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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 6: December 2025" : 75 Documents clear
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.
Isolation enhancement of four port multiple-input multiple-output antenna for sub-6 GHz 5G communication Manikonda, Ramesh; Sudhakar, Annapantula; Tamminaina, Govindarao
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.v14i6.9948

Abstract

For 5G communication, this research suggests a small, broad band, 4-port multiple-input multiple-output (MIMO) antenna with an impedance bandwidth of 2.0 GHz (or 3.0-5.0 GHz). The n77, n78, and n79 bands are covered. The single antenna is realized by inserting the stubs and creating the ‘HI’ slot on the rectangle patch with the defect in the ground plane, using FR-4 substrate. Next, four MIMO antennas are built utilizing the reference antenna. Due to mutual interaction, implementing MIMO systems presents a substantial challenge: achieving good isolation between antenna parts in the confined space. To increase isolation with decoupling procedures, the four antennas are placed orthogonally to one another. Because the antennas are positioned orthogonally, the MIMO antenna has an isolation of 28.0 dB. The diversity gains (DG) and envelop correlation coefficient (ECC) are used to analyze the recommended antenna's diversity performance characteristics, and the results show that the values are 9.99 dB and 0.0003, respectively. The simulated S-parameters have been compared with orthogonal and adjacent positions of quad port MIMO antenna. Anritsu MS2037C VNA is used to measure the parameters, and HFSS software is used to simulate it.
Cyber security threats and web vulnerability analysis of higher educational institutions in Bangladesh Hasan Khan Janny, Shadiqul; Asadujjaman Noor, Md.; Enan Al Harun Sahan, Mohammad; Nafez Sadnan, Sheikh; Towfiqur Rahman, Muhammad; Saleh Md Bakibillah, Abu
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.v14i6.9349

Abstract

This paper presents a comprehensive analysis of cyber security threats and web vulnerabilities in the context of higher educational institutions in Bangladesh, including twenty public and private universities. Educational institutions are highly vulnerable due to their negligence in maintaining a functional network, mainly owing to budgetary constraints. As a result, they have become a hacker playground for many ambitious adversaries to boast their technical skills, regardless of the harm they may inflict. Through the use of vulnerability assessment and penetration testing (VAPT), we conducted a methodical analysis of the institutions’ web infrastructures, identify and categorize the prevalent security threats and vulnerabilities that may compromise the integrity, confidentiality, and availability of information systems. Our findings reveal significant disparities in the security strength of both public and private universities, with the latter demonstrating a higher degree of vulnerability due to varying factors, such as budget constraints, policy enforcement, and awareness levels. This study underscores the urgent need for robust cyber security frameworks tailored to the higher educational sector’s unique requirements, advocating for proactive measures to mitigate potential cyber threats. The implications of this research extend beyond the academic domain, offering insights into national cyber security strategies and the safeguarding of critical information infrastructures.
Soil erosion analysis based on machine learning method Bolsynbek, Mukhammed; Abdikerimova, Gulzira; Serikbayeva, Sandugash; Batyrkhanov, Ardak; Shrymbay, Dana; Taszhurekova, Zhazira; Zhidekulova, Gulkiz; Shraimanova, Gulmira
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.v14i6.10452

Abstract

Soil erosion poses a serious environmental and agricultural threat that undermines land productivity, sustainability, and ecosystem stability. This study develops a robust machine learning framework for predicting and analyzing soil erosion across diverse landscapes by integrating advanced remote sensing data, climate indicators, and soil characteristics. Spectral indices such as the normalized difference vegetation index (NDVI), moisture stress index (MSI), and surface albedo were employed to assess vegetation condition, moisture levels, and surface reflectance. The proposed model, based on the extreme gradient boosting (XGBoost) algorithm, classifies erosion stages with up to 99% accuracy, ranging from healthy land to severely degraded areas. The methodology includes comprehensive feature engineering, dataset preprocessing, and model evaluation. Furthermore, a comparative analysis with traditional models (USLE and RUSLE) highlights the superior predictive performance of the proposed approach. The findings offer valuable insights for sensor-based monitoring systems and cloud-based decision-support tools, supporting sustainable land use management, erosion risk mitigation, and effective soil conservation strategies.
Enhanced speech recognition in natural language processing Chang, Siu-Hong; Ng, Kok-Why; Haw, Su-Cheng; Yoong, Yih-Jian
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.v14i6.9539

Abstract

Speech recognition is crucial for helping individuals with physical disabilities access digital content. However, current systems have significant flaws that hinder user experience and complicate daily tasks. Environmental disturbances can cause misinterpretation, and existing automatic speech recognition (ASR) systems struggle with comprehending acoustic and linguistic nuances and handling diverse speaking styles and accents. To address these issues, a new model integrates bidirectional encoder representations from transformers (BERT) and transformer features with natural language processing (NLP) capabilities. This model aims to consolidate semantic, linguistic, and acoustic information extracted from the Kaldi speech recognition toolkit and improve accuracy by rescoring the list of N-best hypotheses. The innovative approach leverages advancements in NLP to enhance speech recognition's accuracy and robustness across various scenarios. Evaluations on the LibriSpeech dataset show that integrating BERT, transformer encoder, and generative pretrained transformer 2 for rescoring N-best hypotheses significantly improves transcription accuracy. The proposed model achieves a word error rate (WER) of 17.98%, outperforming other models. This development paves the way for advancements in speech recognition technology, offering better user experiences in real-world applications.
Load frequency control of multi-source power system using PID+DD controller based on chess algorithm Areeyat, Chatmongkol; Audomsi, Sitthisak; Obma, Jagraphon; Yang, Xiaoqing; Sa-ngiamvibool, Worawat
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.v14i6.10425

Abstract

This article presents load frequency control for a nonlinear multi-source power system divided into three areas, consisting of thermal reheat power plants, hydropower, and wind generation, while considering generation rate constraints (GRC). A proportional–integral–derivative (PID) plus second-order derivative (PID+DD) controller optimized using the chess algorithm (CA) is proposed. The effectiveness of CA is validated against hippopotamus optimization (HO), grey wolf optimizer (GWO), and ant lion optimizer (ALO) under two scenarios: a 10% step load perturbation (SLP) and a random load pattern (RLP). Simulation results indicate that the proposed CA significantly improves dynamic performance. In scenario 1 (10% SLP), CA achieves a reduction of approximately 30.5% in integral weight time absolute error (ITSE) compared to GWO and 43.7% compared to HO, while also reducing frequency undershoot in Area 2 by 15.2% compared to HO. In scenario 2 RLP, CA maintains robustness, limiting tie-line power deviations to ±8 MW, whereas HO exhibits deviations exceeding ±12 MW. Overall, the CA-tuned PID+DD controller demonstrates superior damping, reduced overshoot and undershoot, and enhanced stability across multi-area interconnected renewable systems, making it a promising approach for future real-time load frequency control (LFC) applications with higher renewable penetration.
Optimized electric vehicle charging allocation with overload management and vehicle to grid support Hamim, S. J.; Rahman, Imran; Yeamin, Md.; Saleh, Abdullah; Aziz, Tareq
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.v14i6.10728

Abstract

The rapid proliferation of electric vehicles (EVs) in residential distribution networks poses significant challenges, particularly in managing peak demand and maintaining grid stability during the peak demand periods. This study employs a day-ahead EV charging framework in compliance with valley-filling technique to align charging during off-peak periods for a centralized residential charging station that balances grid stability with customer satisfaction. To mitigate network overloading, vehicle to grid support is integrated through optimization based on genetic algorithm (GA), enabling optimal scheduling of both charging and discharging activities under operational constraints. Simulation outcomes substantiate the efficacy of the proposed charging scheme in preventing overloads and demonstrate a notable enhancement in the load factor from 70.68% to 82.24%, reflecting enhanced utilization of energy resources. The approach offers technical and economic benefits for both utilities and EV users, highlighting its potential for scalable and efficient grid management.

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