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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 783 Documents
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Improving Bi-LSTM for High Accuracy Protein Sequence Family Classifier Roslidar, Roslidar; Brilianty, Novia; Alhamdi, Muhammad Jurej; Nurbadriani, Cut Nanda; Harnelly, Essy; Zulkarnain, Zulkarnain
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v12i1.4732

Abstract

The primary nutrient that is crucial for identifying biochemical processes and biological norms in living cells is protein. Proteins are usually centered around one or a few functions which are defined by their family type. Hence, identification and classification are needed to separate the proteins according to their structure and families. In this work, we built a model to classify families of protein sequences. We used the protein sequences dataset consists of various macromolecules of biological significance. The classifier is built up using deep learning of Bi-LSTM. We began the research by collecting the dataset from the Protein Data Bank of the Research Collaboratory for Structural Bioinformatics, pre-processing the data using tokenizing, and modeling the classifier based on deep learning network of Bi-LSTM. As we get the best accuracy rate of the trained model, we figure out the model performance using the evaluation metrics of learning curve, accuracy rate, and loss. The results show that Deep Bi-LSTM provides excellent performance with fit learning curve, 99% accuracy rate, and 0.042 loss.
The Effectiveness of Using the Pearson’s Correlation Coefficient for Compression Quality Assessment: Case of ECG Signals Compression with Discrete Cosine Transform El Hanine, Mustapha; Abdelmounim, Elhassane
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i1.5071

Abstract

This work presents a comparative study which aims to validate the importance of using the Pearson’s Correlation Coefficient (PCC), for the first time in this field of research, as an effective parameter for quantitative measurement of Electrocardiogram (ECG) signal compression quality. The comparison with the Percent of Root mean squared Difference coefficient (PRD) was carried out using the Discrete Cosine Transform (DCT). The ECG signals of the three derivations DI, DII and DIII, used for the test, belong to five categories of patients with various pathologies, each category of which includes four patients. The obtained results, based on the morphology comparison of P waves, T waves and QRS complexes before compression and after reconstruction, showed that the range of values between 99.90% and 100% for the PCC, ensures a very good signal reconstruction quality with a Compression Ratio (CR) that could reach 16.
A Privacy-Enhanced Scheme Within The Public Key Infrastructure For The Internet Of Things, Employing Elliptic Curve Diffie-Hellman (ECDH) Sebbah, Abderrezzak; Benamar, Kadri
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i1.5392

Abstract

The Public Key Infrastructure (PKI) serves as the foundation for online security, particularly within the realm of the Internet of Things (IoT). It operates based on certified public keys that remain permanent but can be revoked when necessary, such as in the case of a change in ownership, compromise of the private key, or malicious activities. Although this method ensures secure key utilization with traceability, it also introduces a potential privacy risk due to the traceability and utilization of identity-based certificates. This approach is considered an innovative strategy for ensuring user confidentiality, integrity, authentication, and privacy in the context of the Internet of Things. The proposed solution integrates elliptic curves (ECDH) and traditional PKI to safeguard user privacy. It introduces two types of elliptic curve keys: long-term identity-based certified keys and dynamically generated temporary anonymous aliases. These aliases are seamlessly recorded by the certification authority, which maintains distinct directories for long-term and temporary keys. This dual-key approach enhances security while addressing the specific requirements of the Internet of Things.
To Enhance the Operational Planning of an Independent Microgrid Using a Novel Combination of Demand Response Programs Swami, Rekha; Gupta, Sunil Kumar
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i1.4826

Abstract

Providing electricity in rural or isolated areas involves high capital costs due to the cost of constructing transmission and distribution facilities. An independent Microgrid consisting of distributed generations (including both renewable and non-renewable energy sources) near the load could be an effective alternative. However, the unpredictability of renewable energy sources like wind and solar creates a problem in Microgrid operation, as there are instances when generation may not be enough to satisfy peak demand. Energy storage technology is generally employed to address this uncertainty. The Demand Response Program (DRP) is another technique that makes the Microgrid operation reliable and safe by lowering peak demand and switching it to low-load periods. This article addresses the short-term Unit Commitment Economic Dispatch (UCED) problem for an Independent Microgrid to reduce the overall operating costs using various DRPs. This paper presents a novel combination of DRP to enhance Microgrid’s operation and financial effectiveness and benefit its users. DRP modeling is done based on price elasticity and consumer benefit models. Mixed-integer nonlinear programming (MINLP) is used to formulate and solve the UCED problem in the GAMS software. 11-Bus Microgrid is considered for demonstration. According to the optimization results, implementation of TOU-RTP-CPP�DLC DRPs reduces the operating cost by 13.68%, 13.31%, 17.16%, and 8.41%, respectively, with reduced load shedding. Consumers get benefits only in DLC-DRP. The proposed TOU+DLC-DRP combination reduces the operating costs by 13.48% with increased consumer benefits compared to DLC-DRP alone. Therefore, the proposed method is profitable for both the Microgrid operator and its users.
Design of Service Oriented Architecture for an IoT Healthcare Management System G. Hosary, Amira; Emran, Ahmed; El-Saghir, Basel
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i1.5191

Abstract

Healthcare services maturities are increasing dramatically over the last decade towards better patient anomaly detection, early diagnosis, and more accuracy in manipulation. The applications of IoT in healthcare are becoming more popular day after day with a good focus on the autonomy of detection and decision-making of patient’s vital data during admission phases, which could enable an envisioned environment for the right decisions on time. This paper focuses on developing a framework of architecture, protocols, and algorithms for IoT Healthcare system aimed at increasing the efficiency of systems operation and enhancing the reachability of different types of devices in the same patient or across several patients. The proposed architecture ensures that each individual device is autonomous and can work independently with the surrounding environment. The study includes as well as proof of concept pilot with a-capability to measure the patient’s vital information on a non-invasive basis, such as the pulse sensor unit, room temperature, and the display out device. The concept is validated, proving that devices can communicate together optimally, reliably, intelligently and autonomously in the same patient or across patient categories according to the status of patients without human intrusion.
Classification of Human Emotions Based on Javanese Speech Using Convolutional Neural Network and Multilayer Perceptron Ernawati, Muji; Riana, Dwiza
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i1.5343

Abstract

Emotions in speech are considered a basic principle of human interaction and play an important role in decision-making, learning, and everyday communication. Research on speech emotion recognition is still being carried out by many researchers to develop speech emotion recognition models with better performance. In this research, we combine the application of data augmentation techniques (Add Noise, Time Stretch, and Pitch Shift) to increase the data size of the Javanese Speech Emotion Database (Java-SED). Mel Frequency Cepstral Coefficients (MFCC) is used for feature extraction and then builds a Convolutional Neural Network (CNN) model and applies Multilayer Perceptron (MLP) to classify human emotions from sound. In this research, we produced eight experimental models with a combination of different augmentation techniques. The CNN model parameters include 40 input neurons, four hidden layers with varying neuron counts, Relu activation functions, L2 regularization, dropout rates, Adam optimization, and ModelCheckpoint callbacks to save the best model based on validation loss. From the results of the evaluation that has been carried out, the CNN algorithm produces the highest performance with an accuracy of 96.43%, recall of 96.43%, precision of 96.57%, F1-score of 96.48%, and kappa of 95.71% by applying the Add Noise technique, Time Stretch, and Pitch Shift.
Plant-Disease Relation Model through BERT-BiLSTM-CRF Approach Riyanto, Slamet; Sitanggang, Imas Sukaesih; Djatna, Taufik; Atikah, Tika Dewi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i1.5154

Abstract

Plant Disease Relations (PDR) is one of the Information Extraction (IE) subtasks that reveals the relationship between plant entities and diseases that appear together in a sentence. Previous studies have proposed methods for detecting the extraction of relationships between plant diseases (PDR). Previous research has proposed a Short Dependency Path-Convolutional Neural Network (SDP-CNN) method to predict relationships. However, the proposed method has limitations when faced with long and complex sentences. To overcome these limitations, this study proposes the BERT-BiLSTM-CRF method to improve the model performance in detecting PDR. First, the data is processed into the BERT Encoder layer after the tokenization process. After the BERT Encoder calculates the hidden information, the next step is to enter the linear layer to obtain word embedding. Calculation results in the bilinear layer are forwarded to the softmax layer to predict the relationship of each pair. Computation results in the softmax layer are sent to the BiLSTM layer. Finally, the CRF layer is entered to improve the prediction process. An 80:20 ratio for training and testing data was used to build the model using the same parameter values over ten attempts. GridSearch hyperparameter tuning is also involved in improving model performance. Experimental results show that the architecture proposed in this research can increase the F1 score by 0.790, which proved to be higher than SDP-CNN with a micro F1 score of 0.764. The problem of predicting PDR was overcome by the BERT-BILSTM-CRF method. The issue of forecasting PDR was resolved using the BERT-BILSTM-CRF approach.
Ransomware Detection Using Stacked Autoencoder for Feature Selection Wa Nkongolo, Mike Nkongolo; Tokmak, Mahmut
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i1.5109

Abstract

In response to the escalating malware threats, we propose an advanced ransomware detection and classification method. Our approach combines a stacked autoencoder for precise feature selection with a Long Short-Term Memory classifier which significantly enhances ransomware stratification accuracy. The process involves thorough preprocessing of the UGRansome dataset, training an unsupervised stacked autoencoder for optimal feature selection, and fine-tuning via supervised learning to elevate the Long Short-Term Memory model's classification capabilities. We meticulously analysed the autoencoder's learned weights and activations to pinpoint essential features for distinguishing 17 ransomware families from other malware and created a streamlined feature set for precise classification. Our results demonstrate the exceptional performance of the stacked autoencoder-based Long Short-Term Memory model across the 17 ransomware families. This model exhibits high precision, recall, and F1 score values. Furthermore, balanced average scores affirm its ability to generalize effectively across various malware types. To optimise the proposed model, we conducted extensive experiments, including up to 400 epochs, and varying learning rates and achieved an exceptional 98.5% accuracy in ransomware classification. These results surpass traditional machine learning classifiers. Moreover, the proposed model surpasses the Extreme Gradient Boosting (XGBoost) algorithm, primarily due to its effective stacked autoencoder feature selection mechanism and demonstrates outstanding performance in identifying signature attacks with a 98.5% accuracy rate. This result outperforms the XGBoost model, which achieved a 95.5% accuracy rate in the same task. In addition, a prediction of the ransomware financial impact using the proposed model reveals that while Locky, SamSam, and WannaCry still incur substantial cumulative costs, their attacks may not be as financially damaging as those of NoobCrypt, DMALocker, and EDA2.
Enhancing indoor radio tomographic imaging based on minimum RF nodes Abdullah, M. S. M.; Rahiman, M. H. F.; Khalid, N. S.; Nasir, A. S. A.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i1.4674

Abstract

Uses the attenuation on the links between transceivers to produce an image using Radio Tomographic Imaging (RTI), a network of transceivers, or a Wireless Sensor Network (WSN). Several RTI setups have been constructed as monitoring areas. However, it is observed that most setups have limitations in the number of RF nodes due to a limited number of measurements. However, it is well known that the main difficulty in radio tomographic imaging attributes to the uncertainties in the RSS measurements of transceivers due to multipath effects, especially, when the environment of interest is much cluttered, and requirements on the larger number of nodes for the performance improvement. It is highly remarkable that the motivation of using fewer nodes in this work is to reduce the deployment cost of radio tomographic imaging, slower data collection rates, longer imaging reconstruction times, and bigger sensitivity matricest, this lead author to proposed to design and development of an RTI system with a minimum of 8 RF nodes. The strong and weak received signal strength (RSS) exhibited in the images will be used to assess the effectiveness and accuracy of human sensing localization in a region. The images were reconstructed based on selected image reconstruction algorithms, and they are Linear Back- Projection (LBP), Filtered Back Projection (FBP), Gaussian, Newton’s One-step’s Error Reconstruction (NOSER) and Tikhonov Regularization (TR). The reconstructed images will be analysed using the Mean Structural Similarity (MSSIM) index. A comparison between the algorithms mentioned RTI system based on the MSSIM index. NOSER and TR algorithms scored the highest for the MSSIM index overall experiments, and it is the best technique to produce images that appear similar to the original images.
Driver Drowsiness Detection using Hybrid Algorithm Lakshmi, U. Poorna; Srinivas, P.V.S.; Shyam, S; Muchintala, Mallikarjun Reddy; Palugulla, Viswanath Reddy; Mandra, Hemanth Yadav
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i1.4738

Abstract

In this work we focus on the discernment of sleepiness in drivers’ drowsiness proposing a hybrid algorithm which aims to confirm whether the driver's level of attention has decreased owing to a nap or any other medical issue, such as brain problems. Therefore, the proposed hybrid algorithm uses both Haarcascade classifier and Convolutional Neural Network (CNN) algorithm to detect drivers’ drowsiness. The driver's eyes will be monitored and an alert sound will be generated by Raspberry Pi module, but the face must be moving in real time, and the aspect ratio must be between 16:9 and 1.85:1. People often feel sleepy since activities like driving call for a proper mental state, and bad work-life balance has additional negative repercussions. When we give input through normal camera it analyses drivers state of eyes and mouth, actually it checks aspect ratio of eye. We proved in comparative trials that our hybrid algorithm beats current driving fatigue detection algorithms in speed as well as accuracy.

Page 74 of 79 | Total Record : 783

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