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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 19 Documents
 1   2 
Search results for , issue "Vol 12, No 1: March 2024" : 19 Documents clear
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.
Fuzzy Logic Based DTC Control of Synchronous Reluctance Motor Madbouly, Sayed O.
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.v12i1.5372

Abstract

This paper presents the utilization of a fuzzy logic controller (FLC) within the speed control loop of the direct torque control (DTC) algorithm. The aim is to enhance the dynamic performance of a 3-phase synchronous reluctance motor (SynRM) in variable speed applications. The proposed FLC employs the speed error and change of speed error to generate the torque command signal needed for the torque hysteresis comparator within the DTC scheme. The system being analyzed comprises of a synchronous reluctance motor, voltage source converter and the proposed fuzzy logic-based DTC. In order to evaluate the performance of the proposed controller, a comprehensive system model is developed and simulated using MATLAB Simulink. The dynamic response of the entire system is investigated when subjected to various command speeds and loading conditions. It is found that the proposed controller achieves fast and precise dynamic response under all operating conditions. Furthermore, a comparative analysis is conducted between utilizing the FLC and the traditional proportional integral differential (PID) controller in the speed control loop of the DTC, the results demonstrate a significant improvement in the dynamic response when employing FLC compared to the traditional PID controller.
Smart Security Solution for Market Shop Using IoT and Deep Learning Bin Abdul Hai, Talha; Rahman, Wahidur; Hosen, Md Solaiman; Islam, Md. Tarequl; Sadi, A H M Saifullah; Faruque, Gazi Golam; Azad, Mir Mohammad
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.v12i1.4780

Abstract

Nowadays, security system in the market shop is an immense concern everywhere. The modern world is leaning towards intelligent, automated security systems instead of the traditional human-based security or CCTV surveillance system because of their limitations. A typical CCTV surveillance system is not intelligent enough to detect intruders or fire. The proposed security system in this paper is an IoT, deep learning, and GSM based innovative security solution specially designed for shops and offices. The objectives of this system are to prevent burglary and fire. For this, the proposed model focuses on fire and intruder detection through both IoT and deep learning approaches. Several IoT sensors have been utilized with a deep learning model to detect fires in shops or offices at an initial stage. The model also utilizes a current sensor for identifying electrical short-circuit to prevent unexpected damages. This system further utilizes GSM technology to send the corresponding notifications to the authorized user and play alarm sounds at the shop as well as the owner's house while detecting suspicious occurrences. The proposed solution has used two pre-trained Convolutional Neural Network (CNN) architecture, namely ResNet50 and Inception V3. This research found an accuracy of 99.49% with ResNet50 architecture in fire detection.
Ant-Lion Optimization Algorithm Based Optimal Performance of Micro Grids Nagaraju, Samala; Chandramouli, Bethi
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.v12i1.5119

Abstract

In the operational state of an electrical power system, ensuring efficient utilization and high-quality power usage is essential. Various quality enhancement measures, such as linear and adaptive filters, are implemented to improve the current's quality. Additionally, power flow controllers are employed to mitigate losses and enhance fault tolerance. However, the escalating demand for power supply, driven by rapid industrial and urban growth, often exceeds the capacity of existing generation systems. To address this challenge, supplementary subunits are integrated into the power system. This proposal's main objective is to introduce a weight-defined parameter monitoring system for power scheduling within a multi-parameter monitoring framework. The aim is to enhance the conventional preference-based scheduler by incorporating intelligent control techniques, including Unified Power Quality Conditioner (UPQC) with the ANT-LION Optimization (ALO) algorithm, which will be compared to a Fuzzy Logic controller. UPQC plays a pivotal role in addressing power quality issues within the system, combining a shunt active power filter with an Artificial Neural Network (ANN) controlled by the ALO algorithm. Our research demonstrates the effectiveness of this proposed system, particularly in microgrid applications, with validation conducted using MATLAB/Simulink. 
Partition-Based Technique to Enhance Missing Data Prediction Barati Jozan, Mohammad Mahdi; Tabesh, Hamed
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.v12i1.4440

Abstract

Managing missing data is a critical aspect of preprocessing in data mining endeavors, significantly influencing output accuracy during both model development and utilization phases. This study introduces a novel approach to predicting missing values by partitioning data into disjoint subsets based on partitioning measures. The rationale behind this approach is the elimination of unrelated data through partitioning, thereby improving the accuracy of missing value prediction within each subset. Through a combination of expert panel insights and statistical tests (including the Chi-square test and Cramer's V coefficient), the database partitioning measure was determined using operational data from the Mashhad Fire and Safe Services Organization. Models were constructed for each partition, and missing data were segmented accordingly, with the corresponding models employed for prediction. The results revealed that in 44% of cases, models built on partitioned data outperformed those constructed on the entire dataset. The evaluation of this method underscores its capability to predict missing values with heightened accuracy. Notably, this approach is independent of the method employed for missing value prediction, enabling seamless integration into existing methods as an additional step to bolster prediction accuracy.  
Intelligent Bankruptcy Prediction Models Involving Corporate Governance Indicators, Financial Ratios and SMOTE Idhmad, Azzeddine; Kaicer, Mohammed; Nejjar, Chaymae; Benjouad, Abdelghani
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.v12i1.5241

Abstract

This study enhances bankruptcy prediction models by investigating synergies between predictors, utilizing a diverse dataset of financial statements and corporate governance data. Rigorous feature selection identifies key financial ratios (FRs) and corporate governance indicators (CGIs) to enhance model interpretability. Multiple machine learning algorithms construct and assess the models, including Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, and Neural Networks. Integration of CGIs with FRs aims to identify effective combinations that improve model performance with an accuracy respectively 90%, 95%, 97%, and 98%. Researchers explore feature weighting techniques and ensemble methods, examining their impact on accuracy, sensitivity, and specificity. The study also explores how regulatory frameworks and governance practices affect bankruptcy prediction, analyzing data across periods to uncover changes in predictive power under varying conditions. The findings have implications for investors, institutions, and policymakers, offering more accurate risk assessments and emphasizing the interplay between financial performance and governance quality for corporate well-being.
Summary on RoF Technologies, Modulations, and Optical Filters: Review Obied, Manea Naif; Askar, Mishari 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.v12i1.5354

Abstract

In order to meet the growing need for bandwidth, this article offers a thorough examination of Radio over Fibre (RoF) technology and its integration with wireless communication networks. It starts out by going over the development of wireless networks and the difficulties they encounter, like spectral congestion and RF spectrum operational constraints. An effective way to handle data traffic is to include optical fibre into wireless networks. A detailed analysis is conducted of the technical features of RoF systems, including modulation approaches such as external and direct modulation. While external modulation provides better performance by getting around constraints, direct modulation uses the RF signal to directly modify the brightness of the light source. It is detailed how optical filters, including Fabry-Perot, Fiber-Bragg Grating, and Tunable filters, are used in a variety of applications. They provide an explanation of their functions and importance in optical communication. In addition, a thorough review of relevant literature is included in the study, along with a summary of the main conclusions, approaches, goals, drawbacks, and achievements of academic studies on optical communication and RoF systems. This analysis focuses on the field's problems and achievements. In summary, RoF technology integration of optical and wireless networks holds enormous potential to satisfy the changing needs of high-capacity, high-speed wireless communication. In order to effectively utilise the potential of RoF systems and progress contemporary wireless networks, additional study and development work is yet required.

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