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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 2,901 Documents
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Research on PMSM control without speed sensorless applied to industrial electric drive system based on ADSMC method Van Chinh, Ha; Duc Chuyen, Tran; Hoai Nam, Nguyen
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10266

Abstract

The paper research, calculates, and designs an industrial electric drive system control such as: computer numerical control (CNC) machining machines, milling machines, and grinding machines, with sensorless permanent magnet synchronous motors (PMSM) based on measuring current components, axial position and applied voltage to obtain information about rotation angle and speed for PMSM based on adaptive sliding mode control (ADSMC) method. Here an optimal sliding surface will be designed to demonstrate faster convergence than conventional sliding mode control. Then, an adaptive law is researched and developed to make the control parameters, especially the switching gain, updated quickly online. Therefore, the motor noise can be effectively reduced and the system can be better eliminated from noise, Chattering, and nonlinear noise. Finally, a reference model was created, the exponential decay curve was applied to track the angular position error. The ADSMC system with model reference proposed by the authors in the paper has combined the advantages of sliding mode control method and adaptive control method according to the sample model. The simulation results show that the performance is achieved faster and the control process is more accurate, the error of speed and angular position (less than 0.01%) compared to other control methods.
Cybersecurity challenges in healthcare: mitigating risks in a rapidly evolving digital landscape Arina, Alexei; Bolun, Ion; Alexei, Anatolie
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9685

Abstract

The rapid digital transformation of the healthcare sector brings significant benefits but also exposes institutions to escalating cybersecurity risks. This study analyzes vulnerabilities such as ransomware, supply chain compromises, and insider threats, drawing on international reports from World Health Organization (WHO), Healthcare Information and Management Systems Society (HIMSS), Ponemon, and Verizon. The paper contributes a unique mitigation framework that consolidates three strategic pillars: i) continuous cybersecurity training for medical staff, ii) deployment of advanced technological safeguards, and iii) establishment of collaborative incident reporting mechanisms. Beyond mapping current threats, the study provides policy-oriented guidance for strengthening resilience in both developed and emerging healthcare systems. With healthcare breaches costing an average of $10.1 million per incident, the findings highlight the urgent need for coordinated action to ensure patient safety, service continuity, and institutional trust.
Feature selection to predict COVID-19 new patients in the four southern border provinces of Thailand Photphanloet, Chadaphim; Shuaib, Sherif Eneye; Ritraksa, Siriprapa; Riyapan, Pakwan
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9068

Abstract

This paper presents a machine learning-based prediction framework that utilizes ensemble feature selection techniques to accurately forecast the number of new coronavirus disease (COVID-19) infections in Thailand’s four southern border provinces. The framework used include multiple linear regression (MLR), mul tilayer perceptron neural networks (MLP-NN), and support vector regression (SVR), to classify short-term trends in new patient cases. The study evaluates the effectiveness of these models across different provinces and demonstrates how integrating feature selection methods: forward selection (FS), backward elimination (BE), and genetic algorithms (GA) enhances prediction accuracy. The findings highlight the adaptability of the models, with each province ben efiting from tailored model-feature selection strategies. The results show that the predictive models align closely with real patient data, enabling authorities to anticipate outbreaks and implement timely interventions. Moreover, the pro posed methodology can be applied to other epidemics, making it a valuable tool for public health planning and preparedness. The study offers actionable in sights for decision-makers, emphasizing the importance of predictive modeling in community-level outbreak management.
Enhancing cloud resource management: leveraging adversarial reinforcement learning for resilient optimization Dwinggo Samala, Agariadne; Rawas, Soha; Criollo-C, Santiago
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10636

Abstract

This paper introduces the first adversarial reinforcement learning (ARL) framework for resilient cloud resource optimization under dynamic and adversarial conditions. While traditional reinforcement learning (RL) methods improve adaptability, they fail when faced with sudden workload surges, security threats, or system failures. To address this, we propose an ARL-based approach that trains RL agents using simulated adversarial perturbations, such as workload spikes and resource drops, enabling them to develop robust allocation policies. The framework is evaluated using synthetic and real-world Google Cluster traces within an OpenAI Gym-based simulator. Results show that the ARL model achieves 82% resource utilization and a 180 ms response time under adversarial scenarios, outperforming static policies and conventional RL by up to 12% in terms of cost-effectiveness. Statistical validation (p0.05) confirms significant improvements in resilience. This work demonstrates the potential of ARL for self-healing cloud schedulers in production environments.
Iris-based lung cancer pre-scanning for mobile platforms Ho-Dac, Hung; Anh Le, Tuan; Thua Huynh, Trong
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.11146

Abstract

Lung cancer remains one of the leading causes of cancer-related mortality globally, with early detection being critical for improving survival rates. Traditional diagnostic methods such as computed tomography (CT) scans and biopsies are effective but often costly, invasive, and inaccessible in resource-limited settings. In this study, we evaluate suitable deep learning models for mobile platforms and propose an application for early detection of lung cancer based on iris images. Through experimentation and comparison, the results show that the MobileNet model family achieves high performance while maintaining a light-weight architecture. The positive results of this study further strengthen the potential application of iris in the pre-diagnosis of lung cancer via mobile platforms.
Prediction of asphalt performance based on plastic waste using machine learning Agung Ananda Putra, I Gusti; Rokade, Darpan
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10598

Abstract

The incorporation of plastic waste into asphalt mixtures offers a promising solution to address the growing environmental concerns while enhancing the performance of road materials. Traditional methods, such as the Marshall test, are costly and time-consuming, thus highlighting the need for more efficient prediction techniques. Machine learning (ML) models, including random forest (RF), extreme gradient boosting (XGBoost), and artificial neural networks (ANN), have shown significant potential in predicting asphalt performance, optimizing material compositions, and reducing the dependence on labor-intensive laboratory tests. Key influencing factors such as bitumen content, plastic size, and temperature have been identified as crucial for improving asphalt properties. This systematic review emphasizes the potential of ML in streamlining the development of plastic-modified asphalt, offering a sustainable and cost-effective approach to road construction. Furthermore, it supports the advancement of green infrastructure and lays the foundation for future innovations in sustainable pavement engineering, contributing both to academic research and practical applications in the construction industry.
Internet of things-based fuzzy controller for automatic irrigation and NPK nutrient monitoring of grapes Sarosa, Moechammad; Wirayoga, Septriandi; Kusumawardani, Mila; Firmanda Al Riza, Dimas; Mulyani Azis, Yunia
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9715

Abstract

Grape cultivation has gained increasing attention due to its short growing period and the high market value of its sweet, refreshing fruits. However, achieving optimal growth requires precise environmental and nutrient management, which can be challenging under conventional farming practices. This research aims to develop an automatic watering system that integrates soil moisture and nutrient monitoring to optimize grape cultivation. The system utilizes Nitrogen Phosphorus Potassium (NPK) sensors, soil moisture sensors, and a camera for growth observation, all connected through the internet of things (IoT) for remote monitoring via Android devices. A fuzzy logic controller is implemented to regulate watering duration based on environmental conditions such as temperature and humidity. Experimental results show that the system effectively adjusts watering duration to approximately six seconds when the temperature is between 25–32 °C and humidity is around 60%. The DS18B20 temperature sensor achieved an average error rate of only 0.12%, while the humidity sensor demonstrated 0.2% error, indicating high accuracy levels of 99.8%. Despite minor limitations related to internet stability and sensor calibration, the system demonstrates strong potential for commercial-scale smart farming applications, promoting resource-efficient and data-driven grape cultivation.
Parameter tuning of PIDG controller on maximum photovoltaic power point for battery charging system Irwanto, Muhammad; Timoteus Gultom, Togar; Satria, Habib; Ismail, Baharuddin; Erniati Panjaitan, Christin; Syukri, Mahdi
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.11044

Abstract

Maximum photovoltaic power point (MPVPP) based on DC-DC buck converter is supplied by photovoltaic module. A controller method is needed to control the signal that it drives the switching component of DC-DC buck converter. The previous researcher conducts proportional integral derivative (PID) controller applying the DC-DC buck converter, but only its parameters (proportional, KP, integral, KI, and derivative, KD) are studied. This paper presents MPVPP based on PID with gain (PIDG) controller on the DC-DC buck converter by tuning the parameters of KP. KI and KD and adding a gain, G connected to PIDG controller for charging 12 V, 7 Ah battery. The DC-DC buck converter is designed for the output voltage of 14.7 V and output power of 150 W and modelled using Simulink MATLAB. The simulation results show that the parameters of KP=0.0032, KI=1, and KD=4×10-7 are suitable to control the switching component. The gain, G gives significant effect on the settling time and the time to reach their steady state value of output voltage of 14.7 V. The battery SOC can increase 1.36% per second, if the initial SOC is 25%, thus it needs arround 55 seconds to reach the fully charging condition.
Development of a machine learning-based framework for predicting failures in heat supply networks Darkenbayev, Dauren; Balakayeva, Gulnar; Zhapbasbayev, Uzak; Zhanuzakov, Mukhit
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10327

Abstract

The increasing complexity and scale of heat supply systems leads to a higher risk of failures, which may cause significant economic and environmental consequences. This study develops a predictive mathematical framework for the early detection of emergency conditions in heat supply networks (HSNs) using machine learning (ML). The proposed approach is based on the LightGBM gradient boosting (GB) algorithm, chosen for its high accuracy and efficiency in handling large datasets. Real operational data (temperature, pressure, flow, and vibration) were considered. Data preprocessing, feature engineering (including SHAP analysis), and hyperparameter tuning with grid search and 5-fold cross-validation improved prediction quality. The model achieved accuracy of 85%, F1-score of 0.82, and receiver operating characteristic (ROC)-area under the curve (AUC) of 0.96, outperforming logistic regression (LR) and decision trees. The framework may be integrated into monitoring systems for predictive maintenance, reducing downtime and optimizing costs.
Smart tourism application: towards software development for artificial intelligence in tourism management Natho, Parinya; Sarathum, Adisak; Sookjam, Amnaj; Putthidech, Anek; Boonmee, Salinun
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10820

Abstract

Artificial intelligence (AI) can manage tourism by optimizing, personalizing the experience, and enhancing user interactions. This research presents the Ayutthaya tourism platform independent model (ATPiM), an intelligent tourism application that integrates a domain-specific language (DSL) designed for chatbot development with machine learning algorithms that generate personalized recommendations based on user preferences, historical data, and real-time contextual influences. This pre-experimental design measures performance on parameters such as response time, recommendation accuracy, and system latency. The outcomes indicate that the mean time taken to respond to a user's query was 2.3 seconds, with 88.5% recommendation accuracy, and no latency. The AI-based recommendation system achieved 89.7% accuracy at destinations, 87.2% at accommodations, 90.3% at itineraries, and 85.6% at activities, with corresponding recalls of 85.4%, 83.5%, 88.1%, and 80.2% respectively. Although these results are promising, a 6.2% error rate for the advanced search, along with data security are some of the remaining issues. The findings reveal that the development of new user-centric and sustainable solutions for tourism, which leverage state-of-the-art natural language processing approaches, can enhance data security and provide additional new technologies, such as augmented reality (AR) and blockchain, for use in tourism.

Page 288 of 291 | Total Record : 2901

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