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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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Lifetime estimation of DC XLPE cable insulation using BPNN-IPM improved with various schemes and optimization methods Fikri, Miftahul; Abdul-Malek, Zulkurnain; Mohd Esa, Mona Riza; Supriyanto, Eko; Mulyana Kartadinata, Iwa Garniwa; Abduh, Syamsir; Christiono, Christiono
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp86-98

Abstract

The world’s need for green energy is something that cannot be postponed any longer, where the transmission-distribution process requires power distribution in DC voltage. However, currently, the majority use AC voltage, so limited experience and lack of data regarding electrical cable aging under high voltage (HVDC) and their reliability are problems that must be resolved. Crosslinked polyethylene (XLPE) constitutes many insulation cables used today, so estimating the lifetime of DC XLPE cable insulation is urgent research, even though various model-optimization improvements are needed to obtain accurate results. This research begins with pre-processing for the input and output data. These results were then analyzed using two improved model schemes to accommodate the addition of variable space charge and thickness: backpropagation neural network (BPNN) and hybrid BPNN with inverse power model (BPNN-IPM). The learning process uses gradient descent (GD), genetic algorithm (GA), and Levenberg-Marquardt (LM) optimization methods. Finally, the proposed method was verified using experimental data from previous research. The results show that the hybrid BPNN-IPM with LM optimization method is the most accurate: training root mean square error (RMSE) achieved 0 days, and testing RMSE achieved 0.83 days. These results show that the method BPNN-IPM-LM used is most accurate in estimating the lifetime of DC XLPE insulation.
Leveraging machine learning for sustainable integration of renewable energy generation Sreenivasan, Pushpa; Ganesan, Keerthiga; Fawad, Iffath; Sureshkumar, Sathya; Dhandapani, Kirubakaran
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1347-1355

Abstract

Long-term economic benefits and sustainability are provided by the integration of renewable energy sources (RESs) into electrical networks. However, because of their intermittent nature and reliance on environmental factors, RESs pose issues in production and consumption balance. Because renewable energy sources like wind and solar are unpredictable, forecasting their output is essential for planning purposes and maintaining grid stability. This thesis focuses on developing effective instruments and algorithms to improve renewable energy generation estimates and handle abnormalities in consumption. These tools and algorithms include maximum power point tracking and machine learning models like random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The methods' effectiveness is confirmed by accuracies higher than 80%, which provides speedier and more user-friendly solutions in comparison to the traditional ways. In the end, our effort seeks to offer practical instruments for anticipatory modelling and mitigating intermittentness in renewable energy sources, enabling their assimilation into current power structures to adequately supply energy requirements in a sustainable manner.
Experimental study of a medical data analysis model based on comparative performance of classification algorithms Ismukhamedova, Aigerim; Uvaliyeva, Indira; Rakhmetullina, Zhenisgul
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp672-684

Abstract

This article centers around the development and analysis of machine learning (ML) and deep learning models aimed at enhancing diabetes diagnosis. In the swiftly evolving landscape of data technologies, it becomes crucial to explore the applications of these methods for accurate predictions and improved medical decision-making. Our research encompasses diverse datasets, leveraging state-of-the-art algorithms and technologies for model training and testing. The primary emphasis lies in evaluating the accuracy, sensitivity, and specificity of models within the realm of diabetes diagnosis. The study results reveal significant advancements in disease prediction, underscoring the potential of ML and deep learning in medical applications. This work introduces fresh perspectives on the utilization of computational methods in healthcare and serves as a foundation for prospective research in this domain.
Deep-SFER: deep convolutional neural network and MFCC an effective speech and face emotion recognition Gummula, Ravi; Arumugam, Vinothkumar; Aranganathan, Abilasha
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1448-1459

Abstract

There has been a lot of progress in recent years in the fields of expert systems, artificial intelligence (AI) and human machine interface (HMI). The use of voice commands to engage with machinery or instruct it to do a certain task is becoming more common. Numerous consumer electronics have SIRI, Alexa, Cortana, and Google Assistant built in. In the field of human-device interaction, emotion recognition from speech is a complex research subject. We can't imagine modern life without machines, so naturally there's a need to create a more robust framework for human-machine communication. A number of academics are now working on speech emotion recognition (SER) in an effort to improve the interaction between humans and machines. We aimed to identify four fundamental emotions: angry, unhappy, neutral and joyful from speech in our experiment. As you can hear below, we trained and tested our model using audio data of brief Manipuri speeches taken from films. This task makes use of convolutional neural networks (CNNs) to extract functions from speech in order to recognize different moods using the Mel-frequency cepstral coefficient (MFCC).
A high efficiency boost converter topology with least component count Swargiary, Rikhit; Deva Sarma, Kaushik Chandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1404-1411

Abstract

This paper presents a design and analysis of novel DC-DC boost converter with a least component count. The proposed converter produces high DC gain voltage in comparison to some recently presented high voltage DC-DC converter. Here one switch, one inductor, two capacitors and one diode are used to achieved a high voltage gain without compromising efficiency of converter. The converter’s performance is evaluated using theatrical, simulation and experimental methods, with results indicating a four times of input voltage and a fast-transient response at various duty cycles is achieved. Due to its low component count the proposed converter is compact and hence it offers an effective solution for various power applications.
Cell nuclei image segmentation using U-Net and DeepLabV3+ with transfer learning and regularization Koishiyeva, Dina; Sydybayeva, Madina; Belginova, Saule; Yeskendirova, Damelya; Azamatova, Zhanerke; Kalpebayev, Azamat; Beketova, Gulzhanat
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1986-2000

Abstract

Semantic nuclei segmentation is a challenging area of computer vision. Accurate nuclei segmentation can help medics in diagnosing many diseases. Automatic nuclei segmentation can help medics in diagnosing many diseases such as cancer by providing automatic tissue analysis. Deep learning algorithms allow automatic feature extraction from medical images, however, hematoxylin and eosin (H&E) stained images are challenging due to variability in staining and textures. Using pre-trained models in deep learning speeds up development and improves their performance. This paper compares Deeplabv3+ and U-Net deep learning methods with the pre-trained models ResNet-50 and EfficientNetB4 embedded in their architecture. In addition, different regularization and dropout parameters are applied to prevent overtraining. The experiment was conducted on the PanNuke dataset consisting of nearly 8,000 histological images and annotated nuclei. As a result, the ResNet50-based DeepLabV3+ model with L2 regularization of 0.02 and dropout of 0.7 showed efficiency with dice coefficient (DCS) of 0.8356, intersection over union (IOU) of 0.7280, and loss of 0.3212 on the test set.
Electroencephalography biometric authentication using eye blink artifacts Madile, Thamang Teddy; Hlomani, Hlomani B.; Zlotnikova, Irina
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp872-881

Abstract

This study presents a novel approach to electroencephalography (EEG) biometric authentication using eye blink artifacts. Unlike traditional methods that rely on imagination and mental tasks, which are susceptible to emotional and physical variations, this approach leverages the consistent effects of eye blinks on brainwaves for authentication. Brainwaves were recorded using the NeuroSky Mindwave Mobile 2 headset, and eye blinks were extracted via NeuroSky’s blink detection algorithm. An authentication algorithm was developed based on blink strength, time, and frequency. The proposed method demonstrated high performance with an accuracy (ACC) of 97%, a false acceptance rate (FAR) of 5%, and a false rejection rate (FRR) of 1%. This study also explored the impact of emotions and physical exercise on the authentication process, confirming the method's robustness under varying conditions. These findings suggest that eye blink artifacts offer a reliable and practical biometric trait for EEG-based authentication systems, providing a secure alternative to traditional biometric methods. The substantial contribution of this research lies in demonstrating the superior stability and usability of eye blink-based EEG authentication under diverse conditions, compared to existing EEG authentication methods that often require mental tasks or multi-channel recordings.
Alzheimer’s prediction via CNN-SVM on chatbot platform with MRI Kadafi, Muhammad Syaekar; Yaqubi, Ahmad Khalil; Purbandini, Purbandini; Astuti, Suryani Dyah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp64-73

Abstract

Artificial intelligence (AI), consisting of models and algorithms capable of concluding data to produce future predictions, has revolutionary potential in various aspects of human life. One application is an Alzheimer’s disease (AD) prediction chat robot (chatbot). Only now has a method provided very accurate findings and recommendations regarding the early detection of AD using magnetic resonance imaging (MRI). Therefore, this research aims to measure AD prediction performance in four stage classes, namely very mild demented, mild demented, moderate demented, and non-demented, using brain MRI images trained in the convolutional neural network (CNN)- support vector machine (SVM) model. The research involved nine combination schemes of dataset proportions and preprocessing in the CNNSVM model. Evaluation shows that scheme 1 produces the highest accuracy, precision, recall, and F1-score, namely 98%, 99%, 98%, and 98%. The chatbot, trained using CNN, achieved 99.34% accuracy in question responses, and was then combined with AD prediction models for improved accuracy. The test results show that the chatbot functionality runs well for each transition, with a functionality score reaching 99.64 points out of 100.00. This success shows excellent potential for early detection of AD. This research brings new hope in preventing AD through AI, with potential positive impacts on human health and quality of life.
Fusion algorithms on identifying vacant parking spots using vision-based approach Adi, Ginanjar Suwasono; Nugroho, Hertog; Rahmatullah, Griffani Megiyanto; Fadhlan, Muhammad Yusuf; Mutamaddin, Dinan
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1640-1654

Abstract

In densely populated cities, parking space scarcity results in issues like traffic congestion and difficulty finding parking spots. Recent advancements in computer vision have introduced methods to address parking lot management challenges. The availability of public image datasets and rapid growth in deep learning technology has led to vision-based parking management studies, offering advantages over sensor-based systems in comprehensive area coverage, cost reduction, and additional functionalities. This study presents an innovative fusion algorithm that integrates object detection with occupancy state algorithms to accurately identify vacant parking spaces. The employment of the YOLOv7 framework for vehicle instance segmentation, combined with three occupancy algorithms Euclidean distance (ED), intersection over reference (IoR), and intersection over union (IoU) are compared to determine the occupancy state of observed areas. The proposed method is evaluated using the CNRPark-EXT dataset, and its performance is compared with state-of-the-art methods. As a result, the proposed approach demonstrates robustness under varying conditions. It outperforms existing methods in terms of system evaluation performance, achieving accuracies of 98.88%, 97.99%, and 90.04% for ED, IoR, and IoU, respectively. This fusion detection method enhances adaptability and addresses occlusions, emphasizing YOLOv7’s advantages and accurate shape approximation for slot annotation. This study contributes valuable insights for effective parking management systems and has potential usage in the real-world implementation of intelligent transportation systems.
Proposed model to predict preeclampsia using machine learning approach Aditya Rahman, Raden Topan; Lakulu, Muhammad Modi; Panessai, Ismail Yusuf; Yuandari, Esti; Ulfa, Ika Mardiatul; Ningsih, Fitriani; Tambunan, Lensi Natalia
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp694-702

Abstract

Pregnancy complications, which are the biggest cause of death in productive women, are more common in developing countries with low incomes. One of the contributors to death in pregnant women is preeclampsia which contributes 2-8% every day. Based on research results, more than 70% of the use of technology can be a solution for early prevention in detecting cases of pregnancy. The aim of this research is to build a model for early detection of preeclampsia using a machine learning approach. Sample using retrospective data with sample size 1.473. Based on the result, decision tree (DT) is the best model with accuracy 92.2% (area under curve (AUC): 0.91; Spec: 92.3; and Sens: 83.6), according to weigh correlation we can show 3 (three) highest features causes preeclampsia is history of hypertension, history of diabetes mellitus, and history of preeclampsia. The health of pregnant women is essential in the development of the fetus, so it needs optimal monitoring. Monitoring during pregnancy can now be done through technology-based examinations for assist health workers in making decisions during pregnancy.

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