenges in food security and sustainable agricultural management.
A modified transfer function for frequency sampling filters: theory, design, and applications Rybka, Serhii; Varava, Ivan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10914

Abstract

The article is devoted to frequency sampling filters (FSF). FSFs are highly efficient linear-phase digital filters that can be significantly more computationally efficient than Parks–McClellan FIR filters. FSFs are well-suited for implementing filter banks, which can be used to construct high-performance spectrum analyzers. Although these filters have been known for a long time, they have not been widely adopted despite their efficiency. This article presents the authors’ perspective on the reasons behind the limited use of FSFs. A new original method for forming the transfer function (TF) of FSFs using an ideal analog prototype is proposed. The proposed method fundamentally differs from the longestablished classical approach. The TF of an analog filter that meets the require-ments of absolute linearity in the phase response (PR) was considered. The bilinear z-transform method was applied to this ideal TF. By applying L’Hˆopital’s rule, an analytical expression for determining the weighting coefficients of the resulting TF was obtained. The article presents examples of calculated FSFs and a filter bank based on them. Tables of the optimized weighting coefficients and plots of their attenuation characteristics are provided. An improved block diagram of the FSF is also presented. The characteristics of the classical FSF TF and the proposed (alternative) FSF TF are analyzed and compared to determine their advantages and disadvantages.

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