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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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Context dependent bidirectional deep learning and Bayesian gaussian auto-encoder for prediction of kidney disease M, Jayashree; N, Anitha
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp387-398

Abstract

Chronic kidney disease (CKD) has emerged as a significant global health issue, leading to millions of premature deaths annually. Early prediction of CKD is crucial for timely diagnosis and preventive measures. While various deep learning (DL) methods have been introduced for CKD prediction, achieving robust quantification results remains challenging. To address this, we propose the context-dependent bi-directional DL and Bayesian gaussian autoencoder (CDBDP-BGA) method for CKD prediction. This approach utilizes clinical parameters and symptoms from a structured dataset. By incorporating context dependence into the bi-directional long short-term memory (Bi-LSTM) model, CDBDP-BGA efficiently redistributes the representation of information, enhancing its modeling capabilities. Feature selection is optimized using a BGA-based algorithm, which employs the Bayesian gaussian function. The SoftMax activation function classifies CKD into five distinct stages based on estimated-glomerular filtration-rate (eGFR), considering both symptoms (texture and numerical features) and clinical parameters (age, sex, and creatinine). Simulation results using two datasets demonstrate that CDBDP-BGA outperforms conventional methods, achieving 97.4% accuracy without eGFR and 98.7% with eGFR.
Non-contact breathing rate monitoring using infrared thermography and machine learning Salsabila, Anadya Ghina; Setiawan, Rachmad; Hikmah, Nada Fitrieyatul; Syulthoni, Zain Budi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp669-680

Abstract

Monitoring vital physiological parameters such as breathing rate (BR) is crucial for assessing patient health. However, current contact-based measurement methods often cause discomfort, particularly in infants or burn patients. This study aims to develop a non-contact system for monitoring BR using infrared thermography (IRT). This approach permits to detects and tracks the nose from thermal video, extracts temperature variations into a breathing signal, and processes this signal to estimate BR. The estimated BR is then classified into three health categories (bradypnea/normal/tachypnea) using k-nearest neighbors (k-NN). To evaluate system accuracy and robustness, experiments were conducted under three conditions: (i) stationary breathing, (ii) breathing with head movements, and (iii) specific breathing patterns. Results demonstrated high consistency with contact-based photoplethysmography (PPG) measurements, achieving complement of the absolute normalized difference (CAND) index values of 94.57%, 93.71%, and 96.06% across the three conditions and mean absolute BR errors of 1.045 bpm, 1.259 bpm, and 0.607 bpm. The k-NN classifier demonstrated high performance with training, validation, and testing accuracies of 100%, 100%, and 99.2%, respectively. Sensitivity, specificity, precision, and F-measure results confirm system reliability for non-contact BR monitoring in clinical and practical settings.
Empowering microgrids: harnessing electric vehicle potential through vehicle-to-grid integration Mishra, Debani Prasad; Senapati, Rudranarayan; Samal, Sarita; Rai, Niti Rani; Behera, Niharika; Salkuti, Surender Reddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1422-1430

Abstract

Electric vehicles (EVs) can potentially be integrated into microgrids via vehicle-to-grid (V2G) technology, which enhances the energy system's stability and durability. This paper provides an in-depth examination and evaluation of V2G integration in microgrid systems. It analyses the present state of research as well as possible uses, challenges, and directions for V2G technology in the future. This paper addresses the technological, economic, and regulatory aspects of implementing V2G and provides case studies and pilot projects to shed light on potential benefits and barriers associated with its adoption. The research highlights how V2G contributes to more efficient integration of renewable energy sources, grid stabilization, and cost savings for EV owners. It also addresses the latest developments in technology and proposed laws aimed at encouraging growing applications of V2G.
Identification and segmentation of tumor using deep learning and image segmentation algorithms Chippalakatti, Shilpa; Chodavarapu, Renu Madhavi; Pallavi, Andhe
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1782-1792

Abstract

Brain tumor is a typical mass of tissue that develops when cells proliferate and divide excessively. Brain tumor perception requires a great deal of work and experience from the medical professional in order to identify the tumor's precise location. If a brain tumor is not discovered in a timely manner, it affects a person's ability to function normally and raises the death rate. This study focuses on tumor segmentation and tumor detection using magnetic resonance imaging (MRI) images. This work helps the medical professional to precisely identify the tumor location and segmentation process provides cost effective data storage. The YOLOv8s model is utilized for tumor identification, while the image segmentation technique is employed for tumor segmentation. The images come from an open-source dataset used for research, and Roboflow 100 transforms them into .yaml files that are congenial with YOLOv8s. To train the model the dataset is split into training, validation and testing. Proposed model consist of dataset which comprises 639 images, of which 453 are utilized for training, 122 for validation, and 64 for testing, resulting in a ratio of 71:19:10. The dataset is subjected to preprocessing and augmentation. The suggested model performance is assessed depending on the parameters like precision, recall, mAP50 and mAP50-95.
Using ResNet architecture with MRI for classification of brain images Dhanalakshmi, Subramanian; Arulselvi, Subramanian
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp148-158

Abstract

A strong classification model that can correctly detect abnormalities and neurological disorders in brain images is the main goal. The focus of this research is on improving the accuracy of MRI brain image categorization using residual networks (ResNet) methods. Improving the model's capacity to extract complex characteristics from MRI images and achieving more accurate classification results is the aim of using ResNet architectures. By conducting extensive experiments and validating our results, our project aims to attain top-notch performance in brain image classification tasks. The goal is to help improve medical diagnosis and treatment planning. A secondary goal of the research is to determine if deep learning approaches have any use in radiology, with the hope that this will lead to better medical image analysis pipelines. The main objective is to make it easier to identify neurological problems early on, which will enhance patient outcomes and allow for more calculated treatment decisions. Results proved that the proposed ResNet system achieves 98.8% overall accuracy with 98.6% sensitivity and 99% specificity.
Word embedding and imbalanced learning impact on Indonesian Quran ontology population Utomo, Fandy Setyo; Purwati, Yuli; Azmi, Mohd Sanusi; Shafira, Lulu; Trinarsih, Nikmah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp603-613

Abstract

This research addresses limitations in Quranic instance classification, exceptionally high dimensionality, lack of semantic relationships in the term frequency-inverse document frequency (TF-IDF) technique, and imbalanced data distribution, which reduce prediction accuracy for minority classes. This study investigates the impact of word embedding and imbalance learning techniques on instance classification frameworks using Indonesian Quran translation and Tafsir datasets to handle previous research limitations. Four classification frameworks were built and evaluated using accuracy and hamming loss metrics. The results show that the synthetic minority oversampling technique (SMOTE) technique, TF-IDF model, and logistic regression classifier provide the best accuracy results of 62.74% and a hamming loss score of 0.3726 on the Quraish Shihab Tafsir dataset. This is better than the performance of previous classifiers backpropagation neural network (BPNN) and support vector machine (SVM) used in the previous framework, with accuracies of 59.91% and 62.26%, respectively. Logistic regression can also provide the best classification results with an accuracy of 67.92% and a hamming loss of 0.3208 using the previous framework. These results are better than the performance of the previous classifiers BPNN and SVM used in the previous framework, with accuracies of 62.26% and 66.98%, respectively. TF-IDF feature extraction outperforms word2vec in instance classification results due to its superior support under limited dataset conditions.
An efficient DVHOP localization algorithm based on simulated annealing for wireless sensor network Arroub, Omar; Darif, Anouar; Saadane, Rachid; Rahmani, My Driss; Aarab, Zineb
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp720-736

Abstract

In the last decade, the research community has devoted significant attention to wireless sensor networks (WSNs) because they contribute positively to some critical issues encountered in nature and even in industry. On the other hand, localization is one of the most important parts of WSN. Hence, the conception of an efficient method of localization has become a hot research topic. Lastly, it has been invented, a set of optimal positioning methods that make locate a node with low cost and give precise results. In our contribution, we investigate the source of imprecision in the distance vectorhop (DVHOP) localization algorithm. However, we found the last step of DVHOP caused an imprecision in the calculation. Consequently, our work was to replace this step, aiming to reach satisfactory precision. For that purpose, we created three improved versions of this algorithm by adopting two meta-heuristic (simulated annealing, particle swarm optimization) and Fmincon solver dedicated to optimization in the field of WSN node localization. The experimental results obtained in this work prove the efficiency of simulated annealing (SA)-DVHOP in terms of accuracy. Furthermore, the enhanced algorithm outperforms its opponents by varying the percentage of anchors and the number of nodes.
A new modified B4 inverter using SRF controller with SVPWM technique for grid-connected PV system Anitha, Golkonda; Kondreddi, Krishnaveni; Yesuratnam, Guduri
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1411-1421

Abstract

The integration of renewable power sources into the grid presents a complex challenge, as the grid operates at AC voltage, while photovoltaic (PV) arrays generate DC power. A 3-phase inverter synchronizes with the grid’s voltage and frequency for efficient energy integration. In conventional technique, a 3-ph 6-switch (B6) inverter is used for sharing the power to the grid. In this paper reduced switch count 3-ph 4-switch (B4) inverter topology is introduced with reduced power losses. This topology has 4 insulated gate bipolar transistor (IGBT) switches and two capacitors replacing the other 2 switches positioned in one leg of the inverter, which connects to a grid connected PV system. A grid synchronization method called synchronous reference frame (SRF) based proportional integral (PI) is used to track the phase angle of the grid and subsequently inject current into the grid. A B4 inverter is operated by a novel space vector pulse width modulation (SVPWM) control technique which operates in 4 possible switching states. A comparative analysis is carried out with the PV array grid integration connected through B4 and B6 inverter topologies with SRF control. The modeling and design are carried out in a MATLAB/Simulink environment with graphs plotted according to the conditions. The comparative analysis validates the importance of SRF controllers for the grid integration of any renewable source.
Enhanced performance and efficiency of robotic autonomous procedures through path planning algorithm Latha, Raman; Sriram, Saravanan; Bharathi, Balu; Fernandes, John Bennilo; Raju, Ayalapogu Ratna; Boopathy, Kannan; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp214-224

Abstract

To optimize surgical routes for better patient outcomes and more efficient operations, we want to test how well these algorithms work. Finding the best algorithms for different types of surgeries and seeing how they affect things like time spent in surgery, precision, and patient safety is the goal of this exhaustive study. By shedding light on the effectiveness of route planning algorithms, this work aspires to aid in the development of autonomous robotic surgery. To find out how well various algorithms work in actual surgical settings; this study compares them. The results of this work have the potential to enhance robotic surgery efficiency and improve surgical outcomes by informing the creation of more efficient route planning algorithms. The overarching goal of this study is to provide evidence that autonomous robotic surgery can benefit from using sophisticated route planning algorithms, which might lead to more accurate, faster, and safer procedures. The surgical patient dataset exhibits a wide variety of medical variables, including ages 38–62, weight 65–85 kg, height 160–180 cm, blood pressure 110–140/90 mm Hg, heart rate 70–85 bpm, hemoglobin 12–14 g/DL, and body mass index (BMI) 25.4–29.4.
Secure data transmission towards mitigating potentially unknown threats in wireless sensor network Puttaswamy, Chaya; Kanakapura Shivaprasad, Nandini Prasad
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp523-530

Abstract

Wireless sensor network (WSN) is known for its wider range of applications towards sensing physical attributes over human-inaccessible regions. With consistently rising concerns of security threats, WSN is the pivotal topic of network security. A literature review showcases the shortcomings of conventional data transmission schemes in WSN. This manuscript introduces an innovative approach to mitigating the potentially vulnerable and unknown threats. The implemented model promotes a group-based communication followed by a newly introduced threat onlooker node capable of identifying the malicious request of a newly designed adversary module. The scheme also hybridizes symmetric and asymmetric encryption at the end to cipher the aggregated data. The validation of the model is carried out considering standard scores of simulation parameters related to system variables. Further, the scheme has been compared with frequently adopted real-world encryption algorithms. Scripted in MATLAB, the model is assessed to confirm 35% of increased residual energy, 57% of better threat detection, 27% of enhanced throughput, and 68% of reduced processing time in contrast to existing secure data transmission schemes.

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