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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 2,826 Documents
 274   275   276   277   278 
Integration of deep learning algorithms for real-time vehicle accident detection from surveillance videos Mota, Riya; Wankhade, Renuka; Rahul Shinde, Gitanjali; Rajendra Patil, Rutuja; Bobhate, Grishma; Kaur, Gagandeep
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9587

Abstract

Major road accidents have increased due to the rapid rise of vehicles on the roads due to affordability and accessibility. While minor accidents can be resolved without the need for escorting to hospitals, significant accidents that involve the deployment of airbags necessitate the immediate attention of authorities. Thus, subsequent action of first aid and proper communication to concerned medical personnel can avoid most fatalities from accidents. The system involves the automatic detection of traffic accidents from videos extracted by closed-circuit television (CCTV) surveillance. In case of an accident, the system will detect and information about the accident will be instantly relayed to the nearest medical center. We have implemented different machine learning models such as Resnet-18, VGG-16, LeNet, and Inception V1 to ensure the accuracy of accident detection. From comparing all these models, the convolutional neural network (CNN) model shows the highest accuracy of 98%. The quick response will be an important step toward a safer and more secure transportation landscape.
Enhancing data integrity in internet of things-based healthcare applications: a visualization approach for duplicate detection Noor Basirah Md Isa, Siti; A. Emran, Nurul; Harum, Norharyati; Machap, Logenthiran; Nordin, Azlin
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10063

Abstract

This study addresses the critical issue of data duplication in healthcare-related internet of things (IoT) datasets, which can compromise the reliability of analyses and patient outcomes. A Python-based visualization framework using Pandas and Matplotlib was developed to detect and represent duplicate records. The methodology was applied to six cancer-related datasets sourced from Kaggle, ranging from 300 to 55,000 records, encompassing numerical, textual, and categorical data types. The visualization technique provided clear insights into duplication patterns, identifying specific counts such as 7 duplicates in the wearable device dataset, 19 in the thyroid recurrence dataset, and 534 in the synthetic healthcare electronic health record (EHR) dataset. Compared to traditional detection methods, the visualization tool facilitated faster and more intuitive initial data assessment, demonstrating its effectiveness for rapid quality checks in healthcare datasets. However, scalability limitations were observed in larger datasets, where visual clarity declined. These findings highlight the value of visualization as a preliminary data quality assessment tool and suggest future integration with advanced detection algorithms to enhance robustness and scalability.
Feature separation of music across diverse dataset: a comparative perspective Shunmugalingam Parvathi, Sakthidevi; Chandrasekar, Divya
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9962

Abstract

In music, feature separation is the process of separating distinguishable auditory characteristics, such as pitch, timbre, rhythm, and harmonic content, from a complicated, mixed signal. Virtual reality (VR), gaming, music transcription, karaoke systems, audio restoration, music information retrieval (MIR), music education, and audio forensics, are just a few of the areas where the topic has attracted a lot of attention. Feature extraction is crucial in music separation as it identifies and isolates sound elements, improving accuracy, and reducing noise. It simplifies raw audio into meaningful data for efficient processing and effective model learning. Without it, clean separation of audio components is very difficult. In this research, extracting features from mixed audio sources enables clean and accurate isolation of musical elements, enhancing quality, supporting precise evaluations, and boosting neural network performance across varied datasets including DSD100, MUSDB, and MUSDB18-HQ, which collectively afford rich musical content for making evaluations and benchmarks. Evaluation metrics, such as F1-score, precision, and recall, are utilized to demonstrate the performance data of the extracted features. The MUSDB18-HQ dataset yielded an overall increase of 17.86% in the F1-score metrics with significant increases in drums (+25.05%) and vocals (+20.04%), showing that the dataset was highly effective for feature separation.
Low-cost internet of things system for water metering in smart campus de Souza Medeiros, Átila; Delgado Gomes, Ruan; F. B. F. da Costa, Anderson
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9442

Abstract

Internet of things (IoT) technologies are transforming the monitoring of water distribution networks (WDN) and urban water infrastructure (UWI), as well as smart campus infrastructures, which has the same problems as an urban water network, such as leaks, inaccurate readings, and unnecessary expenses. Smart water meters (SWM) represent an economical IoT solution for remotely monitoring system parameters such as flow rate, pressure, and water quality to reduce losses. This paper introduces an IoT-based smart water metering solution employing message queuing telemetry transport (MQTT), long range (LoRa), a middleware for IoT, and low-cost sensors, implemented at the Federal Institute of Para´ıba, Brazil, as an initial effort toward establishing a smart campus. The evaluation of the IoT device showed a measurement performance index (MPI) of 97.83%, with a flow sensor error margin (FS400A) below 2% for calibrated ranges. The quality of the wireless link yielded an average RSSI of-89 dBm and a packet error rate of 0.35%. The IoT system demonstrated potential as a feasible smart campus application
Automated real-time cervical cancer diagnosis using NVIDIA Jetson Nano Mulmule, Pallavi; Shilaskar, Swati; Bhatlawande, Shripad; Mulmule, Vedant; H Kamble, Vaishali; Madake, Jyoti
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10169

Abstract

Cervical cancer is a global health concern, making early detection critical for ensuring effective treatment outcomes. Screening technique, the Papanicolaou test (Pap test), has been adopted globally for timely detection. Nevertheless, the process of screening is subjective. The current study aims to advance the development of an automated real time framework for cervical cell analysis for early-stage diagnosis using supervised classification on NVIDIA Jetson Nano platform. Our approach, leveraging adaptive fuzzy k-means (AFKM) clustering and k-means clustering, extracts distinctive features from cervical cell images for accurate classification. Utilizing multilayer perceptron (MLP) and support vector machine (SVM) classifiers, we achieved a classification accuracy of 97%, highlighting the potential of our system for real-time applications in cervical cancer investigation. Validation by two expert pathologists further supports the system’s practical utility.
The use of fiber bragg grating coated with polyimide for CO2 gas sensor Irawan, Dedi; Saktioto, Saktioto; Azhar, Azhar; Sutoyo, Sutoyo; Sahal, Muhammad; Hanto, Dwi; Widiyatmoko, Bambang
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9283

Abstract

This study presents the application of fiber bragg grating (FBG) sensors coated with polyimide for detecting carbon dioxide (CO₂) gas, employing both theoretical and experimental approaches. The basic FBG components were coated with polyimide layers of varying thicknesses. Subsequently, the fabricated FBG sensors were characterized using an optical interrogator system with four channels. Furthermore, the sensor was tested for CO₂ detection at a working temperature of 47 °C. Experimental data showed that the FBG sensor coated with polyimide layers of 10 nm, 15 nm, and 20 nm demonstrated sensitivities of 1.9 ppm, 1.84 ppm, and 1.8 ppm, respectively. In contrast, the uncoated FBG sensor exhibited a higher sensitivity of 3 ppm. Increasing the coating thickness beyond 20 nm leads to a decrease in sensor sensitivity. The findings suggest that an optimal polyimide coating thickness for CO₂ detection using FBG sensors is around 20 nm. Achieving high sensitivity in CO₂ gas sensors is crucial for their effective use across a broad range of applications.
A hybrid extreme machine learning model for predicting heart disease M. Ahmed, Abdelmoty; Bataineh, Bilal; Shakah, Ghazi; O. Al Enany, Marwa; M. Aboghazalah, Maie; M. Khattab, Mahmoud
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10028

Abstract

Heart disease (HD), the leading cause of death for adults over 65, can affect anyone at any time. Additionally, modern lifestyles, poor diets, and other factors have led to an increased risk of HD among teenagers. One significant challenge is managing and analysing vast amounts of data, often surpassing terabytes, which is crucial for researching, diagnosing, and predicting cardiovascular diseases quickly. To enhance primary health care, especially in early and rapid diagnosis of heart attacks and to assist less experienced doctors in understanding clinical HD data, we propose a hybrid method called the "hybrid extreme machine learning model (HEMLM)". This technique combines the strengths of multi-layer perceptron (MLP), random layers, and logistic regression (LR). The model offers various feature patterns and multiple classification techniques. Compared to support vector machine (SVM), LR, and Naive Bayes (NB), the HEMLM algorithm demonstrates superior performance and efficiency. Testing results show identification accuracies of 94.91%, 94.77%, 92.42%, and 87.14% for data splitting ratios of 85:15, 80:20, 70:30, and 60:40, respectively.
Wireless sensor network using nRF24L01+ for precision agriculture Abidin, Zainul; Falah, Raisul; Setyawan, Raden Arief; Wardana, Fitri Candra
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.v14i2.8481

Abstract

Precision agriculture is a strategy for varying inputs and cultivation methods to suit varying soil conditions and agricultural crops. In order to optimize precision agriculture, wireless sensor network (WSN) is suitable to be integrated. In this research, network devices that communicate using nRF24L01+ based WSN was proposed. As a prototype, four sensor nodes were employed to measure the parameters of air temperature and humidity, soil moisture, and power supply voltage. While, a sink node serves to store measurement data locally. The data are sent to the sink node with a mesh network topology and saved in a comma-separated values (CSV) file and local database. Experimental results show that each sensor node can measure all parameters and successfully send data to the sink node every 1 minute without losing the data. The mesh topology can route data transfer automatically. Round trip time (RTT) of each sensor node depends on the distance from each node. Average power consumption of all sensor nodes in send mode is between 84 mW and 90 mW. Meanwhile, in sleep mode, the sensor nodes 1 and 2 consumed around 21-22 mW and the sensor nodes 3 and 4 consumed around 30 mW which are lower than the send mode.
Optimizing plant health monitoring: improved accuracy and the computational efficiency with stacked machine learning models and feature filtering Sangeetha, Tupili; Ezhumalai, Periyathambi
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.v14i2.8809

Abstract

Plant cultivation can be effectively achieved with the help of hydroponic farming that allows growing soilless and organic plant veggies. However, maintaining optimal plant health in such controlled environments requires continuous monitoring and assessment techniques. This paper provides a comprehensive description of how to determine and categorize the health of hydroponic plants based on a wide range of parameters, such as temperature, pH, electrical conductivity (EC), leaf count, plant height, and vegetative indices. We present a novel approach termed “Hybrid XGBoosting” that combines the multi-classification algorithm extreme gradient boosting (XGBoost) with gradient-based one-side sampling (GOSS) methods to improve accuracy and processing efficiency. This approach first adopts a feature correlation method known as “Pearson’s correlation” for reducing repeated data that are directly proportional or inversely proportional to each other. Finally, we perform a thorough comparative study using well-known algorithms including traditional XGBoost, AdaBoost, and gradient boosting. We demonstrate the better prediction capabilities of Hybrid XGBoosting with 97.93% accuracy through rigorous testing and evaluation, showing its potential for improving hydroponic plant health assessment approaches. Additionally, our research employs comprehensive algorithm assessment measures, such as root mean squared scaled error (RMSSEE), to guarantee the stability and reliability of the results.
A guide for selection of wireless communication technology for effective and robust early forest fire detection system Salaria, Anshika; Singh, Amandeep
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.v14i2.8613

Abstract

The world is facing a major ecosystem crisis due to global warming and pollution. Considering the rate at which the temperatures are rising, one must think about the causes and origins of this extreme environmental shift. Today, countries like India, have cities ranked as some of the most polluted cities in the world. Apart from vehicular traffic and industrial wastes, one of the prime components of the entire problem is forest fires. Burning forests emit tons of harmful gases into the atmosphere. This disaster also leaves drastic aftereffects on the economy and society. Therefore, an efficient system should be designed to monitor the forest fires at the earliest. Highlighting the role of wireless sensor networks in the irregular terrains of forests and considering the technical challenges, it is important to identify, first, the best technology for communication among sensors, in such complex terrains. Second, the identification of an optimization algorithm for the deployment of sensors to achieve maximum coverage This work presents an analysis of state-of-the-art wireless sensor networks to identify a reliable communication technique. Further an optimization algorithm is proposed for maximum coverage with a minimum number of sensors. The algorithm outperforms the other state-of-the-art algorithms in simulation results.

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